Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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
  • G06F16/24—Querying
  • G06F16/245—Query processing
  • G06F16/2457—Query processing with adaptation to user needs
  • G06F16/24578—Query processing with adaptation to user needs using ranking
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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
  • G06F16/25—Integrating or interfacing systems involving database management systems
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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
  • G06F16/95—Retrieval from the web
  • G06F16/951—Indexing; Web crawling techniques

Definitions

• Multiple criteria decision analysis generally refers to an approach of making numerous and sometimes conflicting evaluations among multiple relations having various uncertainties.
• Such multiple criteria decision analysis has various applications.
• One example application is in finding travel deals through an interface from different sources.
• the interface is configured to access the different sources which have no direct access to each other.
• Some example sources may include travel search engines (e.g., kayak.com, fly.com, skyscanner.com) and hotel search engines (e.g., trivago.com, room77.com, google.com/hotelfinder).
• travel search engines e.g., kayak.com, fly.com, skyscanner.com
• hotel search engines e.g., trivago.com, room77.com, google.com/hotelfinder.
• the data at these sources typically are not maintained at one central repository but instead in different databases.
• Some embodiments of the present disclosure may generally relate to methods for selecting a preferred data set among distributed databases including a first database having a first probability attribute and a second database having a second probability attribute.
• One example method may include receiving a first data set selected from the first database and a second data set selected from the second database.
• the first data set includes a first monotonic attribute.
• the second data set includes a second monotonic attribute.
• the example method may further include selecting a candidate data set from one of the first data set and the second data set based on a comparison of the first monotonic attribute and the second monotonic attribute and determining whether the selected candidate data set is the preferred data set at least partly based on a minimum probability of the selected candidate data set being the preferred data set, a maximum probability of the selected candidate data set being the preferred data set and a threshold probability.
• Additional embodiments of the present disclosure may generally relate to computer- readable mediums containing instructions for selecting a preferred data set among distributed databases including a first database having a first probability attribute and a second database having a second probability attribute.
• One example computer-readable medium may contain instructions, which when executed by a computing device, causes the computing device to receive a first data set selected from the first database and a second data set selected from the second database.
• the first data set includes a first monotonic attribute.
• the second data set includes a second monotonic attribute.
• the example computer-readable medium may further containing additional instructions, which when executed by the computing device, causes the computing device to select a candidate data set from one of the first data set and the second data set based on a comparison of the first monotonic attribute and the second monotonic attribute and determine whether the selected candidate data set is the preferred data set at least partly based on a minimum probability of the selected candidate data set being the preferred data set, a maximum probability of the selected candidate data set being the preferred data set and a threshold probability.
• Other embodiments of the present disclosure may generally relate to a computing device configured to select a preferred data set among distributed databases including a first database having a first probability attribute and a second database having a second probability attribute.
• One example computing device includes a processing unit.
• the processing unit is configured to receive a first data set selected from the first database and a second data set selected from the second database.
• the first data set includes a first monotonic attribute.
• the second data set includes a second monotonic attribute.
• the processing unit is further configured to select a candidate data set from one of the first data set and the second data set based on a comparison of the first monotonic attribute and the second monotonic attribute and determine whether the selected candidate data set is the preferred data set at least partly based on a minimum probability of the selected candidate data set being the preferred data set, a maximum probability of the selected candidate data set being the preferred data set and a threshold probability.
• FIG. 1 is a flow chart of an illustrative embodiment of a method for selecting a preferred data set among distributed databases
• Fig. 2A illustrates a table for determining a maximum probability of a data set being the preferred data set
• Fig. 2B illustrates a table for determining a minimum probability of a data set being the preferred data set
• Fig. 3 shows a block diagram of an illustrative embodiment illustrating a computer program product that is arranged for selecting a preferred data set among distributed databases
• Fig. 4 shows a block diagram of an illustrative embodiment of a computing device that is arranged for selecting a preferred data set among distributed databases, all arranged in accordance with embodiments of the disclosure.
• This disclosure is drawn, inter alia, to methods, apparatus, computer programs, and systems of selecting a preferred data set among distributed databases.
• the term "skyline computation” generally refers to an approach to address a problem of multi-criteria decision making where there is no clear preference function over the attributes of different data sets. Instead, such computation attempts to get an overall picture of which objects dominate other objects based on one or more preset preferences.
• a first tuple "dominates" a second tuple if at least one attribute in the first tuple is preferred over the respective attribute in the second tuple, while the rest of the attributes in the first tuple are considered equal to or more preferred than the respective attributes in the second tuple.
• skyline probability generally refers to a probability that an object is part of a data set obtained as a result of a skyline computation.
• An "existence probability attribute” is an attribute in a relation.
• the "existence probability” generally refers to a probability associated with a tuple in the relation. It denotes the probability of whether the tuple exists or not.
• the multiple criteria decision analysis may include a skyline computation.
• a skyline computation has wide applications. For example, one application may involve selecting hotels that are cheap and close to the beach. After the skyline computation, hotels in the computed preferred data set are better than those not in the preferred set because they are superior in at least one attribute, cost or closeness to the beach, while being equivalent or better in all other attributes.
• Another example may include selecting a combination of a camera and a memory card in which the total price is the lowest, the image resolution of the camera is the highest, and the capacity of the memory card is the highest.
• the multiple criteria decision analysis may be more complicated when the existence of a tuple in a relation is uncertain and has an existence probability.
• the existence probability may play a role when a query includes a threshold probability.
• the probability of the tuple being a skyline may be the existence probability multiplied with the probability that none of other tuples dominate the tuple. In some embodiments, if a tuple has a skyline probability less than the threshold probability, the tuple may not be a part of the skyline.
• a data set may be a relation (or table) in a relational database and the preferred data set may be computed as a result set for a Structured Query Language (SQL) query.
• SQL Structured Query Language
• a data set may be a flat file.
• a data set may be a set of tuples in a non-relational database.
• a data set may be a set of tuples in a semi-structured data store.
• Fig. 1 is a flow chart of an illustrative embodiment of a method 100 for selecting a preferred data set among distributed databases, in accordance with some embodiments of the disclosure.
• the distributed databases may include a first database and a second database.
• the data sets stored in the first database and the data sets stored in the second database may not be maintained at a central repository.
• the first database and the second database may be configured to be accessed by a coordinator which is configured to receive a query.
• the preferred data set may be selected from the data sets stored in the first database and the second database in response to the query.
• the query may be associated with the skyline computation and may include a threshold probability.
• the coordinator may receive a first data set selected from the first database and a second data set selected from the second database.
• the first data set may include a first local skyline probability attribute and a first monotonic attribute.
• the second data set may include a second local skyline probability attribute and a second monotonic attribute.
• a local skyline probability generally refers to a probability that an object is part of a data set obtained as a result of a skyline computation carried out in a single database.
• the local skyline probability of a data set may be the existence probability of the data set if the data set is not dominated by other data sets in the same database.
• the local skyline probability of a data set may be the product of the existence probability of the data set and the non-existence probabilities of data sets in the same database that dominate the data set.
• the monotonic attribute may be computed using a monotonic function.
• Some example monotonic attributes include, but not limited to, Manhattan distance attribute, Euclidean distance attribute, etc.
• the data sets in the first database and the data sets in the second database may be stored in a technical feasible index structure, for example, the n-Ary tree index structure.
• An n-Ary tree is an n-dimensional generalization of a Quad tree.
• the first database may be configured to construct a local summary of its own datasets by performing a grid partitioning (uniform or non-uniform) of the entire data space.
• the non-uniform grid partitioning aware of the dense distribution of the preferred data set may provide a summary with a higher precision.
• the cell Assuming a set of data sets lying in a cell of the grid, the cell is configured to store the probability that none of the set of data sets lying in the cell exist. For empty cells of the grid, each of the empty cells is configured to store a value of 1.
• the local summaries of the first database and the second database may be transmitted to the coordinator.
• the coordinator may construct and maintain a global summary based on the local summary of the first database and the local summary of the second database. In some embodiments, the global summary may be updated when any of the local summaries of the databases is changed.
• the data sets in the same database may be arranged in an increasing order of their monotonic attributes from the origin.
• the first data set may have the minimum monotonic attribute among the data sets in the first database if the data sets have not been examined as the preferred data set.
• the second data set may have the minimum monotonic attribute among the data sets in the second database if such data sets have not been examined as the preferred data set.
• the coordinator may select a candidate data set from one of the first data set and the second data set. The selection may be based on a comparison of the first monotonic attribute and the second monotonic attribute.
• the candidate data set may have a monotonic attribute less than the monotonic attribute of the other data set.
• the coordinator may determine whether the selected candidate data set is the preferred data set. In some embodiments, the determination may be at least partly based on a minimum probability of the selected candidate data set being the preferred data set, a maximum probability of the selected candidate data set being the preferred data set and the threshold probability.
• the selected candidate data set when the maximum probability of the selected candidate data set being the preferred data set is less than the threshold probability, the selected candidate data set may not be the preferred data set. In some embodiments, when the minimum probability of the selected candidate data set being the preferred data set is greater than or equal to the threshold probability, the selected candidate data set may be the preferred data set.
• the preferred data set may be transmitted to the second database, with a feedback probability if the preferred data set is selected from the first database.
• the transmission of the preferred data set to a node other than the node that the preferred data set resides may decrease total processing time for examining all data sets stored in the first database and the second database for the preferred data set according to the query received by the coordinator.
• the selected candidate data set may be discarded and determined not to be the preferred data set if a score of the selected candidate is less than a threshold.
• the selected candidate data set may not be broadcasted between the first database and the second database to reduce the communication cost between the first database and the second database, but at the cost of the completeness of the preferred data set.
• the selected candidate data set when (1) the maximum probability of the selected candidate data set being the preferred data set is greater than or equal to the threshold probability and (2) the minimum probability of the selected candidate data set being the preferred data set is less than the threshold probability, the selected candidate data set may be transmitted to the second database if the selected candidate data set is selected from the first database. After receiving the selected candidate data set, the second database may be configured to calculate a domination probability that none of the tuples in the second database that dominate the selected candidate data set exist.
• the second database may transmit the domination probability of 0.28 back to the coordinator.
• the coordinator may then be configured to calculate a skyline probability of the selected candidate data set.
• the skyline probability of the selected candidate data set may be the product of 0.28 and (the probability of the selected candidate data set not dominated by other data sets in the first database).
• the selected candidate data set may not be the preferred data set. If the skyline probability of the selected candidate data set is equal to or greater than the threshold probability, the selected candidate data set may be the preferred data set.
• the coordinator may be configured to receive a third data set selected from the first database.
• the third data set may correspond to the next data set in the first database arranged according to the increasing order of the monotonic attributes in the first database.
• the method 100 may be iterated, where the first data set may be replaced with the third data set.
• Fig. 2A illustrates a table 200 for determining a maximum probability of a data set being the preferred data set, in accordance with some embodiments of the disclosure.
• five data sets i.e., t 1;1 , t li2 , t li3 , t 2 j and t 2i2
• Three data sets i.e., t lsl , ti,2, t li3
• the other two data sets i.e., t 2ili t 2i2
• any of the data sets includes its own existence probability, for example, the existence probabilities of tij, t li2 , t li3 , t 2 j and t 2i2 may be 0.2, 0.2, 0.7, 0.8 and 0.9, respectively.
• a cell is configured to store the probability that none of the set of data sets lying in the cell exist. Therefore, cells 201, 203, 205, 207 and 209 may store the values of (1-0.2), (1-0.2), (1-0.7), (1-0.8) and (1-0.9), respectively.
• a domination region for a data set may correspond to the largest region of space such that any point lying inside the region dominates the data set.
• the domination region for t 2 , 2 may be approximated using the uniform grid partitioning as an orthogonal range space shown as the dotted box in Table 200.
• Fig. 2B illustrates a table 210 for determining a minimum probability of a data set being the preferred data set, in accordance with some embodiments of the disclosure.
• five data sets i.e., t 1;1 , ti i2 , ti i3 , t 2 j and t 2i2
• table 210 Three data sets (i.e., t lsl , ti i2 , ti i3 ) are stored in the first database and the other two data sets (i.e., t 2ili t 2i2 ) are stored in the second database.
• Any of the data sets may include its own existence probability, for example, the existence probabilities of tij, t li2 , t li3 , t 2 j and t 2i2 may be 0.2, 0.2, 0.7, 0.8 and 0.9, respectively.
• a cell may be configured to store the probability that none of the set of data sets lying in the cell exist. Therefore, cells 211, 213, 215, 217 and 219 may store the value of (1-0.2), (1-0.2), (1-0.7), (1-0.8) and (1-0.9), respectively.
• a complement domination region for a data set may correspond to the smallest region of space such that any point lying outside the region cannot dominate the data set.
• the complement domination region for t 2 2 may be approximated using the uniform grid partitioning as a complement orthogonal range space as the dashed-double-dot region shown in Table 210.
• Fig. 3 shows a block diagram illustrating a computer program product that is arranged for selecting a preferred data set among distributed databases.
• the computer program product 300 may include a signal bearing medium 304, which may include one or more sets of executable instructions 302 that, when executed by, for example, a processor of a computing device, may provide at least the functionality described above and illustrated in Fig. 1.
• the signal bearing medium 304 may encompass a non- transitory computer readable medium 308, such as, but not limited to, a hard disk drive, a Compact Disc (CD), a Digital Versatile Disk (DVD), a digital tape, memory, etc.
• the signal bearing medium 304 may encompass a recordable medium 310, such as, but not limited to, memory, read/write (R/W) CDs, R/W DVDs, etc.
• the signal bearing medium 304 may encompass a communications medium 306, such as, but not limited to, a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.)
• the computer program product 300 may also be recorded in the non-transitory computer readable medium 308 or another similar recordable medium 310.
• FIG. 4 shows a block diagram of an illustrative embodiment of a computing device that is arranged for selecting a preferred data set among distributed databases.
• computing device 400 typically includes one or more processors 410 and a system memory 420.
• a memory bus 430 may be used for communicating between processor 410 and system memory 420.
• processor 410 may be of any type including but not limited to a microprocessor ( ⁇ ), a microcontroller ( ⁇ ), a digital signal processor (DSP), or any combination thereof.
• Processor 410 may include one more levels of caching, such as a level one cache 411 and a level two cache 412, a processor core 413, and registers 414.
• An example processor core 413 may include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP Core), or any combination thereof.
• An example memory controller 415 may also be used with processor 410, or in some implementations memory controller 415 may be an internal part of processor 410.
• system memory 420 may be of any type including but not limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof.
• System memory 420 may include an operating system 421, one or more applications 422, and program data 424.
• application 422 may include a data selection algorithm 423 that is arranged to perform the functions as described herein including those described with respect to the steps 101 to 105 of the method 100 of Fig. 1.
• Program data 424 may include data sets 425 that may be useful for the operation of data selection algorithm 423 as will be further described below.
• the data sets 425 may include, without limitation, the first data set and the second data set of block 101 of Fig. 1.
• application 422 may be arranged to operate with program data 424 on operating system 421, such that implementations of selecting preferred data set among distributed databases may be provided as described herein.
• This described basic configuration 401 is illustrated in Fig. 4 by those components within the inner dashed line.
• application 422 may include data selection algorithm 423 that is arranged to perform the functions as described herein including those described with respect to the steps 101 to 105 of the method 100 of Fig. 1.
• Computing device 400 may have additional features or functionality, and additional interfaces to facilitate communications between basic configuration 401 and any required devices and interfaces.
• a bus/interface controller 440 may be used to facilitate communications between basic configuration 401 and one or more data storage devices 450 via a storage interface bus 441.
• Data storage devices 450 may be removable storage devices 451, non- removable storage devices 452, or a combination thereof. Examples of removable storage and non-removable storage devices include magnetic disk devices such as flexible disk drives and hard-disk drives (HDD), optical disk drives such as CD drives or DVD drives, solid state drives (SSD), and tape drives to name a few.
• HDD hard-disk drives
• SSD solid state drives
• System memory 420, removable storage devices 451 and non-removable storage devices 452 are examples of computer storage media.
• Example computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Any such computer storage media may be used to store the desired information and which may be accessed by computing device 400 and may be part of computing device 400.
• Computing device 400 may also include an interface bus 442 for facilitating communication from various interface devices (e.g., output devices 460, peripheral interfaces 470, and communication devices 480) to basic configuration 401 via bus/interface controller 440.
• Example output devices 460 include a graphics processing unit 461 and an audio processing unit 462, which may be configured to communicate to various external devices such as a display or speakers via one or more A/V ports 463.
• Example peripheral interfaces 470 include a serial interface controller 471 or a parallel interface controller 472, which may be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, scanner, etc.) via one or more I/O ports 473.
• An example communication device 480 includes a network controller 481, which may be arranged to facilitate communications with one or more other computing devices 490 over a network communication link via one or more communication ports 482.
• the other computing devices 490 may include other applications, which may be operated based on the results of the application 422.
• the network communication link may be one example of a communication media.
• Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media.
• a "modulated data signal" may be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
• communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), microwave, infrared (IR) and other wireless media.
• RF radio frequency
• IR infrared
• the term computer readable media as used herein may include both storage media and communication media.
• Computing device 400 may be implemented as a portion of a small-form factor portable (or mobile) electronic device such as a cell phone, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions.
• a small-form factor portable (or mobile) electronic device such as a cell phone, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions.
• PDA personal data assistant
• Computing device 400 may also be implemented as a personal computer including both laptop computer and non- laptop computer configurations.
• the implementer may opt for a mainly hardware and/or firmware vehicle; if flexibility is paramount, the implementer may opt for a mainly software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware.
• a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices, such as a touch pad or screen, and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities).
• a typical data processing system may be implemented utilizing any suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.
• any two components so associated can also be viewed as being “operably connected”, or “operably coupled”, to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable”, to each other to achieve the desired functionality.
• operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

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