US20140059083A1 - Context-based search for a data store related to a graph node - Google Patents
Context-based search for a data store related to a graph node Download PDFInfo
- Publication number
- US20140059083A1 US20140059083A1 US13/592,905 US201213592905A US2014059083A1 US 20140059083 A1 US20140059083 A1 US 20140059083A1 US 201213592905 A US201213592905 A US 201213592905A US 2014059083 A1 US2014059083 A1 US 2014059083A1
- Authority
- US
- United States
- Prior art keywords
- graph
- node
- synthetic context
- context event
- synthetic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
Definitions
- the present disclosure relates to the field of computers, and specifically to the use of databases in computers. Still more particularly, the present disclosure relates to a context-based search for data related to entities described in a graph database.
- a database is a collection of data. Examples of database types include relational databases, graph databases, network databases, and object-oriented databases. Each type of database presents data in a non-dynamic manner, in which the data is statically stored.
- a context-based system for searching for data stores related to a set of one or more nodes in a graph database contains a graph database comprising multiple graph nodes.
- a first pointer points from a particular graph node to a particular synthetic context event node in a synthetic context event database.
- a second pointer points from the particular synthetic context event node in the synthetic context event database to a particular data store in a data structure, such that the first pointer and the second pointer associate the particular data store with the particular entity represented in the graph database via the particular synthetic context event node.
- a processor-implemented method searches for data stores related to a set of one or more nodes in a graph database.
- a processor points from a particular graph node in a graph database to a particular synthetic context event node in a synthetic context event database.
- the graph database comprises multiple graph nodes, where each of the multiple graph nodes stores an attribute of a particular entity.
- Each of the multiple graph nodes is logically coupled to another graph node by an edge, which describes a relationship between entities represented by coupled graph nodes.
- the synthetic context event database is made up of multiple synthetic context event nodes, where each of the synthetic context event nodes contains a descriptor of the attribute of the particular entity as well as the relationship between the particular entity and another entity represented by another graph node.
- the processor then points from a particular synthetic context event node in the synthetic context event database to a particular data store in a data structure, such that pointing to the particular data store associates the particular data store with the particular entity represented in the graph database via the particular synthetic context event node.
- a computer program product searches for data stores related to a set of one or more nodes in a graph database.
- Stored on a computer readable storage medium are first program instructions and second program instructions.
- the first program instructions are to point from a particular data store in a data structure to a particular synthetic context event node in a synthetic context event database, where the synthetic context event database comprises multiple synthetic context event nodes.
- the particular synthetic context event node contains a descriptor of an attribute of a particular entity represented by a particular graph node in a graph database, and the particular synthetic context event node further contains a relationship described in an edge between said particular graph node and another graph node in the graph database.
- the second program instructions are to point from the particular synthetic context event node in the synthetic context event database to the particular graph node in the graph database, such that pointing to the particular synthetic context event node and the particular graph node associates the particular data store with the particular entity represented by the graph node via the particular synthetic context event node.
- FIG. 1 depicts an exemplary system and network in which the present disclosure may be implemented
- FIG. 2 illustrates a novel context-based system for searching for data stores related to an entity described by a set of one or more nodes in a graph database
- FIG. 3 is a high-level flow chart of one or more steps performed by a computer processor to locate data stores related to an entity represented by a set of one or more nodes in a graph database.
- aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
- the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
- a computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
- a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
- a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.
- a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
- Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
- Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages.
- the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
- the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
- LAN local area network
- WAN wide area network
- Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
- These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
- the computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
- FIG. 1 there is depicted a block diagram of an exemplary system and network that may be utilized by and in the implementation of the present invention. Note that some or all of the exemplary architecture, including both depicted hardware and software, shown for and within computer 102 may be utilized by software deploying server 150 and/or a data storage system 152 .
- Exemplary computer 102 includes a processor 104 that is coupled to a system bus 106 .
- Processor 104 may utilize one or more processors, each of which has one or more processor cores.
- a video adapter 108 which drives/supports a display 110 , is also coupled to system bus 106 .
- System bus 106 is coupled via a bus bridge 112 to an input/output (I/O) bus 114 .
- An I/O interface 116 is coupled to I/O bus 114 .
- I/O interface 116 affords communication with various I/O devices, including a keyboard 118 , a mouse 120 , a media tray 122 (which may include storage devices such as CD-ROM drives, multi-media interfaces, etc.), a printer 124 , and external USB port(s) 126 . While the format of the ports connected to I/O interface 116 may be any known to those skilled in the art of computer architecture, in one embodiment some or all of these ports are universal serial bus (USB) ports.
- USB universal serial bus
- Network interface 130 is a hardware network interface, such as a network interface card (NIC), etc.
- Network 128 may be an external network such as the Internet, or an internal network such as an Ethernet or a virtual private network (VPN).
- a hard drive interface 132 is also coupled to system bus 106 .
- Hard drive interface 132 interfaces with a hard drive 134 .
- hard drive 134 populates a system memory 136 , which is also coupled to system bus 106 .
- System memory is defined as a lowest level of volatile memory in computer 102 . This volatile memory includes additional higher levels of volatile memory (not shown), including, but not limited to, cache memory, registers and buffers. Data that populates system memory 136 includes computer 102 's operating system (OS) 138 and application programs 144 .
- OS operating system
- OS 138 includes a shell 140 , for providing transparent user access to resources such as application programs 144 .
- shell 140 is a program that provides an interpreter and an interface between the user and the operating system. More specifically, shell 140 executes commands that are entered into a command line user interface or from a file.
- shell 140 also called a command processor, is generally the highest level of the operating system software hierarchy and serves as a command interpreter. The shell provides a system prompt, interprets commands entered by keyboard, mouse, or other user input media, and sends the interpreted command(s) to the appropriate lower levels of the operating system (e.g., a kernel 142 ) for processing.
- a kernel 142 the appropriate lower levels of the operating system for processing.
- shell 140 is a text-based, line-oriented user interface, the present invention will equally well support other user interface modes, such as graphical, voice, gestural, etc.
- OS 138 also includes kernel 142 , which includes lower levels of functionality for OS 138 , including providing essential services required by other parts of OS 138 and application programs 144 , including memory management, process and task management, disk management, and mouse and keyboard management.
- kernel 142 includes lower levels of functionality for OS 138 , including providing essential services required by other parts of OS 138 and application programs 144 , including memory management, process and task management, disk management, and mouse and keyboard management.
- Application programs 144 include a renderer, shown in exemplary manner as a browser 146 .
- Browser 146 includes program modules and instructions enabling a world wide web (WWW) client (i.e., computer 102 ) to send and receive network messages to the Internet using hypertext transfer protocol (HTTP) messaging, thus enabling communication with software deploying server 150 and other computer systems.
- WWW world wide web
- HTTP hypertext transfer protocol
- Application programs 144 in computer 102 's system memory also include a context-based data store locating program (CBDSLP) 148 .
- CBDSLP 148 includes code for implementing the processes described below, including those described in FIGS. 2-3 .
- computer 102 is able to download CBDSLP 148 from software deploying server 150 , including in an on-demand basis, wherein the code in CBDSLP 148 is not downloaded until needed for execution.
- software deploying server 150 performs all of the functions associated with the present invention (including execution of CBDSLP 148 ), thus freeing computer 102 from having to use its own internal computing resources to execute CBDSLP 148 .
- the data storage system 152 stores an electronic data structure, which may be business/medical records, audio files, video files, website entries, text files, etc.
- computer 102 contains the graph database storage system and the synthetic context event database storage system described and claimed herein, while the data storage system is a same or separate system for storing data stores as described and claimed herein.
- computer 102 may include alternate memory storage devices such as magnetic cassettes, digital versatile disks (DVDs), Bernoulli cartridges, and the like. These and other variations are intended to be within the spirit and scope of the present invention.
- CBDSLP 148 is able to generate and/or utilize some or all of the databases depicted in the context-based system in FIG. 2 .
- the context-based system 200 comprises a graph database storage system for storing a graph database 202 , a synthetic context event database storage system for storing a synthetic context event database 204 , and access to a data storage system for storing a data structure 206 .
- the graph database storage system and the synthetic context event database storage system are part of computer 102 shown in FIG. 1
- the data storage system is the data storage system 152 depicted in FIG. 1 .
- the graph database 202 is a schema-less database in which data is organized as a set of nodes (objects) with properties (attributes or values). These nodes are linked to other nodes through edges, which describe the relationship between two nodes. As depicted in FIG. 2 , these nodes are shown as graph nodes 208 a - 208 n , where “n” is an integer. The graph nodes 208 a - 208 n are linked by edges 210 x - 210 z , which describe relationships between linked graph nodes.
- graph node 208 a represents “circulatory diseases” (or persons having a circulatory disease)
- graph node 208 b represented “myocardial infarction” (or persons who are having or have had a myocardial infarction).
- the edge 210 x thus describes the graph node 208 b as being a subset of graph node 208 a.
- graph node 208 b still represents persons who have had a myocardial infarction
- graph node 208 n represents all persons who are morbidly obese, live in a certain city/state/country/geographical region, drink green tea, etc.
- the edge 210 z would thus describe the persons represented by graph node 208 b as “being morbidly obese”, “a resident of the certain city/state/country/geographical region”, a “drinker of green tea”, etc.
- two or more graph nodes can be clustered into a graph node cluster 212 , which includes graph node 208 a and graph node 208 n .
- This graph node cluster 212 has been predetermined to include all related graph nodes (as indicated by linking edges and/or shared attributes within the graph nodes).
- the synthetic context event database 204 is made up of synthetic context event nodes 214 a - 214 n (where “n” is an integer). These synthetic context event nodes 214 a - 214 n may or may not be interlinked (i.e., logically associated with one another by having shared attributes, etc.). Each of these synthetic context event nodes 214 a - 214 n represents a synthetic event (i.e., they are fabricated by consolidating information from different sources which may or may not describe an actual event).
- each of the synthetic context event nodes 214 a - 214 n contains a descriptor of an attribute of a particular entity (i.e., information from one or more of the graph nodes 208 a - 208 n ) and a relationship between a particular entity and another entity represented by another graph node (i.e., the information found in an edge such as edge 210 x ).
- the data structure 206 is a database of multiple data stores 216 a - 216 n , which may be text documents, hierarchical files, tuples, object oriented database stores, spreadsheet cells, uniform resource locators (URLs), etc.
- the data structure 206 is a database of text documents (represented by one or more of the data stores 216 a - 216 n ), such as journal articles, webpage articles, electronically-stored business/medical/operational notes, etc.
- the data structure 206 is a database of text, audio, video, multimedia, etc. files (represented by one or more of the data stores 216 a - 216 n ) that are stored in a hierarchical manner, such as in a tree diagram, a lightweight directory access protocol (LDAP) folder, etc.
- LDAP lightweight directory access protocol
- the data structure 206 is a relational database, which is a collection of data items organized through a set of formally described tables.
- a table is made up of one or more rows, known as “tuples”.
- Each of the tuples (represented by one or more of the data stores 216 a - 216 n ) share common attributes, which in the table are described by column headings.
- Each tuple also includes a key, which may be a primary key or a foreign key.
- a primary key is an identifier (e.g., a letter, number, symbol, etc.) that is stored in a first data cell of a local tuple.
- a foreign key is typically identical to the primary key, except that it is stored in a first data cell of a remote tuple, thus allowing the local tuple to be logically linked to the foreign tuple.
- the data structure 206 is an object oriented database, which stores objects (represented by one or more of the data stores 216 a - 216 n ).
- objects represented by one or more of the data stores 216 a - 216 n .
- an object contains both attributes, which are data (i.e., integers, strings, real numbers, references to another object, etc.), as well as methods, which are similar to procedures/functions, and which define the behavior of the object.
- the object oriented database contains both executable code and data.
- the data structure 206 is a spreadsheet, which is made up of rows and columns of cells (represented by one or more of the data stores 216 a - 216 n ). Each cell (represented by one or more of the data stores 216 a - 216 n ) contains numeric or text data, or a formula to calculate a value based on the content of one or more of the other cells in the spreadsheet.
- the data structure 206 is a collection of universal resource locators (URLs) for identifying a webpage, in which each URL (or a collection or URLs) is represented by one or more of the data stores 216 a - 216 n.
- URLs universal resource locators
- data structure 206 is homogenous in one embodiment, while data structure 206 is heterogeneous in another embodiment.
- data structure 206 is a relational database, and all of the data stores 216 a - 216 n are tuples.
- data structure 206 is homogenous, since all of the data stores 216 a - 216 n are of the same type.
- data store 216 a is a text document
- data store 216 b is an MRI image
- data store 216 c is a tuple from a relational database, etc.
- data structure 206 is a heterogeneous data structure, since it contains data stores that are of different formats.
- the synthetic context event database 204 may include filtering logic (i.e., part of CBDSLP 148 shown in FIG. 1 ), which allows the user to specify what type of data store is to be located.
- filtering logic i.e., part of CBDSLP 148 shown in FIG. 1
- such a filter may request only image files (e.g., an MRI image), or it may request only text files (e.g., journal articles), or it may request only universal resource locators (URLs) to websites, or it may request only tuples from a relational database, or it may request any combination of data stores (i.e., a combination of data stores that are inclusive of some types of data stores and are exclusive of other types of data stores).
- a first pointer points to one of the synthetic context event nodes 214 a - 214 n
- a second pointer points from one of the synthetic context event nodes 214 a - 214 n to one of the data stores 216 a - 216 n
- graph node 208 b represents persons who have had a myocardial infarction
- information from edge 210 x and/or edge 210 z describes those persons' relationships to entities represented by graph node 208 a and graph node 208 n , respectively.
- synthetic context event node 214 a contains the information stored in graph node 208 b as well as the information stored in edge 210 x and/or edge 210 z .
- a first pointer 218 a points from graph node 208 b to synthetic context event node 214 a.
- a first pointer 218 b points from graph node cluster 212 , which includes graph node 208 a and graph node 208 n , as well as the information in edge 210 y , to a synthetic context event node 214 b .
- graph node cluster 212 which includes graph node 208 a and graph node 208 n , as well as the information in edge 210 y , to a synthetic context event node 214 b .
- the information found in graph node 208 a , graph node 208 n , and edge 210 y are also represented in synthetic context event node 214 b.
- a second pointer points from a synthetic context event node, which was pointed to by the first pointer, to a particular data store in the data structure, such that the first pointer and the second pointer associate the particular data store with the particular entity represented in the graph database via the particular synthetic context event node.
- graph node 208 b represents persons who have had a myocardial infarction; that information from edge 210 x and/or edge 210 z describes those persons' relation to entities represented by graph node 208 a and graph node 208 n , respectively; that synthetic context event node 214 a contains the information stored in graph node 208 b as well as the information stored in edge 210 x and/or edge 210 z ; and that first pointer 218 a points from graph node 208 b to synthetic context event node 214 a .
- Second pointer 220 a now points from synthetic content event node 214 a to data store 216 a , ultimately resulting in the linkage of graph node 208 b to data store 216 a via synthetic context event node 214 a.
- Second pointer 220 a points to (i.e., identifies and/or retrieves) data store 216 a according to a logical relationship between the synthetic context event node 214 a and the data store 216 a .
- synthetic context event node 214 a contains entries (i.e., information from graph node 208 b and/or edges 210 x and/or 210 z ) that match descriptive data such as a keyword, metadata, context-based mined data, etc. found in data store 216 a .
- this descriptive data describes an activity related to the entity described in graph node 208 b .
- graph node 208 b may describe the person's lifestyle (i.e., smoker, runner, etc.), medical history (i.e., has had a particular medical procedure, other disease, treatment in a particular facility or by a particular doctor, etc.), travel history, etc.
- first pointer 218 b points from graph node cluster 212 to synthetic context event node 214 b
- second pointer 220 b points to data store 216 c
- second pointer 220 e points to data store 216 n , thus associating graph node cluster 212 with data store 216 c and/or data store 216 n.
- the single synthetic context event node 214 a has a first second pointer 220 c as well as a second second pointer 220 d , which point to different data stores 216 b and 216 c . That is, multiple data stores, which may be of the same (i.e., are all magazine articles, web entries, etc.) or different (i.e., one is a text file, one is a video file, etc.) types of data stores. Thus, the term data store is used to describe any type of stored file (i.e., text, video, etc.).
- a graph database storage system (i.e., part of computer 102 shown in FIG. 1 ) contains a graph database 202 made up of multiple graph nodes 208 a - 208 n .
- Each of the multiple graph nodes 208 a - 208 n stores an attribute of a particular entity, and each of the multiple graph nodes 208 a - 208 n is logically coupled to another graph node by one or more of the edges 210 x - 210 z , where each edge describes a relationship between entities represented by coupled graph nodes.
- a first pointer (e.g., first pointer 218 a ) points from a particular graph node (e.g., graph node 208 b ) to a particular synthetic context event node (e.g., synthetic context event node 214 a ) in the synthetic context event database 204 .
- a synthetic context event database storage system (i.e., also part of computer 102 shown in FIG. 1 ) contains the synthetic context event database 204 , which contains multiple synthetic context event nodes 214 a - 214 n .
- Each of the multiple synthetic context event nodes 214 a - 214 n contains a descriptor of one or more attributes of the particular entity represented by graph node 208 b , as well as the relationship (e.g., found in edge 210 x ) between that particular entity and another entity represented by another graph node (e.g., graph node 208 a ).
- a second pointer (e.g., second pointer 220 a ) points from the particular synthetic context event node 214 a in the synthetic context event database 204 to a particular data store 216 a in a data structure 206 .
- the first pointer 218 a and the second pointer 220 a associate the particular data store 216 a with the particular entity represented in the graph database (i.e., by graph node 208 b ) via the particular synthetic context event node 214 a.
- data stores 216 a - 216 n within data structure 206 do not merely describe or provide additional detail about the information found in a graph node from the graph database 202 and/or the edges 210 x - 210 z that connect various graph nodes. Rather, these data stores 216 a - 216 n are data stores that are deemed to be related to a particular graph node by a particular synthetic context event node. That is, a particular data store from data stores 216 a - 216 n is deemed to be associated to a particular intermediate synthetic context event node by virtue of the contextual information (i.e., information supplied by one or more graph nodes and/or their edges) found in that particular intermediate synthetic context event node.
- the contextual information i.e., information supplied by one or more graph nodes and/or their edges
- data store 216 a may be a medical journal article that has been associated with synthetic context event node 214 a (e.g., by containing certain keywords, metadata, etc.).
- This medical journal article does not merely describe the information from graph node 208 b and/or the edges to that graph node 208 b , but rather provides medical details about a particular medical study. These medical details include those not suggested by the information from the graph node 208 b and/or the edges to that graph node 208 b.
- the particular data store (e.g., data store 216 a ) describes an activity (i.e., lifestyle, medical activities/history, hobbies, travel history, etc.) related to the particular entity depicted by graph node 208 b.
- an activity i.e., lifestyle, medical activities/history, hobbies, travel history, etc.
- the particular data store (e.g., data store 216 a ) describes a set of diagnostic and/or treatment options for medical patients described by graph node 208 b.
- the particular data store (e.g., data store 216 a ) describes a set of financial, legal, technical, etc. reports related to a business entity described by graph node 208 b.
- the second pointer 220 a uses a keyword that is in both the particular synthetic context event node 214 a and the particular data store 216 a to point to the particular data store 216 a .
- the second pointer 220 a uses metadata that is associated with both the particular synthetic context event node 214 a and the particular data store 216 a to point to the particular data store 216 a.
- multiple second pointers (e.g., second pointers 220 a , 220 c and 220 d ) point from the particular synthetic context event node 214 a to multiple data stores 216 a , 216 b and 216 c in the data structure 206 .
- a first second pointer 220 d from a first synthetic context event node 214 a and a second second pointer 220 b from a second synthetic context event node 214 b point to a same data store 216 c in the data structure 206 . That is, a same data store 216 c may be relevant to two synthetic context event nodes, and thus related to two different graph nodes and/or graph node clusters.
- the data structure 206 is a relational database, such that the particular data store (e.g., data store 216 a ) is a tuple within the relational database.
- the data structure 206 is a text data structure, such that the particular data store (e.g., data store 216 a ) describes a study (i.e., a medical journal article, doctor's notes, engineering notes, financial reports, etc.) about the particular entity represented in the graph database 202 .
- a study i.e., a medical journal article, doctor's notes, engineering notes, financial reports, etc.
- a processor points (e.g., using a first pointer such as first pointer 218 a shown in FIG. 2 ) from a particular graph node (e.g., graph node 208 b ) in a graph database (e.g., graph database 202 ) to a particular synthetic context event node (e.g., synthetic context event node 214 a ) in a synthetic content event database (e.g., synthetic context event database 204 (block 304 ).
- a particular graph node e.g., graph node 208 b
- a graph database e.g., graph database 202
- synthetic context event node e.g., synthetic context event node 214 a
- synthetic content event database e.g., synthetic context event database 204 (block 304 ).
- the graph database comprises multiple graph nodes, wherein each of the multiple graph nodes stores an attribute of a particular entity that is described by the particular graph node.
- Each of the multiple graph nodes is logically coupled to another graph node by an edge, which describes a relationship between entities represented by coupled graph nodes.
- the synthetic context event database comprises multiple synthetic context event nodes.
- Each of the multiple synthetic context event nodes contains a descriptor of the attribute of the particular entity as well as the relationship between the particular entity and another entity represented by another graph node in the graph database.
- the processor then points (e.g., using a second pointer such as second pointer 220 a shown in FIG. 2 ) from the particular synthetic context event node (e.g., synthetic context event node 214 a ) in the synthetic context event database to a particular data store (e.g., data store 216 a ) in a data structure (e.g., data structure 206 ).
- a second pointer such as second pointer 220 a shown in FIG. 2
- the particular synthetic context event node e.g., synthetic context event node 214 a
- a particular data store e.g., data store 216 a
- a data structure e.g., data structure 206
- the identified data store (e.g., data store 216 a ) is then retrieved (e.g., for display, printing, etc.) and sent to a computer system, requesting entity, etc.
- the process ends at terminator block 310 .
- a particular data store can search for a particular graph node.
- data store 216 a is a medical journal article about a particular disease (e.g., cancer).
- data store 216 a is linked by second pointer 220 a to synthetic context event node 214 a , which leads a user to graph node 208 b .
- a user who initially only knew about the medical journal article represented as data store 216 a also now knows not only about graph node 208 b , but also knows about linked graph nodes 208 a and 208 n .
- graph node 208 b represented a particular genetic marker, which may or may not have been mentioned in the medical journal article
- graph nodes 208 a and 208 n represent other genetic markers
- knowing about these related genetic markers allows the user to expand his data store search.
- the user is able to traverse from the newly-identified graph node cluster 212 and/or graph node 208 a or graph node 208 n to data store 216 c and/or data store 216 n .
- the present invention enables the user to identify data store 216 c and/or data store 216 n , through the use of synthetic context event node 214 b , which is pointed to by first pointer 218 b from the graph node cluster 212 and/or graph node 208 a and/or graph node 208 n .
- data store 216 c and/or data store 216 n may be another medical journal article, a set of medical examination results (e.g., X-rays, MRIs, lab workups, etc.), etc., which may be in any digital format (e.g., PDF, JPEG, MPEG, .doc, etc.).
- data store 216 n is accessible only via synthetic context event node 214 b
- data store 216 c is accessible via synthetic context event node 214 a or synthetic context event node 214 b.
- knowing about data store 216 a enables the user to also know about data store 216 b and data store 216 c , since synthetic context event node 214 a not only points to data store 216 a but also to data store 216 b and data store 216 c .
- the synthetic context event node 214 a which is defined by the graph database features described above, allows the user to make a direct connection between different data stores within the data structure 206 via the synthetic context event node 214 a , such that data store 216 b and/or data store 216 c can be located and/or retrieved based on the user's awareness of data store 216 a.
- data store 216 n is accessible only via synthetic context event node 214 b
- data store 216 c is accessible via synthetic context event node 214 a or synthetic context event node 214 b . If data store 216 a is used to locate additional related data stores within the data structure 206 , then data store 216 c can be located directly via the synthetic context event node 214 a .
- a pathway through synthetic context event node 214 a must be traversed to the graph database 202 , and then returning through the synthetic context event node 214 b in order to locate/retrieve the data store 216 n via the pointers described herein.
- each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
- the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
- VHDL VHSIC Hardware Description Language
- VHDL is an exemplary design-entry language for Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), and other similar electronic devices.
- FPGA Field Programmable Gate Arrays
- ASIC Application Specific Integrated Circuits
- any software-implemented method described herein may be emulated by a hardware-based VHDL program, which is then applied to a VHDL chip, such as a FPGA.
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
Description
- The present disclosure relates to the field of computers, and specifically to the use of databases in computers. Still more particularly, the present disclosure relates to a context-based search for data related to entities described in a graph database.
- A database is a collection of data. Examples of database types include relational databases, graph databases, network databases, and object-oriented databases. Each type of database presents data in a non-dynamic manner, in which the data is statically stored.
- In one embodiment of the present invention, a context-based system for searching for data stores related to a set of one or more nodes in a graph database is presented. A graph database storage system contains a graph database comprising multiple graph nodes. A first pointer points from a particular graph node to a particular synthetic context event node in a synthetic context event database. A second pointer points from the particular synthetic context event node in the synthetic context event database to a particular data store in a data structure, such that the first pointer and the second pointer associate the particular data store with the particular entity represented in the graph database via the particular synthetic context event node.
- In one embodiment, a processor-implemented method searches for data stores related to a set of one or more nodes in a graph database. A processor points from a particular graph node in a graph database to a particular synthetic context event node in a synthetic context event database. The graph database comprises multiple graph nodes, where each of the multiple graph nodes stores an attribute of a particular entity. Each of the multiple graph nodes is logically coupled to another graph node by an edge, which describes a relationship between entities represented by coupled graph nodes. The synthetic context event database is made up of multiple synthetic context event nodes, where each of the synthetic context event nodes contains a descriptor of the attribute of the particular entity as well as the relationship between the particular entity and another entity represented by another graph node. The processor then points from a particular synthetic context event node in the synthetic context event database to a particular data store in a data structure, such that pointing to the particular data store associates the particular data store with the particular entity represented in the graph database via the particular synthetic context event node.
- In one embodiment, a computer program product searches for data stores related to a set of one or more nodes in a graph database. Stored on a computer readable storage medium are first program instructions and second program instructions. The first program instructions are to point from a particular data store in a data structure to a particular synthetic context event node in a synthetic context event database, where the synthetic context event database comprises multiple synthetic context event nodes. The particular synthetic context event node contains a descriptor of an attribute of a particular entity represented by a particular graph node in a graph database, and the particular synthetic context event node further contains a relationship described in an edge between said particular graph node and another graph node in the graph database. The second program instructions are to point from the particular synthetic context event node in the synthetic context event database to the particular graph node in the graph database, such that pointing to the particular synthetic context event node and the particular graph node associates the particular data store with the particular entity represented by the graph node via the particular synthetic context event node.
-
FIG. 1 depicts an exemplary system and network in which the present disclosure may be implemented; -
FIG. 2 illustrates a novel context-based system for searching for data stores related to an entity described by a set of one or more nodes in a graph database; and -
FIG. 3 is a high-level flow chart of one or more steps performed by a computer processor to locate data stores related to an entity represented by a set of one or more nodes in a graph database. - As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
- Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
- A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
- Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
- Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
- Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
- These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
- The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
- With reference now to the figures, and in particular to
FIG. 1 , there is depicted a block diagram of an exemplary system and network that may be utilized by and in the implementation of the present invention. Note that some or all of the exemplary architecture, including both depicted hardware and software, shown for and withincomputer 102 may be utilized bysoftware deploying server 150 and/or adata storage system 152. -
Exemplary computer 102 includes aprocessor 104 that is coupled to a system bus 106.Processor 104 may utilize one or more processors, each of which has one or more processor cores. Avideo adapter 108, which drives/supports adisplay 110, is also coupled to system bus 106. System bus 106 is coupled via abus bridge 112 to an input/output (I/O)bus 114. An I/O interface 116 is coupled to I/O bus 114. I/O interface 116 affords communication with various I/O devices, including akeyboard 118, amouse 120, a media tray 122 (which may include storage devices such as CD-ROM drives, multi-media interfaces, etc.), aprinter 124, and external USB port(s) 126. While the format of the ports connected to I/O interface 116 may be any known to those skilled in the art of computer architecture, in one embodiment some or all of these ports are universal serial bus (USB) ports. - As depicted,
computer 102 is able to communicate with asoftware deploying server 150, using anetwork interface 130.Network interface 130 is a hardware network interface, such as a network interface card (NIC), etc.Network 128 may be an external network such as the Internet, or an internal network such as an Ethernet or a virtual private network (VPN). - A hard drive interface 132 is also coupled to system bus 106. Hard drive interface 132 interfaces with a
hard drive 134. In one embodiment,hard drive 134 populates asystem memory 136, which is also coupled to system bus 106. System memory is defined as a lowest level of volatile memory incomputer 102. This volatile memory includes additional higher levels of volatile memory (not shown), including, but not limited to, cache memory, registers and buffers. Data that populatessystem memory 136 includescomputer 102's operating system (OS) 138 andapplication programs 144. -
OS 138 includes ashell 140, for providing transparent user access to resources such asapplication programs 144. Generally,shell 140 is a program that provides an interpreter and an interface between the user and the operating system. More specifically,shell 140 executes commands that are entered into a command line user interface or from a file. Thus,shell 140, also called a command processor, is generally the highest level of the operating system software hierarchy and serves as a command interpreter. The shell provides a system prompt, interprets commands entered by keyboard, mouse, or other user input media, and sends the interpreted command(s) to the appropriate lower levels of the operating system (e.g., a kernel 142) for processing. Note that whileshell 140 is a text-based, line-oriented user interface, the present invention will equally well support other user interface modes, such as graphical, voice, gestural, etc. - As depicted,
OS 138 also includeskernel 142, which includes lower levels of functionality forOS 138, including providing essential services required by other parts ofOS 138 andapplication programs 144, including memory management, process and task management, disk management, and mouse and keyboard management. -
Application programs 144 include a renderer, shown in exemplary manner as abrowser 146.Browser 146 includes program modules and instructions enabling a world wide web (WWW) client (i.e., computer 102) to send and receive network messages to the Internet using hypertext transfer protocol (HTTP) messaging, thus enabling communication withsoftware deploying server 150 and other computer systems. -
Application programs 144 incomputer 102's system memory (as well assoftware deploying server 150's system memory) also include a context-based data store locating program (CBDSLP) 148.CBDSLP 148 includes code for implementing the processes described below, including those described inFIGS. 2-3 . In one embodiment,computer 102 is able to downloadCBDSLP 148 fromsoftware deploying server 150, including in an on-demand basis, wherein the code inCBDSLP 148 is not downloaded until needed for execution. Note further that, in one embodiment of the present invention,software deploying server 150 performs all of the functions associated with the present invention (including execution of CBDSLP 148), thus freeingcomputer 102 from having to use its own internal computing resources to executeCBDSLP 148. - The
data storage system 152 stores an electronic data structure, which may be business/medical records, audio files, video files, website entries, text files, etc. In one embodiment,computer 102 contains the graph database storage system and the synthetic context event database storage system described and claimed herein, while the data storage system is a same or separate system for storing data stores as described and claimed herein. - Note that the hardware elements depicted in
computer 102 are not intended to be exhaustive, but rather are representative to highlight essential components required by the present invention. For instance,computer 102 may include alternate memory storage devices such as magnetic cassettes, digital versatile disks (DVDs), Bernoulli cartridges, and the like. These and other variations are intended to be within the spirit and scope of the present invention. - Note that
CBDSLP 148 is able to generate and/or utilize some or all of the databases depicted in the context-based system inFIG. 2 . - With reference now to
FIG. 2 , a novel context-basedsystem 200 for searching for data stores related to an entity described by a set of one or more nodes in a graph database is presented. The context-basedsystem 200 comprises a graph database storage system for storing agraph database 202, a synthetic context event database storage system for storing a syntheticcontext event database 204, and access to a data storage system for storing adata structure 206. In one embodiment, the graph database storage system and the synthetic context event database storage system are part ofcomputer 102 shown inFIG. 1 , while the data storage system is thedata storage system 152 depicted inFIG. 1 . - The
graph database 202 is a schema-less database in which data is organized as a set of nodes (objects) with properties (attributes or values). These nodes are linked to other nodes through edges, which describe the relationship between two nodes. As depicted inFIG. 2 , these nodes are shown as graph nodes 208 a-208 n, where “n” is an integer. The graph nodes 208 a-208 n are linked by edges 210 x-210 z, which describe relationships between linked graph nodes. For example, assume thatgraph node 208 a represents “circulatory diseases” (or persons having a circulatory disease), andgraph node 208 b represented “myocardial infarction” (or persons who are having or have had a myocardial infarction). Theedge 210 x thus describes thegraph node 208 b as being a subset ofgraph node 208 a. - In another example, assume that
graph node 208 b still represents persons who have had a myocardial infarction, andgraph node 208 n represents all persons who are morbidly obese, live in a certain city/state/country/geographical region, drink green tea, etc. Theedge 210 z would thus describe the persons represented bygraph node 208 b as “being morbidly obese”, “a resident of the certain city/state/country/geographical region”, a “drinker of green tea”, etc. - In one embodiment, two or more graph nodes can be clustered into a
graph node cluster 212, which includesgraph node 208 a andgraph node 208 n. Thisgraph node cluster 212 has been predetermined to include all related graph nodes (as indicated by linking edges and/or shared attributes within the graph nodes). - The synthetic
context event database 204 is made up of synthetic context event nodes 214 a-214 n (where “n” is an integer). These synthetic context event nodes 214 a-214 n may or may not be interlinked (i.e., logically associated with one another by having shared attributes, etc.). Each of these synthetic context event nodes 214 a-214 n represents a synthetic event (i.e., they are fabricated by consolidating information from different sources which may or may not describe an actual event). In the present invention, each of the synthetic context event nodes 214 a-214 n contains a descriptor of an attribute of a particular entity (i.e., information from one or more of the graph nodes 208 a-208 n) and a relationship between a particular entity and another entity represented by another graph node (i.e., the information found in an edge such asedge 210 x). - The
data structure 206 is a database of multiple data stores 216 a-216 n, which may be text documents, hierarchical files, tuples, object oriented database stores, spreadsheet cells, uniform resource locators (URLs), etc. - In one embodiment, the
data structure 206 is a database of text documents (represented by one or more of the data stores 216 a-216 n), such as journal articles, webpage articles, electronically-stored business/medical/operational notes, etc. - In one embodiment, the
data structure 206 is a database of text, audio, video, multimedia, etc. files (represented by one or more of the data stores 216 a-216 n) that are stored in a hierarchical manner, such as in a tree diagram, a lightweight directory access protocol (LDAP) folder, etc. - In one embodiment, the
data structure 206 is a relational database, which is a collection of data items organized through a set of formally described tables. A table is made up of one or more rows, known as “tuples”. Each of the tuples (represented by one or more of the data stores 216 a-216 n) share common attributes, which in the table are described by column headings. Each tuple also includes a key, which may be a primary key or a foreign key. A primary key is an identifier (e.g., a letter, number, symbol, etc.) that is stored in a first data cell of a local tuple. A foreign key is typically identical to the primary key, except that it is stored in a first data cell of a remote tuple, thus allowing the local tuple to be logically linked to the foreign tuple. - In one embodiment, the
data structure 206 is an object oriented database, which stores objects (represented by one or more of the data stores 216 a-216 n). As understood by those skilled in the art of computer software, an object contains both attributes, which are data (i.e., integers, strings, real numbers, references to another object, etc.), as well as methods, which are similar to procedures/functions, and which define the behavior of the object. Thus, the object oriented database contains both executable code and data. - In one embodiment, the
data structure 206 is a spreadsheet, which is made up of rows and columns of cells (represented by one or more of the data stores 216 a-216 n). Each cell (represented by one or more of the data stores 216 a-216 n) contains numeric or text data, or a formula to calculate a value based on the content of one or more of the other cells in the spreadsheet. - In one embodiment, the
data structure 206 is a collection of universal resource locators (URLs) for identifying a webpage, in which each URL (or a collection or URLs) is represented by one or more of the data stores 216 a-216 n. - These described types of data stores are exemplary, and are not to be construed as limiting what types of data stores are found within
data structure 206. - Note that the
data structure 206 is homogenous in one embodiment, whiledata structure 206 is heterogeneous in another embodiment. For example, assume in a first example thatdata structure 206 is a relational database, and all of the data stores 216 a-216 n are tuples. In this first example,data structure 206 is homogenous, since all of the data stores 216 a-216 n are of the same type. However, assume in a second example thatdata store 216 a is a text document,data store 216 b is an MRI image,data store 216 c is a tuple from a relational database, etc. In this second example,data structure 206 is a heterogeneous data structure, since it contains data stores that are of different formats. - In one embodiment, the synthetic
context event database 204 may include filtering logic (i.e., part ofCBDSLP 148 shown inFIG. 1 ), which allows the user to specify what type of data store is to be located. For example, such a filter may request only image files (e.g., an MRI image), or it may request only text files (e.g., journal articles), or it may request only universal resource locators (URLs) to websites, or it may request only tuples from a relational database, or it may request any combination of data stores (i.e., a combination of data stores that are inclusive of some types of data stores and are exclusive of other types of data stores). - As depicted in
FIG. 2 , a first pointer points to one of the synthetic context event nodes 214 a-214 n, and a second pointer points from one of the synthetic context event nodes 214 a-214 n to one of the data stores 216 a-216 n. For example, assume thatgraph node 208 b represents persons who have had a myocardial infarction, and information fromedge 210 x and/or edge 210 z describes those persons' relationships to entities represented bygraph node 208 a andgraph node 208 n, respectively. In this example, syntheticcontext event node 214 a contains the information stored ingraph node 208 b as well as the information stored inedge 210 x and/or edge 210 z. In order to associate the syntheticcontext event node 214 a withgraph node 208 b and its edges, afirst pointer 218 a points fromgraph node 208 b to syntheticcontext event node 214 a. - Similarly, a
first pointer 218 b points fromgraph node cluster 212, which includesgraph node 208 a andgraph node 208 n, as well as the information inedge 210 y, to a syntheticcontext event node 214 b. In one embodiment, only the information found ingraph node 208 a,graph node 208 n, and edge 210 y are represented in syntheticcontext event node 214 b. In another embodiment, the information found ingraph node 208 a,graph node 208 n, and edge 210 y, as well as the information found inedge 210 x and/or edge 210 z are also represented in syntheticcontext event node 214 b. - As further depicted in
FIG. 2 , a second pointer points from a synthetic context event node, which was pointed to by the first pointer, to a particular data store in the data structure, such that the first pointer and the second pointer associate the particular data store with the particular entity represented in the graph database via the particular synthetic context event node. For example, continue to assume thatgraph node 208 b represents persons who have had a myocardial infarction; that information fromedge 210 x and/or edge 210 z describes those persons' relation to entities represented bygraph node 208 a andgraph node 208 n, respectively; that syntheticcontext event node 214 a contains the information stored ingraph node 208 b as well as the information stored inedge 210 x and/or edge 210 z; and thatfirst pointer 218 a points fromgraph node 208 b to syntheticcontext event node 214 a.Second pointer 220 a now points from syntheticcontent event node 214 a todata store 216 a, ultimately resulting in the linkage ofgraph node 208 b todata store 216 a via syntheticcontext event node 214 a. -
Second pointer 220 a points to (i.e., identifies and/or retrieves)data store 216 a according to a logical relationship between the syntheticcontext event node 214 a and thedata store 216 a. For example, assume that syntheticcontext event node 214 a contains entries (i.e., information fromgraph node 208 b and/oredges 210 x and/or 210 z) that match descriptive data such as a keyword, metadata, context-based mined data, etc. found indata store 216 a. In one embodiment, this descriptive data describes an activity related to the entity described ingraph node 208 b. For example,graph node 208 b may describe the person's lifestyle (i.e., smoker, runner, etc.), medical history (i.e., has had a particular medical procedure, other disease, treatment in a particular facility or by a particular doctor, etc.), travel history, etc. - If the
first pointer 218 b points fromgraph node cluster 212 to syntheticcontext event node 214 b, thensecond pointer 220 b points todata store 216 c, and/orsecond pointer 220 e points todata store 216 n, thus associatinggraph node cluster 212 withdata store 216 c and/ordata store 216 n. - Note that, in one embodiment, the single synthetic
context event node 214 a has a firstsecond pointer 220 c as well as a secondsecond pointer 220 d, which point todifferent data stores - Thus, as described above and depicted in
FIG. 2 , a context-basedsystem 200 for searching for data stores related to a set of one or more nodes in a graph database is represented. A graph database storage system (i.e., part ofcomputer 102 shown inFIG. 1 ) contains agraph database 202 made up of multiple graph nodes 208 a-208 n. Each of the multiple graph nodes 208 a-208 n stores an attribute of a particular entity, and each of the multiple graph nodes 208 a-208 n is logically coupled to another graph node by one or more of the edges 210 x-210 z, where each edge describes a relationship between entities represented by coupled graph nodes. - A first pointer (e.g.,
first pointer 218 a) points from a particular graph node (e.g.,graph node 208 b) to a particular synthetic context event node (e.g., syntheticcontext event node 214 a) in the syntheticcontext event database 204. A synthetic context event database storage system (i.e., also part ofcomputer 102 shown inFIG. 1 ) contains the syntheticcontext event database 204, which contains multiple synthetic context event nodes 214 a-214 n. Each of the multiple synthetic context event nodes 214 a-214 n contains a descriptor of one or more attributes of the particular entity represented bygraph node 208 b, as well as the relationship (e.g., found inedge 210 x) between that particular entity and another entity represented by another graph node (e.g.,graph node 208 a). - A second pointer (e.g.,
second pointer 220 a) points from the particular syntheticcontext event node 214 a in the syntheticcontext event database 204 to aparticular data store 216 a in adata structure 206. Thus, thefirst pointer 218 a and thesecond pointer 220 a associate theparticular data store 216 a with the particular entity represented in the graph database (i.e., bygraph node 208 b) via the particular syntheticcontext event node 214 a. - Note that data stores 216 a-216 n within
data structure 206 do not merely describe or provide additional detail about the information found in a graph node from thegraph database 202 and/or the edges 210 x-210 z that connect various graph nodes. Rather, these data stores 216 a-216 n are data stores that are deemed to be related to a particular graph node by a particular synthetic context event node. That is, a particular data store from data stores 216 a-216 n is deemed to be associated to a particular intermediate synthetic context event node by virtue of the contextual information (i.e., information supplied by one or more graph nodes and/or their edges) found in that particular intermediate synthetic context event node. The data store itself, however, is not merely an expansion of this contextual information, but rather is a data store that describes a study, analysis, evaluation, entity association, etc. of the entity(s) described by the relevant graph node(s). For example,data store 216 a may be a medical journal article that has been associated with syntheticcontext event node 214 a (e.g., by containing certain keywords, metadata, etc.). This medical journal article does not merely describe the information fromgraph node 208 b and/or the edges to thatgraph node 208 b, but rather provides medical details about a particular medical study. These medical details include those not suggested by the information from thegraph node 208 b and/or the edges to thatgraph node 208 b. - For example, in one embodiment, the particular data store (e.g.,
data store 216 a) describes an activity (i.e., lifestyle, medical activities/history, hobbies, travel history, etc.) related to the particular entity depicted bygraph node 208 b. - In another exemplary embodiment, the particular data store (e.g.,
data store 216 a) describes a set of diagnostic and/or treatment options for medical patients described bygraph node 208 b. - In another exemplary embodiment, the particular data store (e.g.,
data store 216 a) describes a set of financial, legal, technical, etc. reports related to a business entity described bygraph node 208 b. - In order to link a particular synthetic context event node to a particular data store, various linkage processes may be utilized. For example, in one embodiment, the
second pointer 220 a uses a keyword that is in both the particular syntheticcontext event node 214 a and theparticular data store 216 a to point to theparticular data store 216 a. In another exemplary embodiment, thesecond pointer 220 a uses metadata that is associated with both the particular syntheticcontext event node 214 a and theparticular data store 216 a to point to theparticular data store 216 a. - In one embodiment, multiple second pointers (e.g.,
second pointers context event node 214 a tomultiple data stores data structure 206. - In one embodiment, a first
second pointer 220 d from a first syntheticcontext event node 214 a and a secondsecond pointer 220 b from a second syntheticcontext event node 214 b point to asame data store 216 c in thedata structure 206. That is, asame data store 216 c may be relevant to two synthetic context event nodes, and thus related to two different graph nodes and/or graph node clusters. - In one embodiment, the
data structure 206 is a relational database, such that the particular data store (e.g.,data store 216 a) is a tuple within the relational database. - In one embodiment, the
data structure 206 is a text data structure, such that the particular data store (e.g.,data store 216 a) describes a study (i.e., a medical journal article, doctor's notes, engineering notes, financial reports, etc.) about the particular entity represented in thegraph database 202. - With reference now to
FIG. 3 , a high-level flow chart of one or more steps performed by a computer processor to locate data stores related to an entity represented by a set of one or more nodes in a graph database is presented. Afterinitiator block 302, a processor points (e.g., using a first pointer such asfirst pointer 218 a shown inFIG. 2 ) from a particular graph node (e.g.,graph node 208 b) in a graph database (e.g., graph database 202) to a particular synthetic context event node (e.g., syntheticcontext event node 214 a) in a synthetic content event database (e.g., synthetic context event database 204 (block 304). The graph database comprises multiple graph nodes, wherein each of the multiple graph nodes stores an attribute of a particular entity that is described by the particular graph node. Each of the multiple graph nodes is logically coupled to another graph node by an edge, which describes a relationship between entities represented by coupled graph nodes. - The synthetic context event database comprises multiple synthetic context event nodes. Each of the multiple synthetic context event nodes contains a descriptor of the attribute of the particular entity as well as the relationship between the particular entity and another entity represented by another graph node in the graph database.
- As described in
block 306, the processor then points (e.g., using a second pointer such assecond pointer 220 a shown inFIG. 2 ) from the particular synthetic context event node (e.g., syntheticcontext event node 214 a) in the synthetic context event database to a particular data store (e.g.,data store 216 a) in a data structure (e.g., data structure 206). Thus, pointing to the particular synthetic context event node and the particular data store associates the particular data store with the particular entity (which is represented by a graph node in the graph database) via the particular synthetic context event node. - As described in
block 308, the identified data store (e.g.,data store 216 a) is then retrieved (e.g., for display, printing, etc.) and sent to a computer system, requesting entity, etc. The process ends atterminator block 310. - While the present invention has been described in the context of a graph node searching for a data store, in one embodiment the process works in the other direction. That is, a particular data store can search for a particular graph node. For example, assume that
data store 216 a is a medical journal article about a particular disease (e.g., cancer). Continue to assume that, as described above,data store 216 a is linked bysecond pointer 220 a to syntheticcontext event node 214 a, which leads a user to graphnode 208 b. At this point, a user who initially only knew about the medical journal article represented asdata store 216 a also now knows not only aboutgraph node 208 b, but also knows about linkedgraph nodes graph node 208 b represented a particular genetic marker, which may or may not have been mentioned in the medical journal article, andgraph nodes graph node cluster 212 and/orgraph node 208 a orgraph node 208 n todata store 216 c and/ordata store 216 n. That is, the present invention enables the user to identifydata store 216 c and/ordata store 216 n, through the use of syntheticcontext event node 214 b, which is pointed to byfirst pointer 218 b from thegraph node cluster 212 and/orgraph node 208 a and/orgraph node 208 n. Note thatdata store 216 c and/ordata store 216 n may be another medical journal article, a set of medical examination results (e.g., X-rays, MRIs, lab workups, etc.), etc., which may be in any digital format (e.g., PDF, JPEG, MPEG, .doc, etc.). In this example, note thatdata store 216 n is accessible only via syntheticcontext event node 214 b, whiledata store 216 c is accessible via syntheticcontext event node 214 a or syntheticcontext event node 214 b. - In another embodiment, knowing about
data store 216 a enables the user to also know aboutdata store 216 b anddata store 216 c, since syntheticcontext event node 214 a not only points todata store 216 a but also todata store 216 b anddata store 216 c. Thus, the syntheticcontext event node 214 a, which is defined by the graph database features described above, allows the user to make a direct connection between different data stores within thedata structure 206 via the syntheticcontext event node 214 a, such thatdata store 216 b and/ordata store 216 c can be located and/or retrieved based on the user's awareness ofdata store 216 a. - As described in the example shown in
FIG. 2 ,data store 216 n is accessible only via syntheticcontext event node 214 b, whiledata store 216 c is accessible via syntheticcontext event node 214 a or syntheticcontext event node 214 b. Ifdata store 216 a is used to locate additional related data stores within thedata structure 206, thendata store 216 c can be located directly via the syntheticcontext event node 214 a. However, in order to locate/retrievedata store 216 n based on awareness ofdata store 216 a, a pathway through syntheticcontext event node 214 a must be traversed to thegraph database 202, and then returning through the syntheticcontext event node 214 b in order to locate/retrieve thedata store 216 n via the pointers described herein. - The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
- The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
- The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of various embodiments of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the present invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the present invention. The embodiment was chosen and described in order to best explain the principles of the present invention and the practical application, and to enable others of ordinary skill in the art to understand the present invention for various embodiments with various modifications as are suited to the particular use contemplated.
- Note further that any methods described in the present disclosure may be implemented through the use of a VHDL (VHSIC Hardware Description Language) program and a VHDL chip. VHDL is an exemplary design-entry language for Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), and other similar electronic devices. Thus, any software-implemented method described herein may be emulated by a hardware-based VHDL program, which is then applied to a VHDL chip, such as a FPGA.
- Having thus described embodiments of the present invention of the present application in detail and by reference to illustrative embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the present invention defined in the appended claims.
Claims (20)
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US13/592,905 US8676857B1 (en) | 2012-08-23 | 2012-08-23 | Context-based search for a data store related to a graph node |
DE102013215661.8A DE102013215661A1 (en) | 2012-08-23 | 2013-08-08 | Contextual search for a stored file associated with a graph node |
CN201310371164.0A CN103631847B (en) | 2012-08-23 | 2013-08-23 | The method and system of the data storage that search based on context is relevant to graphical nodes |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US13/592,905 US8676857B1 (en) | 2012-08-23 | 2012-08-23 | Context-based search for a data store related to a graph node |
Publications (2)
Publication Number | Publication Date |
---|---|
US20140059083A1 true US20140059083A1 (en) | 2014-02-27 |
US8676857B1 US8676857B1 (en) | 2014-03-18 |
Family
ID=50069755
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/592,905 Active US8676857B1 (en) | 2012-08-23 | 2012-08-23 | Context-based search for a data store related to a graph node |
Country Status (3)
Country | Link |
---|---|
US (1) | US8676857B1 (en) |
CN (1) | CN103631847B (en) |
DE (1) | DE102013215661A1 (en) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20160110434A1 (en) * | 2014-10-17 | 2016-04-21 | Vmware, Inc. | Method and system that determine whether or not two graph-like representations of two systems describe equivalent systems |
US9798829B1 (en) * | 2013-10-22 | 2017-10-24 | Google Inc. | Data graph interface |
CN108829728A (en) * | 2018-05-10 | 2018-11-16 | 杭州依图医疗技术有限公司 | A kind of storage method and device in medical terminology library |
US10313526B2 (en) | 2014-05-29 | 2019-06-04 | Avaya Inc. | Mechanism for work assignment in a graph-based contact center |
CN112528090A (en) * | 2020-12-11 | 2021-03-19 | 北京百度网讯科技有限公司 | Graph data storage method and storage device |
Families Citing this family (126)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8930331B2 (en) | 2007-02-21 | 2015-01-06 | Palantir Technologies | Providing unique views of data based on changes or rules |
US8984390B2 (en) | 2008-09-15 | 2015-03-17 | Palantir Technologies, Inc. | One-click sharing for screenshots and related documents |
US9092482B2 (en) | 2013-03-14 | 2015-07-28 | Palantir Technologies, Inc. | Fair scheduling for mixed-query loads |
US8799240B2 (en) | 2011-06-23 | 2014-08-05 | Palantir Technologies, Inc. | System and method for investigating large amounts of data |
US9547693B1 (en) | 2011-06-23 | 2017-01-17 | Palantir Technologies Inc. | Periodic database search manager for multiple data sources |
US9280532B2 (en) | 2011-08-02 | 2016-03-08 | Palantir Technologies, Inc. | System and method for accessing rich objects via spreadsheets |
US8732574B2 (en) | 2011-08-25 | 2014-05-20 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US8504542B2 (en) | 2011-09-02 | 2013-08-06 | Palantir Technologies, Inc. | Multi-row transactions |
US9348677B2 (en) | 2012-10-22 | 2016-05-24 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9380431B1 (en) | 2013-01-31 | 2016-06-28 | Palantir Technologies, Inc. | Use of teams in a mobile application |
US9146986B2 (en) * | 2013-03-14 | 2015-09-29 | Facebook, Inc. | Systems, methods, and apparatuses for implementing an interface to view and explore socially relevant concepts of an entity graph |
US10037314B2 (en) | 2013-03-14 | 2018-07-31 | Palantir Technologies, Inc. | Mobile reports |
US8909656B2 (en) | 2013-03-15 | 2014-12-09 | Palantir Technologies Inc. | Filter chains with associated multipath views for exploring large data sets |
US8937619B2 (en) | 2013-03-15 | 2015-01-20 | Palantir Technologies Inc. | Generating an object time series from data objects |
US8818892B1 (en) | 2013-03-15 | 2014-08-26 | Palantir Technologies, Inc. | Prioritizing data clusters with customizable scoring strategies |
US9965937B2 (en) | 2013-03-15 | 2018-05-08 | Palantir Technologies Inc. | External malware data item clustering and analysis |
US8917274B2 (en) | 2013-03-15 | 2014-12-23 | Palantir Technologies Inc. | Event matrix based on integrated data |
US10275778B1 (en) | 2013-03-15 | 2019-04-30 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation based on automatic malfeasance clustering of related data in various data structures |
US8868486B2 (en) | 2013-03-15 | 2014-10-21 | Palantir Technologies Inc. | Time-sensitive cube |
US8799799B1 (en) | 2013-05-07 | 2014-08-05 | Palantir Technologies Inc. | Interactive geospatial map |
US9223773B2 (en) | 2013-08-08 | 2015-12-29 | Palatir Technologies Inc. | Template system for custom document generation |
US9335897B2 (en) | 2013-08-08 | 2016-05-10 | Palantir Technologies Inc. | Long click display of a context menu |
US8713467B1 (en) | 2013-08-09 | 2014-04-29 | Palantir Technologies, Inc. | Context-sensitive views |
US9785317B2 (en) | 2013-09-24 | 2017-10-10 | Palantir Technologies Inc. | Presentation and analysis of user interaction data |
US8938686B1 (en) | 2013-10-03 | 2015-01-20 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
US8812960B1 (en) | 2013-10-07 | 2014-08-19 | Palantir Technologies Inc. | Cohort-based presentation of user interaction data |
US9116975B2 (en) * | 2013-10-18 | 2015-08-25 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive simultaneous querying of multiple data stores |
US8924872B1 (en) | 2013-10-18 | 2014-12-30 | Palantir Technologies Inc. | Overview user interface of emergency call data of a law enforcement agency |
US9021384B1 (en) | 2013-11-04 | 2015-04-28 | Palantir Technologies Inc. | Interactive vehicle information map |
US8868537B1 (en) | 2013-11-11 | 2014-10-21 | Palantir Technologies, Inc. | Simple web search |
US9105000B1 (en) | 2013-12-10 | 2015-08-11 | Palantir Technologies Inc. | Aggregating data from a plurality of data sources |
US10025834B2 (en) | 2013-12-16 | 2018-07-17 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US9552615B2 (en) | 2013-12-20 | 2017-01-24 | Palantir Technologies Inc. | Automated database analysis to detect malfeasance |
US10356032B2 (en) | 2013-12-26 | 2019-07-16 | Palantir Technologies Inc. | System and method for detecting confidential information emails |
US9043696B1 (en) | 2014-01-03 | 2015-05-26 | Palantir Technologies Inc. | Systems and methods for visual definition of data associations |
US8832832B1 (en) | 2014-01-03 | 2014-09-09 | Palantir Technologies Inc. | IP reputation |
US9009827B1 (en) | 2014-02-20 | 2015-04-14 | Palantir Technologies Inc. | Security sharing system |
US9483162B2 (en) | 2014-02-20 | 2016-11-01 | Palantir Technologies Inc. | Relationship visualizations |
US9727376B1 (en) | 2014-03-04 | 2017-08-08 | Palantir Technologies, Inc. | Mobile tasks |
US8924429B1 (en) | 2014-03-18 | 2014-12-30 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US9857958B2 (en) | 2014-04-28 | 2018-01-02 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive access of, investigation of, and analysis of data objects stored in one or more databases |
US9009171B1 (en) | 2014-05-02 | 2015-04-14 | Palantir Technologies Inc. | Systems and methods for active column filtering |
US9535974B1 (en) | 2014-06-30 | 2017-01-03 | Palantir Technologies Inc. | Systems and methods for identifying key phrase clusters within documents |
US9619557B2 (en) | 2014-06-30 | 2017-04-11 | Palantir Technologies, Inc. | Systems and methods for key phrase characterization of documents |
US9202249B1 (en) | 2014-07-03 | 2015-12-01 | Palantir Technologies Inc. | Data item clustering and analysis |
US9256664B2 (en) | 2014-07-03 | 2016-02-09 | Palantir Technologies Inc. | System and method for news events detection and visualization |
US10572496B1 (en) | 2014-07-03 | 2020-02-25 | Palantir Technologies Inc. | Distributed workflow system and database with access controls for city resiliency |
US9419992B2 (en) | 2014-08-13 | 2016-08-16 | Palantir Technologies Inc. | Unwanted tunneling alert system |
US9454281B2 (en) | 2014-09-03 | 2016-09-27 | Palantir Technologies Inc. | System for providing dynamic linked panels in user interface |
US9767172B2 (en) | 2014-10-03 | 2017-09-19 | Palantir Technologies Inc. | Data aggregation and analysis system |
US9501851B2 (en) | 2014-10-03 | 2016-11-22 | Palantir Technologies Inc. | Time-series analysis system |
US9785328B2 (en) | 2014-10-06 | 2017-10-10 | Palantir Technologies Inc. | Presentation of multivariate data on a graphical user interface of a computing system |
US9984133B2 (en) | 2014-10-16 | 2018-05-29 | Palantir Technologies Inc. | Schematic and database linking system |
US9229952B1 (en) | 2014-11-05 | 2016-01-05 | Palantir Technologies, Inc. | History preserving data pipeline system and method |
US9043894B1 (en) | 2014-11-06 | 2015-05-26 | Palantir Technologies Inc. | Malicious software detection in a computing system |
US10362133B1 (en) | 2014-12-22 | 2019-07-23 | Palantir Technologies Inc. | Communication data processing architecture |
US9348920B1 (en) | 2014-12-22 | 2016-05-24 | Palantir Technologies Inc. | Concept indexing among database of documents using machine learning techniques |
US10552994B2 (en) | 2014-12-22 | 2020-02-04 | Palantir Technologies Inc. | Systems and interactive user interfaces for dynamic retrieval, analysis, and triage of data items |
US9367872B1 (en) | 2014-12-22 | 2016-06-14 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures |
US10452651B1 (en) | 2014-12-23 | 2019-10-22 | Palantir Technologies Inc. | Searching charts |
US9817563B1 (en) | 2014-12-29 | 2017-11-14 | Palantir Technologies Inc. | System and method of generating data points from one or more data stores of data items for chart creation and manipulation |
US9870205B1 (en) | 2014-12-29 | 2018-01-16 | Palantir Technologies Inc. | Storing logical units of program code generated using a dynamic programming notebook user interface |
US9335911B1 (en) | 2014-12-29 | 2016-05-10 | Palantir Technologies Inc. | Interactive user interface for dynamic data analysis exploration and query processing |
US10372879B2 (en) | 2014-12-31 | 2019-08-06 | Palantir Technologies Inc. | Medical claims lead summary report generation |
US9727560B2 (en) | 2015-02-25 | 2017-08-08 | Palantir Technologies Inc. | Systems and methods for organizing and identifying documents via hierarchies and dimensions of tags |
US9891808B2 (en) | 2015-03-16 | 2018-02-13 | Palantir Technologies Inc. | Interactive user interfaces for location-based data analysis |
US9886467B2 (en) | 2015-03-19 | 2018-02-06 | Plantir Technologies Inc. | System and method for comparing and visualizing data entities and data entity series |
CN104765843B (en) * | 2015-04-16 | 2018-11-09 | 国家电网公司 | A kind of Graphic Interface Control method for electric power real-time monitoring system |
US9672257B2 (en) | 2015-06-05 | 2017-06-06 | Palantir Technologies Inc. | Time-series data storage and processing database system |
US9384203B1 (en) | 2015-06-09 | 2016-07-05 | Palantir Technologies Inc. | Systems and methods for indexing and aggregating data records |
US9407652B1 (en) | 2015-06-26 | 2016-08-02 | Palantir Technologies Inc. | Network anomaly detection |
US9392008B1 (en) | 2015-07-23 | 2016-07-12 | Palantir Technologies Inc. | Systems and methods for identifying information related to payment card breaches |
US9454785B1 (en) | 2015-07-30 | 2016-09-27 | Palantir Technologies Inc. | Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data |
US9996595B2 (en) | 2015-08-03 | 2018-06-12 | Palantir Technologies, Inc. | Providing full data provenance visualization for versioned datasets |
US9456000B1 (en) | 2015-08-06 | 2016-09-27 | Palantir Technologies Inc. | Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications |
US9600146B2 (en) | 2015-08-17 | 2017-03-21 | Palantir Technologies Inc. | Interactive geospatial map |
US10489391B1 (en) | 2015-08-17 | 2019-11-26 | Palantir Technologies Inc. | Systems and methods for grouping and enriching data items accessed from one or more databases for presentation in a user interface |
US9537880B1 (en) | 2015-08-19 | 2017-01-03 | Palantir Technologies Inc. | Anomalous network monitoring, user behavior detection and database system |
US10853378B1 (en) | 2015-08-25 | 2020-12-01 | Palantir Technologies Inc. | Electronic note management via a connected entity graph |
US11150917B2 (en) | 2015-08-26 | 2021-10-19 | Palantir Technologies Inc. | System for data aggregation and analysis of data from a plurality of data sources |
US10402385B1 (en) | 2015-08-27 | 2019-09-03 | Palantir Technologies Inc. | Database live reindex |
US9485265B1 (en) | 2015-08-28 | 2016-11-01 | Palantir Technologies Inc. | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces |
US10706434B1 (en) | 2015-09-01 | 2020-07-07 | Palantir Technologies Inc. | Methods and systems for determining location information |
US9576015B1 (en) | 2015-09-09 | 2017-02-21 | Palantir Technologies, Inc. | Domain-specific language for dataset transformations |
US9454564B1 (en) | 2015-09-09 | 2016-09-27 | Palantir Technologies Inc. | Data integrity checks |
US10296617B1 (en) | 2015-10-05 | 2019-05-21 | Palantir Technologies Inc. | Searches of highly structured data |
US10044745B1 (en) | 2015-10-12 | 2018-08-07 | Palantir Technologies, Inc. | Systems for computer network security risk assessment including user compromise analysis associated with a network of devices |
US9424669B1 (en) | 2015-10-21 | 2016-08-23 | Palantir Technologies Inc. | Generating graphical representations of event participation flow |
US10613722B1 (en) | 2015-10-27 | 2020-04-07 | Palantir Technologies Inc. | Distorting a graph on a computer display to improve the computer's ability to display the graph to, and interact with, a user |
US10212056B2 (en) * | 2015-11-17 | 2019-02-19 | Microsoft Technology Licensing, Llc | Graph node with automatically adjusting input ports |
US9542446B1 (en) | 2015-12-17 | 2017-01-10 | Palantir Technologies, Inc. | Automatic generation of composite datasets based on hierarchical fields |
US10268735B1 (en) | 2015-12-29 | 2019-04-23 | Palantir Technologies Inc. | Graph based resolution of matching items in data sources |
US9823818B1 (en) | 2015-12-29 | 2017-11-21 | Palantir Technologies Inc. | Systems and interactive user interfaces for automatic generation of temporal representation of data objects |
US10698938B2 (en) | 2016-03-18 | 2020-06-30 | Palantir Technologies Inc. | Systems and methods for organizing and identifying documents via hierarchies and dimensions of tags |
US10650558B2 (en) | 2016-04-04 | 2020-05-12 | Palantir Technologies Inc. | Techniques for displaying stack graphs |
US10007674B2 (en) | 2016-06-13 | 2018-06-26 | Palantir Technologies Inc. | Data revision control in large-scale data analytic systems |
US10324609B2 (en) | 2016-07-21 | 2019-06-18 | Palantir Technologies Inc. | System for providing dynamic linked panels in user interface |
US10719188B2 (en) | 2016-07-21 | 2020-07-21 | Palantir Technologies Inc. | Cached database and synchronization system for providing dynamic linked panels in user interface |
US9753935B1 (en) | 2016-08-02 | 2017-09-05 | Palantir Technologies Inc. | Time-series data storage and processing database system |
US10437840B1 (en) | 2016-08-19 | 2019-10-08 | Palantir Technologies Inc. | Focused probabilistic entity resolution from multiple data sources |
US9881066B1 (en) | 2016-08-31 | 2018-01-30 | Palantir Technologies, Inc. | Systems, methods, user interfaces and algorithms for performing database analysis and search of information involving structured and/or semi-structured data |
US10133588B1 (en) | 2016-10-20 | 2018-11-20 | Palantir Technologies Inc. | Transforming instructions for collaborative updates |
US10318630B1 (en) | 2016-11-21 | 2019-06-11 | Palantir Technologies Inc. | Analysis of large bodies of textual data |
US10884875B2 (en) | 2016-12-15 | 2021-01-05 | Palantir Technologies Inc. | Incremental backup of computer data files |
US10223099B2 (en) | 2016-12-21 | 2019-03-05 | Palantir Technologies Inc. | Systems and methods for peer-to-peer build sharing |
EP3343403A1 (en) | 2016-12-28 | 2018-07-04 | Palantir Technologies Inc. | Systems and methods for retrieving and processing data for display |
US10460602B1 (en) | 2016-12-28 | 2019-10-29 | Palantir Technologies Inc. | Interactive vehicle information mapping system |
US10475219B1 (en) | 2017-03-30 | 2019-11-12 | Palantir Technologies Inc. | Multidimensional arc chart for visual comparison |
US10896097B1 (en) | 2017-05-25 | 2021-01-19 | Palantir Technologies Inc. | Approaches for backup and restoration of integrated databases |
GB201708818D0 (en) | 2017-06-02 | 2017-07-19 | Palantir Technologies Inc | Systems and methods for retrieving and processing data |
US10956406B2 (en) | 2017-06-12 | 2021-03-23 | Palantir Technologies Inc. | Propagated deletion of database records and derived data |
US10403011B1 (en) | 2017-07-18 | 2019-09-03 | Palantir Technologies Inc. | Passing system with an interactive user interface |
US11334552B2 (en) | 2017-07-31 | 2022-05-17 | Palantir Technologies Inc. | Lightweight redundancy tool for performing transactions |
US10417224B2 (en) | 2017-08-14 | 2019-09-17 | Palantir Technologies Inc. | Time series database processing system |
CN107832323B (en) * | 2017-09-14 | 2021-09-17 | 北京知道未来信息技术有限公司 | Distributed realization system and method based on graph database |
US10216695B1 (en) | 2017-09-21 | 2019-02-26 | Palantir Technologies Inc. | Database system for time series data storage, processing, and analysis |
US11281726B2 (en) | 2017-12-01 | 2022-03-22 | Palantir Technologies Inc. | System and methods for faster processor comparisons of visual graph features |
US10614069B2 (en) | 2017-12-01 | 2020-04-07 | Palantir Technologies Inc. | Workflow driven database partitioning |
US11016986B2 (en) | 2017-12-04 | 2021-05-25 | Palantir Technologies Inc. | Query-based time-series data display and processing system |
US10929476B2 (en) | 2017-12-14 | 2021-02-23 | Palantir Technologies Inc. | Systems and methods for visualizing and analyzing multi-dimensional data |
CN108363785A (en) * | 2018-02-12 | 2018-08-03 | 平安科技(深圳)有限公司 | Data relationship methods of exhibiting, device, computer equipment and storage medium |
US11599369B1 (en) | 2018-03-08 | 2023-03-07 | Palantir Technologies Inc. | Graphical user interface configuration system |
US10754822B1 (en) | 2018-04-18 | 2020-08-25 | Palantir Technologies Inc. | Systems and methods for ontology migration |
US10885021B1 (en) | 2018-05-02 | 2021-01-05 | Palantir Technologies Inc. | Interactive interpreter and graphical user interface |
GB201807534D0 (en) | 2018-05-09 | 2018-06-20 | Palantir Technologies Inc | Systems and methods for indexing and searching |
US11119630B1 (en) | 2018-06-19 | 2021-09-14 | Palantir Technologies Inc. | Artificial intelligence assisted evaluations and user interface for same |
Family Cites Families (108)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5642503A (en) | 1993-12-15 | 1997-06-24 | Microsoft Corporation | Method and computer system for implementing concurrent accesses of a database record by multiple users |
JP3512866B2 (en) | 1994-09-19 | 2004-03-31 | 富士通株式会社 | Grouping of nodes in network and data transfer method |
US5689620A (en) | 1995-04-28 | 1997-11-18 | Xerox Corporation | Automatic training of character templates using a transcription and a two-dimensional image source model |
US5664179A (en) | 1995-06-27 | 1997-09-02 | Mci Corporation | Modified skip list database structure and method for access |
US5701460A (en) * | 1996-05-23 | 1997-12-23 | Microsoft Corporation | Intelligent joining system for a relational database |
US5956728A (en) | 1996-07-17 | 1999-09-21 | Next Software, Inc. | Object graph editing context and methods of use |
CA2270472A1 (en) | 1996-11-15 | 1998-05-28 | Michael Schindler | Computer sorting system for data compression |
US6285999B1 (en) | 1997-01-10 | 2001-09-04 | The Board Of Trustees Of The Leland Stanford Junior University | Method for node ranking in a linked database |
US6178433B1 (en) | 1997-07-15 | 2001-01-23 | International Business Machines Corporation | Method and system for generating materials for presentation on a non-frame capable web browser |
EP0996886B1 (en) * | 1997-07-25 | 2002-10-09 | BRITISH TELECOMMUNICATIONS public limited company | Software system generation |
US7337174B1 (en) | 1999-07-26 | 2008-02-26 | Microsoft Corporation | Logic table abstraction layer for accessing configuration information |
US7216115B1 (en) | 1999-11-10 | 2007-05-08 | Fastcase.Com, Inc. | Apparatus and method for displaying records responsive to a database query |
US6768986B2 (en) | 2000-04-03 | 2004-07-27 | Business Objects, S.A. | Mapping of an RDBMS schema onto a multidimensional data model |
US6633868B1 (en) * | 2000-07-28 | 2003-10-14 | Shermann Loyall Min | System and method for context-based document retrieval |
WO2002046916A2 (en) | 2000-10-20 | 2002-06-13 | Polexis, Inc. | Extensible information system (xis) |
WO2002059773A1 (en) * | 2000-12-04 | 2002-08-01 | Thinkshare Corp. | Modular distributed mobile data applications |
US6944619B2 (en) | 2001-04-12 | 2005-09-13 | Primentia, Inc. | System and method for organizing data |
US6553371B2 (en) | 2001-09-20 | 2003-04-22 | International Business Machines Corporation | Method and system for specifying and displaying table joins in relational database queries |
US20030149562A1 (en) | 2002-02-07 | 2003-08-07 | Markus Walther | Context-aware linear time tokenizer |
US7441264B2 (en) | 2002-06-24 | 2008-10-21 | International Business Machines Corporation | Security objects controlling access to resources |
JP2004177996A (en) | 2002-11-22 | 2004-06-24 | Toshiba Corp | Hierarchical database device and hierarchical database construction method |
US20040153461A1 (en) | 2003-02-03 | 2004-08-05 | Brown Mark L. | System and method for collecting and disseminating information |
US7748036B2 (en) | 2003-04-01 | 2010-06-29 | Sytex, Inc. | Methods for categorizing input data |
EP1703283A4 (en) | 2003-07-18 | 2011-11-23 | A & T Corp | Clinical examination analyzing device, clinical examination analyzing method, and program for allowing computer to execute the method |
US7818572B2 (en) | 2003-12-09 | 2010-10-19 | Dominic Kotab | Security system and method |
US7437005B2 (en) | 2004-02-17 | 2008-10-14 | Microsoft Corporation | Rapid visual sorting of digital files and data |
US7493335B2 (en) | 2004-07-02 | 2009-02-17 | Graphlogic Inc. | Object process graph relational database interface |
US7571163B2 (en) | 2004-07-13 | 2009-08-04 | Hewlett-Packard Development Company, L.P. | Method for sorting a data structure |
US20060235843A1 (en) | 2005-01-31 | 2006-10-19 | Textdigger, Inc. | Method and system for semantic search and retrieval of electronic documents |
JP4755427B2 (en) | 2005-02-23 | 2011-08-24 | インターナショナル・ビジネス・マシーンズ・コーポレーション | Database access system and database access method |
GB2424722A (en) | 2005-03-21 | 2006-10-04 | Think Software Pty Ltd | Method and apparatus for generating relevance sensitive collation keys |
US7478102B2 (en) * | 2005-03-28 | 2009-01-13 | Microsoft Corporation | Mapping of a file system model to a database object |
US20070006321A1 (en) | 2005-07-01 | 2007-01-04 | International Business Machines Corporation | Methods and apparatus for implementing context-dependent file security |
WO2007044763A2 (en) | 2005-10-11 | 2007-04-19 | Rsa Security Inc. | System and method for detecting fraudulent transactions |
US7613690B2 (en) | 2005-10-21 | 2009-11-03 | Aol Llc | Real time query trends with multi-document summarization |
CA2542379A1 (en) | 2006-04-07 | 2007-10-07 | Cognos Incorporated | Packaged warehouse solution system |
US7523118B2 (en) | 2006-05-02 | 2009-04-21 | International Business Machines Corporation | System and method for optimizing federated and ETL'd databases having multidimensionally constrained data |
US7526501B2 (en) | 2006-05-09 | 2009-04-28 | Microsoft Corporation | State transition logic for a persistent object graph |
US7797319B2 (en) | 2006-05-15 | 2010-09-14 | Algebraix Data Corporation | Systems and methods for data model mapping |
US7853577B2 (en) * | 2006-06-09 | 2010-12-14 | Ebay Inc. | Shopping context engine |
US20070300077A1 (en) | 2006-06-26 | 2007-12-27 | Seshadri Mani | Method and apparatus for biometric verification of secondary authentications |
WO2008031088A2 (en) | 2006-09-08 | 2008-03-13 | Advanced Fuel Research, Inc. | Image analysis by object addition and recovery |
US7996393B1 (en) | 2006-09-29 | 2011-08-09 | Google Inc. | Keywords associated with document categories |
US7752154B2 (en) * | 2007-02-26 | 2010-07-06 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for analysis of criminal and security information |
US7809660B2 (en) | 2006-10-03 | 2010-10-05 | International Business Machines Corporation | System and method to optimize control cohorts using clustering algorithms |
US8145582B2 (en) * | 2006-10-03 | 2012-03-27 | International Business Machines Corporation | Synthetic events for real time patient analysis |
US8055603B2 (en) * | 2006-10-03 | 2011-11-08 | International Business Machines Corporation | Automatic generation of new rules for processing synthetic events using computer-based learning processes |
US8190610B2 (en) | 2006-10-05 | 2012-05-29 | Yahoo! Inc. | MapReduce for distributed database processing |
US20080091503A1 (en) | 2006-10-11 | 2008-04-17 | International Business Machines Corporation | E-meeting preparation management |
US7523123B2 (en) | 2006-11-16 | 2009-04-21 | Yahoo! Inc. | Map-reduce with merge to process multiple relational datasets |
US20080133474A1 (en) | 2006-11-30 | 2008-06-05 | Yahoo! Inc. | Bioinformatics computation using a maprreduce-configured computing system |
US8046358B2 (en) | 2007-02-16 | 2011-10-25 | Ge Healthcare | Context-based information retrieval |
US7805391B2 (en) | 2007-02-26 | 2010-09-28 | International Business Machines Corporation | Inference of anomalous behavior of members of cohorts and associate actors related to the anomalous behavior |
US7970759B2 (en) | 2007-02-26 | 2011-06-28 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for pharmaceutical analysis |
US7792774B2 (en) | 2007-02-26 | 2010-09-07 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for analysis of chaotic events |
US7853611B2 (en) * | 2007-02-26 | 2010-12-14 | International Business Machines Corporation | System and method for deriving a hierarchical event based database having action triggers based on inferred probabilities |
US7805390B2 (en) | 2007-02-26 | 2010-09-28 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for analysis of complex accidents |
US7788203B2 (en) | 2007-02-26 | 2010-08-31 | International Business Machines Corporation | System and method of accident investigation for complex situations involving numerous known and unknown factors along with their probabilistic weightings |
US7783586B2 (en) | 2007-02-26 | 2010-08-24 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for analysis of biological systems |
US7788202B2 (en) | 2007-02-26 | 2010-08-31 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for clinical applications |
US7792776B2 (en) | 2007-02-26 | 2010-09-07 | International Business Machines Corporation | System and method to aid in the identification of individuals and groups with a probability of being distressed or disturbed |
US8069188B2 (en) * | 2007-05-07 | 2011-11-29 | Applied Technical Systems, Inc. | Database system storing a data structure that includes data nodes connected by context nodes and related method |
US7788213B2 (en) | 2007-06-08 | 2010-08-31 | International Business Machines Corporation | System and method for a multiple disciplinary normalization of source for metadata integration with ETL processing layer of complex data across multiple claim engine sources in support of the creation of universal/enterprise healthcare claims record |
US7979449B2 (en) * | 2007-08-07 | 2011-07-12 | Atasa Ltd. | System and method for representing, organizing, storing and retrieving information |
US7930262B2 (en) | 2007-10-18 | 2011-04-19 | International Business Machines Corporation | System and method for the longitudinal analysis of education outcomes using cohort life cycles, cluster analytics-based cohort analysis, and probabilistic data schemas |
US8250581B1 (en) | 2007-10-28 | 2012-08-21 | Hewlett-Packard Development Company, L.P. | Allocating computer resources to candidate recipient computer workloads according to expected marginal utilities |
US8341626B1 (en) | 2007-11-30 | 2012-12-25 | Hewlett-Packard Development Company, L. P. | Migration of a virtual machine in response to regional environment effects |
DE202008002980U1 (en) | 2008-03-03 | 2008-09-18 | Linguatec Sprachtechnologien Gmbh | Data correlation system and mobile terminal therefor |
US7953686B2 (en) | 2008-03-17 | 2011-05-31 | International Business Machines Corporation | Sensor and actuator based validation of expected cohort behavior |
US20090287676A1 (en) | 2008-05-16 | 2009-11-19 | Yahoo! Inc. | Search results with word or phrase index |
US8271475B2 (en) * | 2008-05-27 | 2012-09-18 | International Business Machines Corporation | Application of user context to searches in a virtual universe |
US8495701B2 (en) | 2008-06-05 | 2013-07-23 | International Business Machines Corporation | Indexing of security policies |
US8199982B2 (en) | 2008-06-18 | 2012-06-12 | International Business Machines Corporation | Mapping of literature onto regions of interest on neurological images |
WO2009155680A1 (en) | 2008-06-25 | 2009-12-30 | Novell, Inc. | Copying workload files to a virtual disk |
US20100070640A1 (en) | 2008-09-15 | 2010-03-18 | Allen Jr Lloyd W | Method and system for allowing access to presentation materials for a meeting |
US20100131293A1 (en) | 2008-11-26 | 2010-05-27 | General Electric Company | Interactive multi-axis longitudinal health record systems and methods of use |
US8341095B2 (en) | 2009-01-12 | 2012-12-25 | Nec Laboratories America, Inc. | Supervised semantic indexing and its extensions |
KR101052631B1 (en) | 2009-01-29 | 2011-07-28 | 성균관대학교산학협력단 | A method for providing a related word for a search term using the co-occurrence frequency and the device using the same |
US8150882B2 (en) | 2009-03-03 | 2012-04-03 | Microsoft Corporation | Mapping from objects to data model |
US20100241644A1 (en) * | 2009-03-19 | 2010-09-23 | Microsoft Corporation | Graph queries of information in relational database |
US8713038B2 (en) | 2009-04-02 | 2014-04-29 | Pivotal Software, Inc. | Integrating map-reduce into a distributed relational database |
US8161048B2 (en) * | 2009-04-24 | 2012-04-17 | At&T Intellectual Property I, L.P. | Database analysis using clusters |
US8234285B1 (en) | 2009-07-10 | 2012-07-31 | Google Inc. | Context-dependent similarity measurements |
US8402098B2 (en) | 2009-08-13 | 2013-03-19 | Clark C. Dircz | System and method for intelligence gathering and analysis |
US8281065B2 (en) | 2009-09-01 | 2012-10-02 | Apple Inc. | Systems and methods for determining the status of memory locations in a non-volatile memory |
US8321454B2 (en) | 2009-09-14 | 2012-11-27 | Myspace Llc | Double map reduce distributed computing framework |
GB201013195D0 (en) | 2009-09-28 | 2010-09-22 | Qinetiq Ltd | Processor |
US8694514B2 (en) | 2009-10-12 | 2014-04-08 | Oracle International Corporation | Collaborative filtering engine |
US8064677B2 (en) | 2009-11-25 | 2011-11-22 | Fujifilm Corporation | Systems and methods for measurement of objects of interest in medical images |
US9305089B2 (en) | 2009-12-08 | 2016-04-05 | At&T Intellectual Property I, L.P. | Search engine device and methods thereof |
JP5314614B2 (en) | 2010-02-05 | 2013-10-16 | 富士フイルム株式会社 | MEDICAL IMAGE DISPLAY DEVICE, MEDICAL IMAGE DISPLAY METHOD, AND PROGRAM |
US8280839B2 (en) | 2010-02-25 | 2012-10-02 | Mitsubishi Electric Research Laboratories, Inc. | Nearest neighbor methods for non-Euclidean manifolds |
US8595234B2 (en) | 2010-05-17 | 2013-11-26 | Wal-Mart Stores, Inc. | Processing data feeds |
US8560365B2 (en) | 2010-06-08 | 2013-10-15 | International Business Machines Corporation | Probabilistic optimization of resource discovery, reservation and assignment |
US8775625B2 (en) | 2010-06-16 | 2014-07-08 | Juniper Networks, Inc. | Virtual machine mobility in data centers |
US8478879B2 (en) | 2010-07-13 | 2013-07-02 | International Business Machines Corporation | Optimizing it infrastructure configuration |
US8418184B2 (en) | 2010-09-24 | 2013-04-09 | International Business Machines Corporation | Use of constraint-based linear programming to optimize hardware system usage |
US9037720B2 (en) | 2010-11-19 | 2015-05-19 | International Business Machines Corporation | Template for optimizing IT infrastructure configuration |
US8615511B2 (en) | 2011-01-22 | 2013-12-24 | Operational Transparency LLC | Data visualization interface |
US8849931B2 (en) * | 2011-03-15 | 2014-09-30 | Idt Messaging, Llc | Linking context-based information to text messages |
US20120246148A1 (en) | 2011-03-22 | 2012-09-27 | Intergraph Technologies Company | Contextual Display and Scrolling of Search Results in Graphical Environment |
CN102722412A (en) | 2011-03-31 | 2012-10-10 | 国际商业机器公司 | Combined computational device and method |
US8510326B2 (en) | 2011-04-11 | 2013-08-13 | Google Inc. | Priority dimensional data conversion path reporting |
US9647989B2 (en) | 2011-04-27 | 2017-05-09 | Symantec Corporation | System and method of data interception and conversion in a proxy |
US10748092B2 (en) | 2011-06-07 | 2020-08-18 | The Boeing Company | Systems and methods for creating intuitive context for analysis data |
US8533195B2 (en) | 2011-06-27 | 2013-09-10 | Microsoft Corporation | Regularized latent semantic indexing for topic modeling |
US9298837B2 (en) | 2011-11-10 | 2016-03-29 | Room 77, Inc. | Efficient indexing and caching infrastructure for metasearch |
US8799269B2 (en) * | 2012-01-03 | 2014-08-05 | International Business Machines Corporation | Optimizing map/reduce searches by using synthetic events |
-
2012
- 2012-08-23 US US13/592,905 patent/US8676857B1/en active Active
-
2013
- 2013-08-08 DE DE102013215661.8A patent/DE102013215661A1/en active Pending
- 2013-08-23 CN CN201310371164.0A patent/CN103631847B/en active Active
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9798829B1 (en) * | 2013-10-22 | 2017-10-24 | Google Inc. | Data graph interface |
US10313526B2 (en) | 2014-05-29 | 2019-06-04 | Avaya Inc. | Mechanism for work assignment in a graph-based contact center |
US20160110434A1 (en) * | 2014-10-17 | 2016-04-21 | Vmware, Inc. | Method and system that determine whether or not two graph-like representations of two systems describe equivalent systems |
US9703890B2 (en) * | 2014-10-17 | 2017-07-11 | Vmware, Inc. | Method and system that determine whether or not two graph-like representations of two systems describe equivalent systems |
CN108829728A (en) * | 2018-05-10 | 2018-11-16 | 杭州依图医疗技术有限公司 | A kind of storage method and device in medical terminology library |
CN112528090A (en) * | 2020-12-11 | 2021-03-19 | 北京百度网讯科技有限公司 | Graph data storage method and storage device |
Also Published As
Publication number | Publication date |
---|---|
CN103631847B (en) | 2016-12-28 |
US8676857B1 (en) | 2014-03-18 |
DE102013215661A1 (en) | 2014-02-27 |
CN103631847A (en) | 2014-03-12 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8676857B1 (en) | Context-based search for a data store related to a graph node | |
US9286358B2 (en) | Dimensionally constrained synthetic context objects database | |
US9619580B2 (en) | Generation of synthetic context objects | |
US9607048B2 (en) | Generation of synthetic context frameworks for dimensionally constrained hierarchical synthetic context-based objects | |
US9251237B2 (en) | User-specific synthetic context object matching | |
US8645349B2 (en) | Indexing structures using synthetic document summaries | |
US9146994B2 (en) | Pivot facets for text mining and search | |
US8914323B1 (en) | Policy-based data-centric access control in a sorted, distributed key-value data store | |
US9223846B2 (en) | Context-based navigation through a database | |
EP3420469B1 (en) | Content classes for object storage indexing systems | |
US10152538B2 (en) | Suggested search based on a content item | |
US11151154B2 (en) | Generation of synthetic context objects using bounded context objects | |
US8782777B2 (en) | Use of synthetic context-based objects to secure data stores | |
Berendsohn et al. | OpenUp! Creating a cross-domain pipeline for natural history data | |
Kar et al. | Same-day sputum microscopy: The road ahead in tuberculosis diagnosis | |
US8898165B2 (en) | Identification of null sets in a context-based electronic document search | |
Peng et al. | Reliable access to massive restricted texts: Experience‐based evaluation | |
US10073868B1 (en) | Adding and maintaining individual user comments to a row in a database table | |
Huxford et al. | Open Data Training Workshop: Synthetic Data & The 2023 Pediatric Sepsis Data Challenge | |
Mawji et al. | Open Data Training Video: A proposed data de-identification framework for clinical research | |
Mátyás et al. | A novel data storage logic in the cloud | |
Liu et al. | Address and Participant Entity-Resolution in a Large, Cohort Observational Study Utilizing an Open-source Entity Resolution Tool (OYSTER) |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ADAMS, SAMUEL S.;FRIEDLANDER, ROBERT R.;GERKEN, JOHN K., III;AND OTHERS;REEL/FRAME:028837/0410 Effective date: 20120822 |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
FPAY | Fee payment |
Year of fee payment: 4 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1552); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 8 |