WO2018227015A2 - Analyse prédictive rapide de très grands ensembles de données utilisant un graphe de calcul distribué entraîné par un acteur - Google Patents

Analyse prédictive rapide de très grands ensembles de données utilisant un graphe de calcul distribué entraîné par un acteur Download PDF

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WO2018227015A2
WO2018227015A2 PCT/US2018/036550 US2018036550W WO2018227015A2 WO 2018227015 A2 WO2018227015 A2 WO 2018227015A2 US 2018036550 W US2018036550 W US 2018036550W WO 2018227015 A2 WO2018227015 A2 WO 2018227015A2
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data
transformation
pipeline
processor
actor
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PCT/US2018/036550
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English (en)
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WO2018227015A3 (fr
Inventor
Jason Crabtree
Andrew Sellers
John Uchiyama
Ian Macleod
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Fractal Industries, Inc.
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Priority claimed from US15/616,427 external-priority patent/US20170371726A1/en
Application filed by Fractal Industries, Inc. filed Critical Fractal Industries, Inc.
Priority to EP18813012.4A priority Critical patent/EP3635551A4/fr
Publication of WO2018227015A2 publication Critical patent/WO2018227015A2/fr
Publication of WO2018227015A3 publication Critical patent/WO2018227015A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/54—Interprogram communication

Definitions

  • the present invention is in the field of analysis of very large data sets using distributed computational graph tools which allow for transformation of data through both linear and nonlinear transformation pipelines.
  • Data pipelines which are a progression of functions which each perform some action or transformation on a data stream, offer a mechanism to process quantities of data in the volume discussed directly above.
  • data pipelines have either been extremely limited in what they do, for example "move data from a web based merchant site to a distributed data store; extract all purchases and classify by product type and region; store the result logs" or have been rigidly programmed and possibly required the uses of highly specific remote protocol calls to perform needed tasks.
  • Even with these additions their capabilities are very limited and they have all been linear in configuration which prevents their use for analysis and conclusion or action discovery in a majority of complex situations where branching or even recurrent modification is needed.
  • Hie inventor has developed a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, that intelligently combines processing of a current data stream with the ability to retrieve relevant stored data in such a way that conclusions or actions could be drawn in a predictive manner.
  • An actor-driven distributed computational graph may use a pipeline orchestrator to create and manage individual data pipelines while providing data caching to enable interactions between specific activity actors within pipelines.
  • Each pipeline may then comprise a pipeline manager that creates and manages individual activity actors and directs operations within the pipeline while reporting back to the pipeline orchestrator.
  • information may be exchanged between peers without any involvement of centralized service beyond coordination by a pipeline manager.
  • a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph comprising: a pipeline orchestrator configured to: create a plurality of transformation pipelines each comprising at least a pipeline manager; cache a plurality of data contexts provided by a pipeline manager; a pipeline manager configured to: create a plurality of activity actors; provide reporting data to the pipeline orchestrator; receive at least a data context from an activity actor; provide the data context to the pipeline orchestrator, an activity actor configured to: receive at least a set of data as a transformation input; perform an individual transformation upon a set of data; produce a data context based at least in part on the individual transformation; provide the transformed set of data as a transformation output; and provide the data context as a context output, is disclosed.
  • Rg. 1 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect
  • FIG. 2 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect
  • fig. 3 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect.
  • FIG. 4 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect
  • FIG. 5 is a diagram of an exemplary architecture for a system where streams of input data from one or more of a plurality of sources are analyzed to predict outcome using both batch analysis of acquired data and transformation pipeline manipulation of current streaming data according to one aspect
  • FIG. 6 is a diagram of an exemplary architecture for a linear transformation pipeline system which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to one aspect
  • Fig. 7 is a diagram of an exemplary architecture for a transformation pipeline system where one of the transformations receives input from more than one source which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to one aspect
  • FIG. 8 is a diagram of an exemplary architecture for a transformation pipeline system where the output of one data transformation servers as the input of more than one downstream transformations which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to one aspect
  • FIG. 9 is a diagram of an exemplary architecture for a transformation pipeline system where a set of three data transformations act to form a cyclical pipeline which also introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to one aspect.
  • FIG. 10 is a process flow diagram of a method for the receipt, processing and predictive analysis of streaming data according to one aspect
  • FIG. 11 is a process flow diagram of a method for representing the operation of the transformation pipeline as a directed graph function according to one aspect
  • Fig. 12 is a process flow diagram of a method for a linear data transformation pipeline according to one aspect
  • FIG. 13 is a process flow diagram of a method for the disposition of input from two antecedent data transformations into a single data transformation of transformation pipeline according to one aspect
  • Fig. 14 is a process flow diagram of a method for the disposition of output of one data transformation that then serves as input to two postliminary data transformations according to one aspect
  • FIG. 15 is a process flow diagram of a method for processing a set of three or more data transformations within a data transformation pipeline where output of the last member transformation of the set serves as input of the first member transformation thereby creating a cyclical relationship according to one aspect
  • Fig. 16 is a process flow diagram of a method for the receipt and use of streaming data into batch storage and analysis of changes over time, repetition of specific data sequences or the presence of critical data points according to one aspect
  • Fig. 17 is a process flow diagram for an exemplary method for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect.
  • Fig. 18 is a process flow diagram for an exemplary method for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect
  • Rg. 19 is a process flow diagram for an exemplary method for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect
  • Rg. 20 is a block diagram illustrating an exemplary hardware architecture of a computing device.
  • Rg. 21 is a block diagram illustrating an exemplary logical architecture for a client device.
  • Rg. 22 is a block diagram showing an exemplary architectural arrangement of clients, servers, and external services.
  • Rg. 23 is another block diagram iflustrating an exemplary hardware architecture of a computing device.
  • the inventor has conceived, and reduced to practice, various systems and methods for predictive analysis of very large data sets using an actor-driven distributed computational graph.
  • Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise.
  • devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical.
  • steps may be performed simultaneously despite being described or implied as occurring non- simultaneously (e.g., because one step is described after the other step).
  • the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred.
  • steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence.
  • graph is a representation of information and relationships, where each primary unit of information makes up a “node” or “vertex” of the graph and the relationship between two nodes makes up an edge of the graph.
  • nodes are elements of a workflow that produce data output (or other side effects to include internal data changes), and nodes may be for example (but not limited to) data stores that are queried or transformations that return the result of arbitrary operations over input data. Nodes can be further qualified by the connection of one or more descriptors or "properties" to that node.
  • transformation graphs which are highly variable in size and node, edge composition as the system processes data streams.
  • transformation graph may assume many shapes and sizes with a vast topography of edge relationships. The examples given were chosen for illustrative purposes only and represent a small number of the simplest of possibilities. These examples should not be taken to define the possible graphs expected as part of operation of the invention.
  • transformation'' is a function performed on zero or more streams of input data which results in a single stream of output which may or may not then be used as input for another transformation. Transformations may comprise any combination of machine, human or machine-human interactions Transformations need not change data that enters them, one example of this type of transformation would be a storage transformation which would receive input and then act as a queue for that data for subsequent transformations. As implied above, a specific transformation may generate output data in the absence of input data. A time stamp serves as an example. In the invention, transformations are placed into pipelines such that the output of one transformation may serve as an input for another. These pipelines can consist of two or more transformations with the number of transformations limited only by the resources of the system.
  • transformation pipelines have been linear with each transformation in the pipeline receiving input from one antecedent and providing output to one subsequent with no branching or iteration.
  • Other pipeline configurations are possible.
  • the invention is designed to permit several of these configurations including, but not limited to: linear, afferent branch, efferent branch and cyclical.
  • a “database” or “data storage subsystem” (these terms may be considered substantially synonymous), as used herein, is a system adapted for the long-term storage, indexing, and retrieval of data, the retrieval typically being via some sort of querying interface or language.
  • Database may be used to refer to relational database management systems known in the art, but should not be considered to be limited to such systems.
  • any group of data storage systems of databases referred to herein may be included together in a single database management system operating on a single machine, or they may be included in a single database management system operating on a cluster of machines as is known in the art
  • any single database (such as an expanded private capital markets database) may be implemented on a single machine, on a set of machines using clustering technology, on several machines connected by one or more messaging systems known in the art, or in a master/slave arrangement common in the art
  • a "data context”, as used herein, refers to a set of arguments identifying the location of data. This could be a Rabbit queue, a .csv file in cloud-based storage, or any other such location reference except a single event or record. Activities may pass either events or data contexts to each other for processing. The nature of a pipeline allows for direct information passing between activities, and data locations or files do not need to be predetermined at pipeline start
  • Each batch activity may contain a "source” data context (this may be a streaming context if the upstream activities are streaming), and a "destination” data context (which is passed to the next activity).
  • Streaming activities may have an optional "destination” streaming data context (optional meaning: caching/persistence of events vs. ephemeral), though this should not be part of the initial implementation.
  • Fig. 1 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 100, according to one aspect.
  • a directed computational graph (DCG) 100 may comprise a pipeline orchestrator 101 that may be used to perform the functions of a transformation pipeline software module 561 as described below, with reference to Fig. 5.
  • Pipeline orchestrator 101 may spawn a plurality of child pipeline clusters UOa-b, which may be used as dedicated workers for streamlining parallel processing.
  • Pipeline orchestrator 101 may provide a software API for starting, stopping, submitting, or saving pipelines. When a pipeline is started, pipeline orchestrator 101 may send the pipeline information to an available worker node UOa-b, for example using AKKATM clustering. For each pipeline initialized by pipeline orchestrator 101, a reporting object with status information may be maintained. Streaming activities may report the last time an event was processed, and the number of events processed. Batch activities may report status messages as they occur.
  • Pipeline orchestrator 101 may perform batch caching using, for example, an IGFSTM caching filesystem. This allows activities 112a-d within a pipeline UOa-b to pass data contexts to one another, with any necessary parameter configurations.
  • a pipeline manager lllarb may be spawned for every new running pipeline, and may be used to send activity, status, lifecycle, and event count information to the pipeline orchestrator 101.
  • a plurality of activity actors 112ard may be created by a pipeline manager l llarb to handle individual tasks, and provide output to data services 120a-d, optionally using a client API 130 for integration with external services or products.
  • Data models used in a given pipeline may be determined by the specific pipeline and activities, as directed by a pipeline manager llla-b.
  • Each pipeline manager llla-b controls and directs the operation of any activity actors 112a-d spawned by it
  • a service-specific client API 130 is separated from any particular activity actor 112a-d and may be handled by a dedicated service actor in a separate cluster.
  • a pipeline process may need to coordinate streaming data between tasks.
  • a pipeline manager llla-b may spawn service connectors to dynamically create TCP connections between activity instances 112a-d.
  • Data contexts may be maintained for each individual activity 112a-d, and may be cached for provision to other activities 112a-d as needed.
  • a data context defines how an activity accesses information, and an activity 112a-d may process data or simply forward it to a next step. Forwarding data between pipeline steps may route data through a streaming context or batch context
  • a client service cluster 130 may operate a plurality of service actors 221 a-d to serve the requests of activity actors 112a-d, ideally maintaining enough service actors 221a-d to support each activity per the service type. These may also be arranged within service clusters 220a-d, in an alternate arrangement described below in Fig. 2.
  • Fig. 2 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 100, according to one aspect
  • a DCG 100 may be used with a messaging system 210 that enables communication with any number of various services and protocols, relaying messages and translating them as needed into protocol-specific API system calls for interoperability with external systems (rather than requiring a particular protocol or service to be integrated into a DCG 100).
  • Service actors 221a-d may be logically grouped into service clusters 220a-d, in a manner similar to the logical organization of activity actors 112a-d within clusters llOa-b in a data pipeline.
  • a logging service 230 may be used to log and sample DCG requests and messages during operation while notification service 240 may be used to receive alerts and other notifications during operation (for example to alert on errors, which may then be diagnosed by reviewing records from logging service 230), and by being connected externally to messaging system 210, logging and notification services can be added, removed, or modified during operation without impacting DCG 100.
  • a plurality of DCG protocols 250a-b may be used to provide structured messaging between a DCG 100 and messaging system 210, or to enable messaging system 210 to distribute DCG messages across service clusters 220a-d as shown.
  • a service protocol 260 may be used to define service interactions so that a DCG 100 may be modified without impacting service implementations. In this manner, it can be appreciated that the overall structure of a system using an actor-driven DCG 100 operates in a modular fashion, enabling modification and substitution of various components without impacting other operations or requiring additional reconfiguration.
  • Fig. 3 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 100, according to one aspect
  • a variant messaging arrangement may utilize messaging system 210 as a messaging broker using a streaming protocol 310, transmitting and receiving messages immediately using messaging system 210 as a message broker to bridge communication between service actors 221a-b as needed.
  • individual services 120a-b may be utilized as a messaging broker using a streaming protocol 310, transmitting and receiving messages immediately using messaging system 210 as a message broker to bridge communication between service actors 221a-b as needed.
  • individual services 120a-b may
  • a batch context 320 communicates directly in a batch context 320, using a data context service 330 as a broker to batch-process and relay messages between services 120a-b.
  • Fig. 4 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 100, according to one aspect
  • a variant messaging arrangement may utilize a service connector 410 as a central message broker between a plurality of service actors 221a-b, bridging messages in a streaming context 310 while a data context service 330 continues to provide direct peer-to-peer messaging between individual services 120a-b in a batch context 320.
  • Fig. 5 is a block diagram of an exemplary architecture for a system 500 for predictive analysis of very large data sets using a distributed computational graph.
  • streaming input feeds 510 may be a variety of data sources which may include but are not limited to the internet 511, arrays of physical sensors 512, database servers 513, electronic monitoring equipment 514 and direct human interaction 515 ranging from a relatively few number of participants to a large crowd sour ting campaign.
  • Streaming data from any combinations of listed sources and those not listed may also be expected to occur as part of the operation of the invention as the number of streaming input sources is not limited by the design.
  • All incoming streaming data may be passed through a data filter software module 520 to remove information that has been damaged in transit, is misconfigured, or is malformed in some way that precludes use.
  • Many of the filter parameters may be expected to be preset prior to operation, however, design of the invention makes provision for the behavior of the filter software module 520 to be changed as progression of analysis requires through the automation of the system sanity and retrain software module 563 which may serve to optimize system operation and analysis function.
  • Hie data stream may also be split into two identical substreams at the data filter software module 520 with one substream being fed into a streaming analysis pathway that includes the
  • the transformation pipeline software module 561 of the distributed computational graph 560 may be fed to data formalization software module 530 as part of the batch analysis pathway.
  • the data formalization module 530 formats the data stream entering the batch analysis pathway of the invention into data records to be stored by the input event data store 540.
  • the input event data store 540 can be a database of any architectural type known to those knowledgeable in the art, but based upon the quantity of the data the data store module would be expected to store and retrieve, options using highly distributed storage and map reduce query protocols, of which Hadoop is one, but not the only example, may be generally preferable to relational database schema.
  • Analysis of data from the input event data store may be performed by the batch event analysis software module 550.
  • This module may be used to analyze the data in the input event data store for temporal information such as trends, previous occurrences of the progression of a set of events, with outcome, the occurrence of a single specific event with all events recorded before and after whether deemed relevant at the time or not, and presence of a particular event with all documented possible causative and remedial elements, including best guess probability information.
  • temporal information such as trends, previous occurrences of the progression of a set of events, with outcome, the occurrence of a single specific event with all events recorded before and after whether deemed relevant at the time or not, and presence of a particular event with all documented possible causative and remedial elements, including best guess probability information.
  • the search parameters used by the batch event analysis software module 550 are preset by those conducting the analysis at the beginning of the process, however, as the search matures and results are gleaned from the streaming data during transformation pipeline software module 561 operation, providing the system more timely event progress details, the system sanity and retrain software module 563 may automatically update the batch analysis parameters 550. Alternately, findings outside the system may precipitate the authors of the analysis to tune the batch analysis parameters administratively from outside the system 570, 562, 563.
  • the real-time data analysis core 560 of the invention should be considered made up of a transformation pipeline software module 561, messaging module 562 and system sanity and retrain software module 563.
  • the messaging module 562 has connections from both the batch and the streaming data analysis pathways and serves as a conduit for operational as well as result information between those two parts of the invention.
  • the message module also receives messages from those administering analyses 580. Messages aggregated by the messaging module 562 may then be sent to system sanity and retrain software module 563 as appropriate.
  • this is software mat may be used to monitor the progress of streaming data analysis optimizing coordination between streaming and batch analysis pathways by modifying or "retraining" the operation of the data filter software module 520, data formalization software module 530 and batch event analysis software module 540 and the transformation pipeline module 550 of the streaming pathway when the specifics of the search may change due to results produced during streaming analysis.
  • System sanity and retrain module 563 may also monitor for data searches or transformations that are processing slowly or may have hung and for results that are outside established data stability boundaries so that actions can be implemented to resolve the issue. While the system sanity and retrain software module 563 may be designed to act autonomously and employs computer learning algorithms, according to some arrangements status updates may be made by administrators or potentially direct changes to operational parameters by such, according to the aspect
  • Streaming data entering from the outside data feeds 510 through the data filter software module 520 may be analyzed in real time within the transformation pipeline software module 561.
  • a set of functions tailored to the analysis being run are applied to the input data stream.
  • functions may be applied in a linear, directed path or in more complex configurations.
  • Functions may be modified over time during an analysis by the system sanity and retrain software module 563 and the results of the transformation pipeline, impacted by the results of batch analysis are then output in the format stipulated by the authors of the analysis which may be human readable printout, an alarm, machine readable information destined for another system or any of a plurality of other forms known to those in the art
  • FIG. 6 is a block diagram of a preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph 600.
  • streaming input from the data filter software module 520, 615 serves as input to the first transformation node 620 of the transformation pipeline. Transformation node's function is performed on input data stream and transformed output message 625 is sent to transformation node 2 630.
  • the progression of transformation nodes 620, 630, 640, 650, 660 and associated output messages from each node 625, 635, 645, 655, 665 is linear in configuration this is the simplest arrangement and, as previously noted, represents the current state of the ait While transformation nodes are described according to various aspects as uniform shape (referring to Figs.
  • transformations in a pipeline may be entirely selfcontained; certain transformations may involve direct human interaction 630, such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations among a plurality of other possibilities. Further according to the aspect, individual transformation nodes in one pipeline may represent function of another transformation pipeline.
  • FIG. 7 is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph 700.
  • streaming input from a data filter software module 520, 705 serves as input to the first transformation node 710 of the transformation pipeline.
  • Each transformation node's function 710, 720, 730, 740, 750 is performed on input data stream and transformed output message 715, 725, 735, 745, 755, 765 is sent to the next step.
  • transformation node 2 720 has a second input stream 760.
  • This input is inconsequential to the operation of the invention and could be another transformation pipeline software module, a data store, human interaction, physical sensors, monitoring equipment for other electronic systems or a stream from the internet as from a crowdsourcing campaign, just to name a few possibilities 760.
  • Functional integration of a second input stream into one transformation node requires the two input stream events be serialized.
  • the invention performs this serialization using a decomposable transformation software module (not shown), the function of which is described below, referring to Fig. 13. While transformation nodes are described according to various aspects as uniform shape (referring to Figs. 6-9), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline.
  • transformations in a pipeline may be entirely self-contained; certain transformations may involve direct human interaction 630, such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations among a plurality of other possibilities.
  • engines may be singletons (composed of a single activity or transformation).
  • leveraging the architecture in this way allows for versioning and functional decomposition (i.e. embedding entire saved workflows as single nodes in other workflows).
  • individual transformation nodes in one pipeline may represent function of another transformation pipeline.
  • the node length of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrary maximum length 710, 720, 730, 740, 750, as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. It should be further appreciated that there need be no limits on transform pipeline length. Output of the last transformation node and by extension, the transform pipeline, 750 may be sent back to messaging software module 562 for pre-decided action.
  • FIG. 8 is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph 700.
  • streaming input from a data filter software module 520, 805 serves as input to the first transformation node 810 of the transformation pipeline. Transformation node's function is performed on input data stream and transformed output message 815 is sent to transformation node 2 820.
  • transformation node 2820 sends its output stream 825, 860 to two transformation pipelines 830, 840, 850; 865, 875.
  • decomposable transformation software module (not shown), the function of which is described below, referring to Fig. 14. While transformation nodes are described according to various aspects as uniform shape (referring to Figs. 6-9), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between
  • transformations within the pipeline may involve direct human interaction 630, such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations, among a plurality of other possibilities.
  • individual transformation nodes in one pipeline may represent function of another transformation pipeline.
  • the node number of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrary maximum length 810, 820, 830, 840, 850; 865, 875 as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. Further according to the aspect, there need be no limits on transform pipeline length. Output of the last transformation node and by extension, the transform pipeline 850 may be sent back to messaging software module 562 for contemporary enabled action.
  • Fig. 9 is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph 700.
  • streaming input from a data filter software module 520, 905 serves as input to the first transformation node 910 of the transformation pipeline. Transformation node's function may be performed on an input data stream and transformed output message 915 may then be sent to transformation node 2 920.
  • transformation node 3 930 sends its output stream back 935 to transform node 1 910 forming a cyclical relationship between transformation nodes 1 910, transformation node 2 920 and transformation node 3 930.
  • the output of cyclical pipeline activity may be sent to downstream transformation nodes within the pipeline 940, 945.
  • the presence of a generalized cyclical pathway construct allows the invention to be used to solve complex iterative problems with large data sets involved, expanding ability to rapidly retrieve conclusions for complicated issues. Functional creation of a cyclical transformation pipeline requires that each cycle be serialized.
  • the invention performs this serialization using a decomposable transformation software module (not shown), the function of which is described below, referring to Fig. 15. While transformation nodes are described according to various aspects as uniform shape (referring to Figs. 6-9), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline.
  • transformations in pipelines may be entirely self-contained; certain transformations may involve direct human interaction 630, such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; still other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations, among a plurality of other possibilities. Further according to the aspect, individual transformation nodes in one pipeline may represent the cumulative function of another transformation pipeline.
  • Fig. 17 is a process flow diagram for an exemplary method 1700 for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect
  • a DCG 100 may define a plurality of data contexts for each of a plurality of actions within a data pipeline. These contexts each in turn define 1702 how their respective activities may interact with data in the pipeline. Any given activity may, based on the defined data context, either process data 1703 (generally by performing any of a number of data transformations as described previously, referring to Fig. 5), or by forwarding at least a portion of the data onward to the next step in the pipeline 1704, which may in turn be another activity with a defined context determining how it handles the forwarded data. In this manner, operation may continue in a directed fashion wherein each agent has clearly-defined capabilities and data progresses toward the end of the pipeline according to the established definitions.
  • Fig. 18 is a process flow diagram for an exemplary method 1800 for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect
  • a DCG 100 defines a data context for an activity, determining how the activity handles data that is passed to it.
  • the activity then, according to the context definition, receives data and forwards it 1802 to the next step in the data pipeline.
  • the data is then 1803 passed to a messaging system 210 that acts as a central data broker, receiving the data and passing it on 1804 to the next activity actor in the pipeline, which may then have a context assigned 1801 so that operation continues as shown.
  • This allows brokered, centralized messaging between activity actors within data pipelines, using a messaging system 210 to bridge communication between different actors.
  • fig. 19 is a process flow diagram for an exemplary method 1900 for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect
  • a pipeline orchestrator 101 may spawn a plurality of service connectors 410, each of which is configured to bridge communication between two or more service actors 221a-d for peer-to-peer messaging without using a messaging system 210 as a central broker.
  • an appropriate service connector 410 may receive the data and perform any necessary interpretation or modification to bridge service protocols 1903 between the source and destination service actors 221 a-d.
  • the modified data may then be provided 1904 to the destination service actor 221a-d.
  • Service connectors may be created and destroyed as needed without impacting other operations, producing a scalable and on-the-fly peer-to-peer messaging system that does not rely on any centralized broker to relay messages and permits direct communication between actors.
  • fig. 10 is a process flow diagram of a method 1000 for predictive analysis of very large data sets using the distributed computational graph.
  • One or more streams of data from a plurality of sources which includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, may be received by system 1001.
  • the received stream is filtered 1002 to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors.
  • Filtered data may be split into two identical streams at this point (second stream not depicted for simplicity), wherein one substream may be sent for batch processing 1600 while another substream may be formalized 1003 for transformation pipeline analysis 1004, 561, 600, 700, 800, 900 and retraining 1005.
  • Data formalization for transformation pipeline analysis acts to reformat the stream data for optimal, reliable use during analysis. Reformatting might entail, but is not limited to: setting data field order, standardizing measurement units if choices are given, splitting complex information into multiple simpler fields, and stripping unwanted characters, again, just to name a few simple examples.
  • Hie formalized data stream may be subjected to one or more transformations. Each transformation acts as a function on the data and may or may not change the data.
  • transformations working on the same data stream where the output of one transformation acts as the input to the next are represented as transformation pipelines. While the great majority of transformations in transformation pipelines receive a single stream of input, modify the data within the stream in some way and then pass the modified data as output to the next transformation in the pipeline, the invention does not require these characteristics.
  • individual transformations can receive input of expected form from more than one source 1300 or receive no input at all as would a transformation acting as a timestamp.
  • individual transformations may not modify the data as would be encountered with a data store acting as a queue for downstream transformations 1303, 1305, 1405, 1407,1505.
  • individual transformations may provide output to more than one downstream transformations 1400.
  • transformations in a transformation pipeline backbone may form a linear, a quasi- linear arrangement or may be cyclical 1500, where the output of one of the internal transformations serves as the input of one of its antecedents allowing recursive analysis to be run.
  • the result of transformation pipeline analysis may then be modified by results from batch analysis of the data stream 1600 and output 1006 in format predesigned by the authors of the analysis with could be human readable summary printout, human readable instruction printout, human-readable raw printout, data store, or machine encoded information of any format known to the art to be used in further automated analysis or action schema.
  • Fig. 11 is a process flow diagram of a method 1100 for an aspect of modeling the transformation pipeline module 561 of the invention as a directed graph using graph theory.
  • the output of an individual transformation is defined as the function out U fldjj to describe transformations that produce a single output (usable by other transformations).
  • a dependency function can now be defined such that dep(ta,tb) out(ta)in(tb)The messages carrying the data stream through the transformation pipeline 1101,1103, 1105 make up the graph edges.
  • Fig. 12 is a process flow diagram of a method 1200 for one aspect of a linear
  • transformation pipeline 1201. This is the simplest of configurations as the input stream is acted upon by the first transformation node 1202 and the remainder of the transformations within the pipeline are then performed sequentially 1202, 1203, 1204, 1205 for the entire pipeline with no introduction of new data internal to the initial node or splitting output stream prior to last node of the pipeline 1205, which then sends the results of the pipeline 1206 as output
  • This configuration is the current state of the art for transformation pipelines and is the most general form of these constructs.
  • Linear transformation pipelines require no special manipulation to simplify the data pathway and are thus referred to as non-decomposable.
  • the example depicted in this diagram was chosen to convey the configuration of a linear transformation pipeline and is the simplest form of the configuration felt to show the point It in no way implies limitation of the invention.
  • Fig. 13 is a process flow diagram of a method 1300 for one aspect of a transformation pipeline where one transformation node 1307 in a transformation pipeline receives data streams from two source transformation nodes 1301.
  • the invention handles this transformation pipeline configuration by decomposing or serializing the input events 1302-1303, 1304-1305 heavily relying on post transformation function continuation.
  • the results of individual transformation nodes 1302, 1304 just antecedent to the destination transformation node 1306 and placed into a single specialized data storage transformation node 1303, 1305 (shown twice as process occurs twice).
  • the combined results then retrieved from the data store 1306 and serve as the input stream for the transformation node within the transformation pipeline backbone 1307, 1308.
  • Fig. 14 is a process flow diagram of a method 1400 for one aspect of a transformation pipeline where one transformation node 1403 in a transformation pipeline receives input data from a transformation node 1402, and sends output data stream to two destination transformation nodes 1401, 1406, 1408 in potentially two separate transformation pipelines.
  • the invention handles this transformation pipeline configuration by decomposing or serializing the output events 1404,1405-1406, 1407-1408.
  • the results of the source transformation node 1403 just antecedent to the destination transformation nodes 1406 and placed into a single specialized data storage transformation node 1404, 1405, 1407 (shown three times as storage occurs and retrieval occurs twice).
  • the results of the antecedent transformation node may then be retrieved from a data store 1404 and serves as the input stream for the transformation nodes two downstream transformation pipeline 1406, 1408.
  • the example depicted in this diagram was chosen to convey the configuration of transformation pipelines with individual transformation nodes that send output streams to two destination nodes 1406, 1408 and is the simplest form of the configuration felt to show the point It in no way implies limitation of the invention.
  • One knowledgeable in the art will realize the great number of permutations and topologies possible, especially as the invention places no design restrictions on the number of transformation nodes sending output to greater than one destination or the number destinations receiving input from a source node.
  • Fig. 15 is a process flow diagram of a method 1500 for one aspect of a transformation pipeline where the topology of all or part of the pipeline is cyclical 1501.
  • the output stream of one transformation node 1504 acts as an input of an antecedent transformation node within the pipeline 1502 serialization or decomposition linearizes this cyclical configuration by completing the transformation of all of the nodes that make up a single cycle 1502, 1503, 1504 and then storing the result of that cycle in a data store 1505. That result of a cycle is then reintroduced to the transformation pipeline as input 1506 to the first transformation node of the cycle.
  • Fig. 16 is a process flow diagram of a method 1600 for one aspect of the batch data stream analysis pathway which forms part of the invention and allows streaming data to be interpreted with historic context
  • One or more streams of data from a plurality of sources which includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, is received by the system 1601.
  • the received stream may be filtered 1602 to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors.
  • Data formalization 1603 for batch analysis acts to reformat the stream data for optimal, reliable use during analysis. Reformatting might entail, but is not limited to: setting data field order, standardizing measurement units if choices are given, splitting complex information into multiple simpler fields, and stripping unwanted characters, again, just to name a few simple examples.
  • the filtered and formalized stream is then added to a distributed data store 1604 due to the vast amount of information accrued over time.
  • the invention has no dependency for specific data stores or data retrieval model.
  • data stored in the batch pathway store can be used to track changes in specifics of the data important to the ongoing analysis over time, repetitive data sets significant to the analysis or the occurrence of critical points of data 1605.
  • individual transformation nodes 620 may be saved and can be edited also all nodes of a transformation pipeline 600 keep a summary or summarized view (analogous to a network routing table) of applicable parts of the overall route of the pipeline along with detailed information pertaining to adjacent two nodes.
  • This framework information enables steps to be taken and notifications to be passed if individual transformation nodes 640 within a transformation pipeline 600 become unresponsive during analysis operations.
  • Combinations of results from the batch pathway, partial and streaming output results from the transformation pipeline, administrative directives from the authors of the analysis as well as operational status messages from components of the distributed computational graph are used to perform system sanity checks and retraining of one or more of the modules of the system 1606. These corrections are designed to occur without administrative intervention under all but the most extreme of circumstances with deep learning capabilities present as part of the system manager and retrain module 563 responsible for this task.
  • the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card.
  • ASIC application-specific integrated circuit
  • Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory.
  • a programmable network-resident machine which should be understood to include intermittently connected network-aware machines
  • Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols.
  • a general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented.
  • At least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end- user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof.
  • at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments).
  • FIG. 20 there is shown a block diagram depicting an exemplary computing device 10 suitable for implementing at least a portion of the features or functionalities disclosed herein.
  • Computing device 10 may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory.
  • Computing device 10 may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
  • communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
  • computing device 10 includes one or more central processing units (CPU) 12, one or more interfaces 15, and one or more busses 14 (such as a peripheral component interconnect (PCI) bus).
  • CPU 12 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine.
  • a computing device 10 may be configured or designed to function as a server system utilizing CPU 12, local memory 11 and/or remote memory 16, and interface(s) 15.
  • CPU 12 may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like.
  • CPU 12 may include one or more processors 13 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors.
  • processors 13 may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field- programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 10.
  • ASICs application-specific integrated circuits
  • EEPROMs electrically erasable programmable read-only memories
  • FPGAs field- programmable gate arrays
  • a local memory 11 such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory
  • RAM non-volatile random access memory
  • ROM read-only memory
  • Memory 11 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU 12 may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGONTM or SAMSUNG EXYNOSTM CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.
  • SOC system-on-a-chip
  • processor is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit
  • interfaces 15 are provided as network interface cards (NICs).
  • NICs control the sending and receiving of data packets over a computer network; other types of interfaces 15 may for example support other peripherals used with computing device 10.
  • interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like.
  • interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIRE WIRETM, THUNDERBOLTTM, PCI, parallel, radio frequency (RF), BLUETOOTHTM, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVT), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like.
  • USB universal serial bus
  • RF radio frequency
  • BLUETOOTHTM near-field communications
  • near-field communications e.g., using near-field magnetics
  • WiFi wireless FIRE WIRETM
  • PCI parallel
  • Such interfaces 15 may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM).
  • an independent processor such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces
  • volatile and/or non-volatile memory e.g., RAM
  • FIG. 20 illustrates one specific architecture for a computing device 10 for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of die features and techniques described herein may be implemented.
  • architectures having one or any number of processors 13 may be used, and such processors 13 may be present in a single device or distributed among any number of devices.
  • a single processor 13 handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided.
  • different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).
  • the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block 16 and local memory 11) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above).
  • Program instructions may control execution of or comprise an operating system and/or one or more applications, for example.
  • Memory 16 or memories 11, 16 may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.
  • At least some network device aspects may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein.
  • nontransitory machine- readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and "hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like.
  • ROM read-only memory
  • flash memory as is common in mobile devices and integrated systems
  • SSD solid state drives
  • hybrid SSD hybrid SSD
  • such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), "hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably.
  • program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVATM compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
  • interpreter for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language.
  • systems may be implemented on a standalone computing system.
  • Computing device 20 includes processors 21 that may run software that cany out one or more functions or applications of aspects, such as for example a client application 24.
  • Processors 21 may cany out computing instructions under control of an operating system 22 such as, for example, a version of MICROSOFT WINDOWSTM operating system, APPLE macOSTM or iOSTM operating systems, some variety of the Linux operating system, ANDROIDTM operating system, or the like.
  • an operating system 22 such as, for example, a version of MICROSOFT WINDOWSTM operating system, APPLE macOSTM or iOSTM operating systems, some variety of the Linux operating system, ANDROIDTM operating system, or the like.
  • one or more shared services 23 may be operable in system 20, and may be useful for providing common services to client applications 24.
  • Services 23 may for example be WINDOWSTM services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 21.
  • Input devices 28 may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof.
  • Output devices 27 may be of any type suitable for providing output to one or more users, whether remote or local to system 20, and may include for example one or more screens for visual output, speakers, printers, or any combination thereof.
  • Memory 25 may be random- access memory having any structure and architecture known in the art, for use by processors 21, for example to run software.
  • Storage devices 26 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to Fig. 20). Examples of storage devices 26 include flash memory, magnetic hard drive, CD-ROM, and/or the like.
  • systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers.
  • a distributed computing network such as one having any number of clients and/or servers.
  • FIG. 22 there is shown a block diagram depicting an exemplary architecture 30 for implementing at least a portion of a system according to one aspect on a distributed computing network.
  • any number of clients 33 may be provided.
  • Each client 33 may run software for implementing client- side portions of a system; clients may comprise a system 20 such as that illustrated in Rg. 21.
  • any number of servers 32 may be provided for handling requests received from one or more clients 33.
  • Clients 33 and servers 32 may communicate with one another via one or more electronic networks 31, which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other).
  • Networks 31 may be implemented using any known network protocols, including for example wired and/or wireless protocols.
  • servers 32 may call external services 37 when needed to obtain additional information, or to refer to additional data concerning a particular call.
  • external services 37 may take place, for example, via one or more networks 31.
  • external services 37 may comprise web-enabled services or functionality related to or installed on the hardware device itself.
  • client applications 24 may obtain information stored in a server system 32 in the cloud or on an external service 37 deployed on one or more of a particular enterprise's or user's premises.
  • clients 33 or servers 32 may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 31.
  • one or more databases 34 may be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases 34 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means.
  • one or more databases 34 may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as "NoSQL” (for example, HADOOP CASSANDRATM, GOOGLE BIGTABLETM, and so forth).
  • SQL structured query language
  • variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein.
  • database may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database” by those having ordinary skill in the art [083] Similarly, some aspects may make use of one or more security systems 36 and configuration systems 35. Security and configuration management are common information technology ( ⁇ ) and web functions, and some amount of each are generally associated with any ⁇ or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security 36 or configuration system 35 or approach is specifically required by the description of any specific aspect
  • Fig. 23 shows an exemplary overview of a computer system 40 as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data Various modifications and changes may be made to computer system 40 without departing from the broader scope of the system and method disclosed herein.
  • Central processor unit (CPU) 41 is connected to bus 42, to which bus is also connected memory 43, nonvolatile memory 44, display 47, inputybutput (J/O) unit 48, and network interface card (NIC) 53.
  • yO unit 48 may, typically, be connected to keyboard 49, pointing device 50, hard disk 52, and real-time clock 51.
  • NIC 53 connects to network 54, which may be the Internet or a local network, which local network may or may not have connections to the Internet
  • power supply unit 45 connected, in this example, to a main alternating current (AC) supply 46.
  • AC alternating current
  • functionality for implementing systems or methods of various aspects may be distributed among any number of client and/or server components.
  • various software modules may be implemented for performing various functions in connection with the system of any particular aspect, and such modules may be variously implemented to run on server and/or client components.

Abstract

La présente invention concerne un système pour l'analyse prédictive de très grands ensembles de données à l'aide d'un graphe de calcul distribué entraîné par un acteur, un orchestrateur de pipeline créant et gérant des pipelines de données individuels tout en fournissant une mise en cache de données pour permettre des interactions entre des acteurs d'activité spécifiques dans des pipelines. Chaque pipeline comprend ensuite un gestionnaire de pipeline qui crée et gère des acteurs d'activité individuels et dirige des opérations à l'intérieur du pipeline tout en envoyant des rapports à l'orchestrateur de pipeline.
PCT/US2018/036550 2017-06-07 2018-06-07 Analyse prédictive rapide de très grands ensembles de données utilisant un graphe de calcul distribué entraîné par un acteur WO2018227015A2 (fr)

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