Patent Grant
10127303
The present disclosure relates to the field of computers, and specifically to the use of computers in managing data. Still more particularly, the present disclosure relates to sorting and categorizing data.
Data are values of variables, which typically belong to a set of items. Examples of data include numbers and characters, which may describe a quantity or quality of a subject. Other data can be processed to generate a picture or other depiction of the subject. Data management is the development and execution of architectures, policies, practices and procedures that manage the data lifecycle needs of an enterprise. Examples of data management include storing data in a manner that allows for efficient future data retrieval of the stored data.
A processor-implemented method, system, and/or computer program product measures and displays facets in context-based conformed dimensional data gravity wells. One or more processors receive a data stream of non-contextual data objects, where each of the non-contextual data objects ambiguously relates to multiple subject-matters. One or more processors associate one of the non-contextual data objects with a context object to define a synthetic context-based object, where the context object is a first facet that provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the one of the non-contextual data objects. One or more processors parse the synthetic context-based object into a context-based n-tuple, where the context-based n-tuple includes a pointer to the one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, and a weighting factor of importance of the synthetic context-based object. One or more processors create multiple context-based conformed dimensional data gravity well frameworks on a context-based conformed dimensional data gravity wells membrane, where each of the multiple context-based conformed dimensional data gravity well frameworks includes at least one non-contextual data object and at least one context object, and where the context-based conformed dimensional data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based conformed dimensional data gravity wells. One or more processors calculate a virtual mass of a parsed synthetic context-based object, where the virtual mass of the parsed synthetic context-based object is derived from a formula of Pc(C)×Wtc(S), where Pc(C) is a probability that the non-contextual data object has been associated with a correct context object, and where Wtc(S) is the weighting factor of importance of the synthetic context-based object. One or more processors transmit multiple parsed synthetic context-based objects to the context-based conformed dimensional data gravity wells membrane. One or more processors populate each of the multiple context-based conformed dimensional data gravity well frameworks with the multiple parsed synthetic context-based objects to define multiple context-based conformed dimensional data gravity wells according to the virtual mass of multiple parsed synthetic context-based objects that are pulled into each of the context-based conformed dimensional data gravity well frameworks, where each of the multiple parsed synthetic context-based objects is pulled into a particular context-based conformed dimensional data gravity well in response to values from its n-tuple matching said at least one context object in said particular context-based conformed dimensional data gravity well. One or more processors store the particular context-based conformed dimensional data gravity well for use with future retrieval of each of the multiple parsed synthetic context-based objects.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
In one embodiment, instructions are stored on a computer readable storage device (e.g., a CD-ROM), which does not include propagation media.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
With reference now to the figures, and in particular to
Exemplary computer 102 includes a processor 104 that is coupled to a system bus 106. Processor 104 may utilize one or more processors, each of which has one or more processor cores. A video adapter 108, which drives/supports a display 110, is also coupled to system bus 106. System bus 106 is coupled via a bus bridge 112 to an input/output (I/O) bus 114. An I/O interface 116 is coupled to I/O bus 114. I/O interface 116 affords communication with various I/O devices, including a keyboard 118, a mouse 120, a media tray 122 (which may include storage devices such as CD-ROM drives, multi-media interfaces, etc.), a printer 124, and external USB port(s) 126. While the format of the ports connected to I/O interface 116 may be any known to those skilled in the art of computer architecture, in one embodiment some or all of these ports are universal serial bus (USB) ports.
As depicted, computer 102 is able to communicate with a software deploying server 150, using a network interface 130. Network interface 130 is a hardware network interface, such as a network interface card (NIC), etc. Network 128 may be an external network such as the Internet, or an internal network such as an Ethernet or a virtual private network (VPN).
A hard drive interface 132 is also coupled to system bus 106. Hard drive interface 132 interfaces with a hard drive 134. In one embodiment, hard drive 134 populates a system memory 136, which is also coupled to system bus 106. System memory is defined as a lowest level of volatile memory in computer 102. This volatile memory includes additional higher levels of volatile memory (not shown), including, but not limited to, cache memory, registers and buffers. Data that populates system memory 136 includes computer 102's operating system (OS) 138 and application programs 144.
OS 138 includes a shell 140, for providing transparent user access to resources such as application programs 144. Generally, shell 140 is a program that provides an interpreter and an interface between the user and the operating system. More specifically, shell 140 executes commands that are entered into a command line user interface or from a file. Thus, shell 140, also called a command processor, is generally the highest level of the operating system software hierarchy and serves as a command interpreter. The shell provides a system prompt, interprets commands entered by keyboard, mouse, or other user input media, and sends the interpreted command(s) to the appropriate lower levels of the operating system (e.g., a kernel 142) for processing. Note that while shell 140 is a text-based, line-oriented user interface, the present invention will equally well support other user interface modes, such as graphical, voice, gestural, etc.
As depicted, OS 138 also includes kernel 142, which includes lower levels of functionality for OS 138, including providing essential services required by other parts of OS 138 and application programs 144, including memory management, process and task management, disk management, and mouse and keyboard management.
Application programs 144 include a renderer, shown in exemplary manner as a browser 146. Browser 146 includes program modules and instructions enabling a world wide web (WWW) client (i.e., computer 102) to send and receive network messages to the Internet using hypertext transfer protocol (HTTP) messaging, thus enabling communication with software deploying server 150 and other computer systems.
Application programs 144 in computer 102's system memory (as well as software deploying server 150's system memory) also include a context-based conformed dimensional data gravity well mapping logic (CBCDDGWML) 148. CBCDDGWML 148 includes code for implementing the processes described below, including those described in
Note that the hardware elements depicted in computer 102 are not intended to be exhaustive, but rather are representative to highlight essential components required by the present invention. For instance, computer 102 may include alternate memory storage devices such as magnetic cassettes, digital versatile disks (DVDs), Bernoulli cartridges, and the like. These and other variations are intended to be within the spirit and scope of the present invention.
With reference now to
Within system 200 is a synthetic context-based object database 202, which contains multiple synthetic context-based objects 204a-204n (thus indicating an “n” quantity of objects, where “n” is an integer). Each of the synthetic context-based objects 204a-204n is defined by at least one non-contextual data object and at least one context object. That is, at least one non-contextual data object is associated with at least one context object to define one or more of the synthetic context-based objects 204a-204n. The non-contextual data object ambiguously relates to multiple subject-matters, and the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object.
Note that the non-contextual data objects contain data that has no meaning in and of itself. That is, the data in the context objects are not merely attributes or descriptors of the data/objects described by the non-contextual data objects. Rather, the context objects provide additional information about the non-contextual data objects in order to give these non-contextual data objects meaning. Thus, the context objects do not merely describe something, but rather they define what something is. Without the context objects, the non-contextual data objects contain data that is meaningless; with the context objects, the non-contextual data objects become meaningful.
For example, assume that a non-contextual data object database 206 includes multiple non-contextual data objects 208r-208t (thus indicating a “t” quantity of objects, where “t” is an integer). However, data within each of these non-contextual data objects 208r-208t by itself is ambiguous, since it has no context. That is, the data within each of the non-contextual data objects 208r-208t is data that, standing alone, has no meaning, and thus is ambiguous with regards to its subject-matter. In order to give the data within each of the non-contextual data objects 208r-208t meaning, they are given context, which is provided by data contained within one or more of the context objects 210x-210z (thus indicating a “z” quantity of objects, where “z” is an integer) stored within a context object database 212. For example, if a pointer 214a points the non-contextual data object 208r to the synthetic context-based object 204a, while a pointer 216a points the context object 210x to the synthetic context-based object 204a, thus associating the non-contextual data object 208r and the context object 210x with the synthetic context-based object 204a (e.g., storing or otherwise associating the data within the non-contextual data object 208r and the context object 210x in the synthetic context-based object 204a), the data within the non-contextual data object 208r now has been given unambiguous meaning by the data within the context object 210x. This contextual meaning is thus stored within (or otherwise associated with) the synthetic context-based object 204a.
Similarly, if a pointer 214b associates data within the non-contextual data object 208s with the synthetic context-based object 204b, while the pointer 216c associates data within the context object 210z with the synthetic context-based object 204b, then the data within the non-contextual data object 208s is now given meaning by the data in the context object 210z. This contextual meaning is thus stored within (or otherwise associated with) the synthetic context-based object 204b.
Note that more than one context object can give meaning to a particular non-contextual data object. For example, both context object 210x and context object 210y can point to the synthetic context-based object 204a, thus providing compound context meaning to the non-contextual data object 208r shown in
Note also that while the pointers 214a-214b and 216a-216c are logically shown pointing toward one or more of the synthetic context-based objects 204a-204n, in one embodiment the synthetic context-based objects 204a-204n actually point to the non-contextual data objects 208r-208t and the context objects 210x-210z. That is, in one embodiment the synthetic context-based objects 204a-204n locate the non-contextual data objects 208r-208t and the context objects 210x-210z through the use of the pointers 214a-214b and 216a-216c.
Consider now an exemplary case depicted in
In the example shown in
In order to give contextual meaning to the word “purchase” (i.e., define the term “purchase”) in the context of “land”, context object 310x, which contains the context datum “land”, is associated with (e.g., stored in or associated by a look-up table, etc.) the synthetic context-based object 304a. Associated with the synthetic context-based object 304b is a context object 310y, which provides the context/datum of “clothes” to the term “purchase” provided by the non-contextual data object 308r. Thus, the synthetic context-based object 304b defines “purchase” as that which is related to the subject-matter “clothing receipt”, including electronic, e-mail, and paper evidence of a clothing sale. Associated with the synthetic context-based object 304n is a context object 310z, which provides the context/datum of “air travel” to the term “purchase” provided by the non-contextual data object 308r. Thus, the synthetic context-based object 304n defines “purchase” as that which is related to the subject-matter “airline ticket”, including electronic, e-mail, and paper evidence of a person's right to board a particular airline flight.
In one embodiment, the data within a non-contextual data object is even more meaningless if it is merely a combination of numbers and/or letters. For example, consider the scenario in which data “10” were to be contained within a non-contextual data object 308r depicted in
With reference now to
Within system 400 is a conformed dimensional object database 402, which contains multiple conformed dimensional objects 404a-404n (thus indicating an “n” quantity of objects, where “n” is an integer). Each of the conformed dimensional objects 404a-404n is defined by at least one non-dimensional data object and at least one dimension object. That is, at least one non-dimensional data object is associated with at least one dimension object to define one or more of the conformed dimensional objects 404a-404n. The non-dimensional data object is merely a value/number, and has no dimensions (e.g., meters, product units, kilograms, etc.), and the dimension object provides a measurement scale to the non-dimensional data object.
For example, assume that a non-dimensional data object database 406 includes multiple non-dimensional data objects 408r-408t (thus indicating a “t” quantity of objects, where “t” is an integer). However, data within each of these non-dimensional data objects 408r-408t by itself is meaningless, since it has no dimensions. That is, the data within each of the non-dimensional data objects 408r-408t is data that, standing alone, has no meaning, since it could be describing a number of inches, a number of feet, a number of meters, etc. (i.e., it is dimensional-less). In order to give the data within each of the non-dimensional data objects 408r-408t dimensional meaning, they are given dimension, which is provided by data contained within one or more of the dimension objects 410x-410z (thus indicating a “z” quantity of objects, where “z” is an integer) stored within a dimension object database 412. For example, if a pointer 414a points the non-dimensional data object 408r to the conformed dimensional object 404a, while a pointer 416a points the dimension object 410x to the conformed dimensional object 404a, thus associating the non-dimensional data object 408r and the dimension object 410x with the conformed dimensional object 404a (e.g., storing or otherwise associating the data within the non-dimensional data object 408r and the dimension object 410x in the conformed dimensional object 404a), the data within the non-dimensional data object 408r now has been given a label/dimension. This dimensional label/meaning is thus stored within (or otherwise associated with) the conformed dimensional object 404a.
Similarly, if a pointer 414b associates data within the non-dimensional data object 408s with the conformed dimensional object 404b, while the pointer 416c associates data within the dimension object 410z with the conformed dimensional object 404b, then the data within the non-dimensional data object 408s is now given a dimension/label by the data in the dimension object 410z. This dimensional meaning is thus stored within (or otherwise associated with) the conformed dimensional object 404b.
Note that more than one dimension object can give meaning to a particular non-dimensional data object. For example, both dimension object 410x and dimension object 410y can point to the conformed dimensional object 404a, thus providing compound dimensional meaning to the non-dimensional data object 408r shown in
Note also that while the pointers 414a-414b and 416a-416c are logically shown pointing toward one or more of the conformed dimensional objects 404a-404n, in one embodiment the conformed dimensional objects 404a-404n actually point to the non-dimensional data objects 408r-408t and the dimension objects 410x-410z. That is, in one embodiment the conformed dimensional objects 404a-404n locate the non-dimensional data objects 408r-408t and the dimension objects 410x-410z through the use of the pointers 414a-414b and 416a-416c.
With reference now to
In order to properly utilize the synthetic context-based objects 506, a synthetic context-based object parsing logic 508 parses the synthetic context-based objects 506 into parsed synthetic context-based objects 510. These parsed synthetic context-based objects 510 make up an n-tuple (i.e., an ordered list of “n” descriptive elements (where “n” is an integer)) that describe each of the synthetic context-based objects 506. In one embodiment, this n-tuple includes a pointer (e.g., a locating pathway) to the non-contextual data object in the synthetic context-based object. This pointer may be to a storage location (e.g., a universal resource locator (URL) address at which the non-contextual data object is stored), such that the synthetic context-based objects 506 must be generated, or the pointer may be local (such that the synthetic context-based objects 506 exist locally as part of a streaming data packet, etc.). In one embodiment, the n-tuple also includes a probability value that a non-contextual data object has been associated with a correct context object. That is, a correct context object may or may not be associated with a particular non-contextual data object. For example, the non-contextual data object may be incomplete (i.e., a fragment, a corrupted version, etc.) version of the actual non-contextual data.
As such, a “guess” must be made to determine which context data should be associated with that corrupted non-contextual data. In this example, assume that the corrupted non-contextual data object contains the value “3.13”, and that the data is related to areas of circles. If the value of the non-contextual data object had been “3.14159”, then there is a high probability (e.g., is predetermined to have a 99% probability) that the context of this data object is the ratio of a circle's area divided by that circle's radius-squared (i.e., is “pi”). However, a predetermination may be made, based on probability calculations such as those using a Bayesian probability formula, that the likelihood of “3.13” being the ratio of a circle's area divided by that circle's radius-squared is only 85%.
In one embodiment, one of the parameters/values from the n-tuple is a weighting factor of importance of the synthetic context-based object. In one embodiment, this weighting factor is how “important” this particular synthetic context-based object is to an enterprise's project. For example, assume that an enterprise project is to track sales of a particular product at a particular store. If the synthetic context-based object contains information regarding an average sale price of units of a particular product sold at this particular store during a particular time period, then this synthetic context-based object is given (either manually or by an algorithm) an “importance” rating of 95 out of 100. However, if the synthetic context-based object describes whether the items are “red” or “blue” in color, such information is deemed less important (e.g., is given an “importance” rating of 30 out of 100). Note that an algorithm to determine (and/or predetermine) these importance ratings can utilize flags, metadata, etc. to determine the importance of the synthetic context-based objects. For example, assume that a particular synthetic context-based object has a flag indicating that it describes an average price for products sold at a particular store on a particular day. Assume further that a software program for an enterprise project to track such products also has this flag. Thus, if the two flags match, then a high level of importance (e.g., over 95 on a scale of 100) is assigned to synthetic context-based objects that have this flag.
The parsed synthetic context-based objects 510 are then sent to a context-based conformed dimensional data gravity wells membrane 512. The context-based conformed dimensional data gravity wells membrane 512 is a virtual mathematical membrane that is capable of supporting multiple context-based conformed dimensional data gravity wells. That is, the context-based conformed dimensional data gravity wells membrane 512 is a mathematical framework that is part of a program such as CBCDDGWL 148 shown in
With reference now to
In order to properly utilize the conformed dimensional objects 606, a conformed dimensional object parsing logic 608 parses the conformed dimensional objects 606 into parsed conformed dimensional objects 610. These parsed conformed dimensional objects 610 make up an n-tuple (i.e., an ordered list of “n” descriptive elements (where “n” is an integer)) that describe each of the conformed dimensional objects 606. In one embodiment, this n-tuple includes a pointer (e.g., a locating pathway) to the non-dimensional data object in the conformed dimensional object. This pointer may be to a storage location (e.g., a universal resource locator (URL) address at which the non-dimensional data object is stored), such that the conformed dimensional objects 606 must be generated, or the pointer may be local (such that the conformed dimensional objects 606 exist locally as part of a streaming data packet, etc.). In one embodiment, the n-tuple also includes a probability value that a non-dimensional data object has been associated with a correct dimension object. That is, a correct dimension object may or may not be associated with a particular non-dimensional data object. For example, the non-dimensional data object may be incomplete (i.e., a fragment, a corrupted version, etc.) version of the actual non-dimensional data. As such, a “guess” must be made to determine which dimension data should be associated with that corrupted non-dimensional data. In this example, assume that the corrupted non-dimensional data object contains the value “3.13”, and that the data is related to areas of circles. If the value of the non-dimensional data object had been “3.14159”, then there is a high probability (e.g., is predetermined to have a 99% probability) that this data object is the ratio of a circle's area divided by that circle's radius-squared (i.e., is “pi”). However, a predetermination may be made, based on probability calculations such as those using a Bayesian probability formula, that the likelihood of “3.13” being the ratio of a circle's area divided by that circle's radius-squared is only 85%.
In one embodiment, one of the parameters/values from the n-tuple is a weighting factor of importance of the conformed dimensional object. In one embodiment, this weighting factor is how “important” this particular conformed dimensional object is to an enterprise's project. For example, assume that an enterprise project is to track sales of a particular product at a particular store. If the conformed dimensional object contains information regarding how many units of this particular product have been sold at this store during a particular time period, then this conformed dimensional object is given (either manually or by an algorithm) an “importance” rating of 95 out of 100. However, if the conformed dimensional object describes whether the items are being paid for with cash or credit cards, such information is deemed less important (e.g., is given an “importance” rating of 30 out of 100). Note that an algorithm to determine (and/or predetermine) these importance ratings can utilize flags, metadata, etc. to determine the importance of the conformed dimensional objects. For example, assume that a particular conformed dimensional object has a flag indicating that it describes a quantity of products sold at a particular store on a particular day. Assume further that a software program for an enterprise project to track such products also has this flag. Thus, if the two flags match, then a high level of importance (e.g., over 95 on a scale of 100) is assigned to conformed dimensional objects that have this flag.
The parsed conformed dimensional objects 610 are then sent to the context-based conformed dimensional data gravity wells membrane 512, which is described above as depicted in
Note that context-based conformed dimensional data gravity well framework 702 is not yet populated with any parsed objects, and thus is not yet a context-based conformed dimensional data gravity well. However, context-based conformed dimensional data gravity well framework 706 is populated with parsed objects 708, which are synthetic context-based objects and/or conformed dimensional objects, and thus has been transformed into a context-based conformed dimensional data gravity well 710. This transformation occurred when context-based conformed dimensional data gravity well framework 706, which contains (i.e., logically includes and/or points to) a non-contextual data object 704b, a context object 712b, and a dimension object 713b, one or more of which is part of each of the captured parsed objects 708 was populated with one or more parsed objects. As stated above, each of the captured parsed objects 708 may be either a synthetic context-based object or a conformed dimensional object.
Note that parsed objects 701 are streaming in real-time from a data source across the context-based conformed dimensional data gravity wells membrane 512. If a particular parsed object is never pulled into any of the context-based conformed dimensional data gravity wells on the context-based conformed dimensional data gravity wells membrane 512, then that particular parsed object simply continues to stream to another destination, and does not affect the size and/or location of any of the context-based conformed dimensional data gravity wells.
Consider now context-based conformed dimensional data gravity well 716. Note that context-based conformed dimensional data gravity well 716 includes two dimension objects 713c-713d as well as two context objects 712c-712d and a non-contextual data object 704c. The presence of dimension objects 713c-713d and context objects 712c-712d (which in one embodiment are graphically depicted on the walls of the context-based conformed dimensional data gravity well 716) causes objects such as parsed object 714b (which in one embodiment contains both dimension objects 713c and 713d and/or both context objects 712c-712d and/or non-contextual data object 704c) to be pulled into context-based conformed dimensional data gravity well 716. Note further that context-based conformed dimensional data gravity well 716 is depicted as being larger than context-based conformed dimensional data gravity well 710 or context-based conformed dimensional data gravity well 720, since there are more objects (parsed objects 718) in context-based conformed dimensional data gravity well 716 than there are in these other context-based conformed dimensional data gravity wells. That is, it is the quantity of objects that have been pulled into a particular context-based conformed dimensional data gravity well that determines the size and shape of that particular context-based conformed dimensional data gravity well. The fact that context-based conformed dimensional data gravity well 716 has two dimension objects 712c-712d and two context objects 712c-712d, while context-based conformed dimensional data gravity wells 710/720 have only one dimension object 713b/713e and one context object 712b/712e, has no bearing on the size of the context-based conformed dimensional data gravity wells 710/720. Rather, the size and shape of the context-based conformed dimensional data gravity wells 710/716/720 in this embodiment is based solely on the quantity of parsed objects that are pulled in.
Note that, in one embodiment, the context-based conformed dimensional data gravity wells depicted in
In one embodiment, the context-based conformed dimensional data gravity well frameworks and/or context-based conformed dimensional data gravity wells described in
With reference now to
As depicted in block 808, the processor parses each of the streaming objects into an n-tuple. For the conformed dimensional objects, each n-tuple comprises a pointer to one of the non-dimensional data objects, a probability that a non-dimensional data object has been associated with a correct dimension object, and a weighting factor of importance of the conformed dimensional object. For the synthetic context-based objects, each n-tuple comprises a pointer to one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, and a weighting factor of importance of the synthetic context-based object. In one embodiment, the n-tuple also includes a probability that a particular object is uncorrupted. For example, if it is determined that a particular object is a fragment of an original object (e.g., by comparing the length, format, and other features of that object with known lengths, formats, and other features of data/objects coming from the same data/object source as that particular object), then a probability can be assessed as to whether that particular object is corrupted. For example, if a particular data object from “Source A” is 32 characters long, while a typical (e.g., 90% of the data objects from Source A) data object from Source A is 30 characters long, then it is likely (e.g., 80% probable) that this particular data object has been corrupted with additional data. Similarly, if a particular data object from Source A is 22 characters long, while a typical (e.g., 99% of the data objects from Source A) data object from Source A is 30 characters long, then it is likely (e.g., 99% probable) that this particular data object has been corrupted by having certain bits truncated/removed.
With respect to block 810, the processor calculates a virtual mass of each of the parsed objects. In one embodiment, the virtual mass of the parsed object is derived by calculating the virtual mass of a parsed synthetic context-based object by using the formula Pc(C)×Wtc(S), where Pc(C) is the probability that the non-contextual data object has been associated with the correct context object, and where Wtc(S) is the weighting factor of importance of the synthetic context-based object; and by calculating the virtual mass of a parsed conformed dimensional object by using the formula Pd(C)×Wtd(S), where Pd(C) is the probability that 1) said one of the non-dimensional data objects has been associated with the correct dimensional label, 2) said one of the non-dimensional data objects is uncorrupted, and 3) said one of the non-dimensional data objects has come from a data source whose data has been predetermined to be appropriate for storage in a particular context-based conformed dimensional data gravity well; and where Wtd(S) is the weighting factor of importance of the conformed dimensional object. The probabilities of 1) and 2) occurring are discussed above. The probability of 3) occurring can be predetermined by assigning one or more flags or other markers to each of the context-based conformed dimensional data gravity wells. For example, assume that these flags/markers identify five characteristics (e.g., length of the data, format of the data, time/date of when the data is generated, how frequently identical data is generated, and a source type (e.g., point of sales stations, stored databases, websites, etc.) of the data) of data that will be accepted in a particular context-based conformed dimensional data gravity well. If a certain non-dimensional data object has four of these flags/markers (e.g., as part of its n-tuple), then there may be a 90% probability that this non-dimensional data object is appropriate for storage within the particular context-based conformed dimensional data gravity well that has the five flags/markers. However, if a certain non-dimensional data object has only three of these flags/markers (e.g., as part of its n-tuple), then there may be only a 50% probability that this non-dimensional data object is appropriate for storage within that same particular context-based conformed dimensional data gravity well.
Continuing with the overall formula P(C)×Wt(S), Wt(S) is the weighting factor of importance of the conformed dimensional object. As described herein, in one embodiment the weighting factor of importance of the conformed dimensional object is based on how important the conformed dimensional object is to a particular project.
As described in block 812, the processor creates multiple context-based conformed dimensional data gravity well frameworks on a context-based conformed dimensional data gravity wells membrane. Each of the multiple context-based conformed dimensional data gravity well frameworks comprises at least one dimension object, at least one non-contextual data object, and at least one context object. As described herein, the context-based conformed dimensional data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based conformed dimensional data gravity wells.
As described in block 814, multiple parsed objects are then transmitted to the context-based conformed dimensional data gravity wells membrane. That is, these parsed objects are then transmitted to an algorithmic environment in which the logic-enabled context-based conformed dimensional data gravity well frameworks exist. These context-based conformed dimensional data gravity well frameworks are algorithmically generated based on their ability to attract specific objects. As described in block 816, this pulling/attraction enables the processor to define multiple context-based conformed dimensional data gravity wells according to the virtual mass of multiple parsed objects that are pulled into each of the context-based conformed dimensional data gravity well frameworks. As described herein, each of the multiple parsed objects is pulled into a particular context-based conformed dimensional data gravity well in response to values from its n-tuple matching at least one dimensional object, at least one non-contextual data object, at least one context object, and/or other probability factors described herein, that is part of the particular context-based conformed dimensional data gravity well.
In one embodiment, the generated context-based conformed dimensional data gravity wells are presented on a display according to a combined virtual mass of the multiple parsed objects that populate each context-based conformed dimensional data gravity well. That is, a first context-based conformed dimensional data gravity well that holds a more virtually massive combination of parsed objects than a second context-based conformed dimensional data gravity well will be larger, and thus is visually depicted on a display as extending farther away from the context-based conformed dimensional data gravity wells membrane than the second context-based conformed dimensional data gravity well.
In one embodiment, the construction of the context-based conformed dimensional data gravity wells is temporally dynamic. That is, in this embodiment, the processor determines an age (i.e., how “stale” or “old”) each of the multiple parsed objects that have been pulled into the particular context-based conformed dimensional data gravity well is. Based on the age of each of these objects, the processor removes, from the particular context-based conformed dimensional data gravity well that holds a stale object, any parsed object that is older than a predetermined age.
In one embodiment, a likelihood that a particular object is pulled into an appropriate context-based conformed dimensional data gravity well is performed using a Bayesian probability formula. That is, an appropriate context-based conformed dimensional data gravity well is defined as a context-based conformed dimensional data gravity well whose framework includes at least one non-contextual data object, at least one context object, and/or at least one dimension object found in a parsed object that is pulled into that particular (appropriate) context-based conformed dimensional data gravity well.
For example, in order to determine a likelihood that a particular object is pulled into an appropriate context-based conformed dimensional data gravity well, assume that A represents the event that a particular object is a good populator of a particular context-based conformed dimensional data gravity well, and B represents the event that the particular object has a predefined amount of conformed dimension/context objects applied to its data object. This results in the Bayesian probability formula of:
where:
P(A|B) is the probability that a particular data object will be an appropriate populator of a particular context-based conformed dimensional data gravity well (A) given that (|) a predefined amount of dimension objects are applied to a data object in a conformed dimensional object or a predefined amount of context objects are applied to a data object in a synthetic context-based object (B);
P(B|A) is a probability that the predefined amount of dimension objects are applied to the data object in the conformed dimensional object or the predefined amount of context objects are applied to the data object in the synthetic context-based object (B) given that (|) the data object is assigned to the particular context-based conformed dimensional data gravity well (A);
P(A) is a probability that the particular data object will be the appropriate populator of the particular context-based conformed dimensional data gravity well regardless of any other information; and
P(B) is a probability that the particular data object will have the predefined amount of context and/or dimension objects regardless of any other information.
For example, assume that nine out of ten of the data objects that populate a particular context-based conformed dimensional data gravity well have the predefined amount (e.g., 80%) of the context/dimension objects that are on the sides of the particular context-based conformed dimensional data gravity well. Thus, P(B|A)=9/10=0.90. Assume also that the odds that any data object will be an appropriate populator of a particular context-based conformed dimensional data gravity well, regardless of any other information (P(A)), is 0.20, and that the probability that any data object will have the predefined amount of conformed context/dimension objects regardless of any other information (P(B)) is 0.25. The probability that any one data object will be a good populator of a particular context-based conformed dimensional data gravity well (based on these parameters) is 72%:
However, if nine out of ten of the conformed dimensional objects that populate a particular context-based conformed dimensional data gravity well still have the predefined amount (e.g., 80%) of the context/dimension objects that are on the sides of the particular context-based conformed dimensional data gravity well (P(B|A)=9/10=0.90), but now the probability that any data object will be an appropriate populator of a particular context-based conformed dimensional data gravity well, regardless of any other information (P(A)), is 25%, and the probability that any data object will have the predefined amount of conformed context/dimension objects regardless of any other information (P(B)) is now 23%, then the probability that any one data object will be a good populator of a particular context-based conformed dimensional data gravity well (based on these new parameters) is 98%:
As depicted in block 818, the displayed appearance of the particular context-based conformed dimensional data gravity well is dynamically adjusted according to how closely each of the multiple parsed synthetic context-based objects matches at least one context object. For example, consider
However, in
With reference now to
With reference now to
With reference now to
Returning to
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of various embodiments of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the present invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the present invention. The embodiment was chosen and described in order to best explain the principles of the present invention and the practical application, and to enable others of ordinary skill in the art to understand the present invention for various embodiments with various modifications as are suited to the particular use contemplated.
Note further that any methods described in the present disclosure may be implemented through the use of a VHDL (VHSIC Hardware Description Language) program and a VHDL chip. VHDL is an exemplary design-entry language for Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), and other similar electronic devices. Thus, any software-implemented method described herein may be emulated by a hardware-based VHDL program, which is then applied to a VHDL chip, such as a FPGA.
Having thus described embodiments of the present invention of the present application in detail and by reference to illustrative embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the present invention defined in the appended claims.
| Number | Name | Date | Kind |
|---|---|---|---|
| 5450535 | North | Sep 1995 | A |
| 5664179 | Min et al. | Sep 1997 | A |
| 5689620 | Kopec et al. | Nov 1997 | A |
| 5701460 | Kaplan et al. | Dec 1997 | A |
| 5943663 | Mouradian | Aug 1999 | A |
| 5974427 | Reiter | Oct 1999 | A |
| 6167405 | Rosensteel et al. | Dec 2000 | A |
| 6199064 | Schindler | Mar 2001 | B1 |
| 6269365 | Kiyoki et al. | Jul 2001 | B1 |
| 6275833 | Nakamura et al. | Aug 2001 | B1 |
| 6314555 | Ndumu et al. | Nov 2001 | B1 |
| 6334156 | Matsuoka et al. | Dec 2001 | B1 |
| 6353818 | Carino, Jr. | Mar 2002 | B1 |
| 6381611 | Roberge et al. | Apr 2002 | B1 |
| 6405162 | Segond | Jun 2002 | B1 |
| 6424969 | Gruenwald | Jul 2002 | B1 |
| 6553371 | Gutierrez-Rivas et al. | Apr 2003 | B2 |
| 6735593 | Williams | May 2004 | B1 |
| 6768986 | Cras et al. | Jul 2004 | B2 |
| 6925470 | Sangudi et al. | Aug 2005 | B1 |
| 6990480 | Burt | Jan 2006 | B1 |
| 7019740 | Georgalas | Mar 2006 | B2 |
| 7047253 | Murthy et al. | May 2006 | B1 |
| 7058628 | Page | Jun 2006 | B1 |
| 7103836 | Nakamura et al. | Sep 2006 | B1 |
| 7152070 | Musick et al. | Dec 2006 | B1 |
| 7191183 | Goldstein | Mar 2007 | B1 |
| 7209923 | Cooper | Apr 2007 | B1 |
| 7337174 | Craig | Feb 2008 | B1 |
| 7441264 | Himmel et al. | Oct 2008 | B2 |
| 7477165 | Fux | Jan 2009 | B2 |
| 7493253 | Ceusters et al. | Feb 2009 | B1 |
| 7503007 | Goodman et al. | Mar 2009 | B2 |
| 7523118 | Friedlander et al. | Apr 2009 | B2 |
| 7523123 | Yang et al. | Apr 2009 | B2 |
| 7571163 | Trask | Aug 2009 | B2 |
| 7679534 | Kay et al. | Mar 2010 | B2 |
| 7702605 | Friedlander et al. | Apr 2010 | B2 |
| 7748036 | Speirs, III et al. | Jun 2010 | B2 |
| 7752154 | Friedlander et al. | Jul 2010 | B2 |
| 7797319 | Piedmonte | Jul 2010 | B2 |
| 7778955 | Kuji | Aug 2010 | B2 |
| 7783586 | Friedlander et al. | Aug 2010 | B2 |
| 7788202 | Friedlander et al. | Aug 2010 | B2 |
| 7788203 | Friedlander et al. | Aug 2010 | B2 |
| 7792774 | Friedlander et al. | Sep 2010 | B2 |
| 7792776 | Friedlander et al. | Sep 2010 | B2 |
| 7792783 | Friedlander et al. | Sep 2010 | B2 |
| 7805390 | Friedlander et al. | Sep 2010 | B2 |
| 7805391 | Friedlander et al. | Sep 2010 | B2 |
| 7809660 | Friedlander et al. | Oct 2010 | B2 |
| 7853611 | Friedlander et al. | Dec 2010 | B2 |
| 7870113 | Gruenwald | Jan 2011 | B2 |
| 7877682 | Aegerter | Jan 2011 | B2 |
| 7925610 | Elbaz | Apr 2011 | B2 |
| 7930262 | Friedlander et al. | Apr 2011 | B2 |
| 7940959 | Rubenstein | May 2011 | B2 |
| 7953686 | Friedlander et al. | May 2011 | B2 |
| 7970759 | Friedlander et al. | Jun 2011 | B2 |
| 7996373 | Zoppas et al. | Aug 2011 | B1 |
| 7996393 | Nanno et al. | Aug 2011 | B1 |
| 8032508 | Martinez | Oct 2011 | B2 |
| 8046358 | Thattil | Oct 2011 | B2 |
| 8055603 | Angell et al. | Nov 2011 | B2 |
| 8069188 | Larson et al. | Nov 2011 | B2 |
| 8086614 | Novy | Dec 2011 | B2 |
| 8095726 | O'Connell et al. | Jan 2012 | B1 |
| 8145582 | Angell et al. | Mar 2012 | B2 |
| 8150882 | Meek et al. | Apr 2012 | B2 |
| 8155382 | Rubenstein | Apr 2012 | B2 |
| 8161048 | Procopiuc et al. | Apr 2012 | B2 |
| 8199982 | Fueyo et al. | Jun 2012 | B2 |
| 8234285 | Cohen | Jul 2012 | B1 |
| 8250581 | Blanding et al. | Aug 2012 | B1 |
| 8341626 | Gardner et al. | Dec 2012 | B1 |
| 8447273 | Friedlander et al. | May 2013 | B1 |
| 8457355 | Brown et al. | Jun 2013 | B2 |
| 8489641 | Seefeld et al. | Jul 2013 | B1 |
| 8620958 | Adams et al. | Dec 2013 | B1 |
| 8799323 | Nevin, III | Aug 2014 | B2 |
| 8849907 | Hession et al. | Sep 2014 | B1 |
| 8914413 | Adams et al. | Dec 2014 | B2 |
| 8983981 | Adams et al. | Mar 2015 | B2 |
| 20010051881 | Filler | Dec 2001 | A1 |
| 20020091677 | Sridhar | Jul 2002 | A1 |
| 20020111792 | Cherny | Aug 2002 | A1 |
| 20020184401 | Kadel et al. | Dec 2002 | A1 |
| 20030065626 | Allen | Apr 2003 | A1 |
| 20030088576 | Hattori et al. | May 2003 | A1 |
| 20030097589 | Syvanne | May 2003 | A1 |
| 20030149562 | Walther | Aug 2003 | A1 |
| 20030149934 | Worden | Aug 2003 | A1 |
| 20030212664 | Breining et al. | Nov 2003 | A1 |
| 20030212851 | Drescher et al. | Nov 2003 | A1 |
| 20040036716 | Jordahl | Feb 2004 | A1 |
| 20040111410 | Burgoon et al. | Jun 2004 | A1 |
| 20040153461 | Brown et al. | Aug 2004 | A1 |
| 20040162838 | Murayama et al. | Aug 2004 | A1 |
| 20040249789 | Kapoor et al. | Dec 2004 | A1 |
| 20050050030 | Gudbjartsson | Mar 2005 | A1 |
| 20050086243 | Abbott et al. | Apr 2005 | A1 |
| 20050165866 | Bohannon et al. | Jul 2005 | A1 |
| 20050181350 | Benja-Athon | Aug 2005 | A1 |
| 20050188088 | Fellenstein et al. | Aug 2005 | A1 |
| 20050222890 | Cheng et al. | Oct 2005 | A1 |
| 20050273730 | Card et al. | Dec 2005 | A1 |
| 20050283679 | Heller et al. | Dec 2005 | A1 |
| 20060004851 | Gold et al. | Jan 2006 | A1 |
| 20060036568 | Moore et al. | Feb 2006 | A1 |
| 20060190195 | Watanabe et al. | Aug 2006 | A1 |
| 20060197762 | Smith et al. | Sep 2006 | A1 |
| 20060200253 | Hoffberg et al. | Sep 2006 | A1 |
| 20060256010 | Tanygin et al. | Nov 2006 | A1 |
| 20060271586 | Federighi et al. | Nov 2006 | A1 |
| 20060290697 | Madden et al. | Dec 2006 | A1 |
| 20070006321 | Bantz et al. | Jan 2007 | A1 |
| 20070016614 | Novy | Jan 2007 | A1 |
| 20070038651 | Bernstein et al. | Feb 2007 | A1 |
| 20070067343 | Mihaila et al. | Mar 2007 | A1 |
| 20070073734 | Doan et al. | Mar 2007 | A1 |
| 20070079356 | Grinstein | Apr 2007 | A1 |
| 20070088663 | Donahue | Apr 2007 | A1 |
| 20070130182 | Forney | Jun 2007 | A1 |
| 20070136048 | Richardson-Bunbury et al. | Jun 2007 | A1 |
| 20070174840 | Sharma et al. | Jul 2007 | A1 |
| 20070185850 | Walters et al. | Aug 2007 | A1 |
| 20070239710 | Jing et al. | Oct 2007 | A1 |
| 20070282916 | Albahari et al. | Dec 2007 | A1 |
| 20070300077 | Mani et al. | Dec 2007 | A1 |
| 20080065655 | Chakravarthy et al. | Mar 2008 | A1 |
| 20080066175 | Dillaway et al. | Mar 2008 | A1 |
| 20080086442 | Dasdan et al. | Apr 2008 | A1 |
| 20080091503 | Schirmer et al. | Apr 2008 | A1 |
| 20080133474 | Hsiao et al. | Jun 2008 | A1 |
| 20080147780 | Trevor et al. | Jun 2008 | A1 |
| 20080159317 | Iselborn et al. | Jul 2008 | A1 |
| 20080172715 | Geiger et al. | Jul 2008 | A1 |
| 20080208813 | Friedlander et al. | Aug 2008 | A1 |
| 20080208838 | Friedlander et al. | Aug 2008 | A1 |
| 20080208901 | Friedlander et al. | Aug 2008 | A1 |
| 20080281801 | Larson et al. | Nov 2008 | A1 |
| 20080306926 | Friedlander et al. | Dec 2008 | A1 |
| 20090024553 | Angell et al. | Jan 2009 | A1 |
| 20090064300 | Bagepalli et al. | Mar 2009 | A1 |
| 20090080408 | Natoli et al. | Mar 2009 | A1 |
| 20090125546 | Iborra et al. | May 2009 | A1 |
| 20090144609 | Liang et al. | Jun 2009 | A1 |
| 20090164649 | Kawato | Jun 2009 | A1 |
| 20090165110 | Becker et al. | Jun 2009 | A1 |
| 20090177484 | Davis et al. | Jul 2009 | A1 |
| 20090182707 | Kinyon et al. | Jul 2009 | A1 |
| 20090287676 | Dasdan | Nov 2009 | A1 |
| 20090299988 | Hamilton, II et al. | Dec 2009 | A1 |
| 20090327632 | Glaizel et al. | Dec 2009 | A1 |
| 20100024036 | Morozov et al. | Jan 2010 | A1 |
| 20100030780 | Eshghi et al. | Feb 2010 | A1 |
| 20100070640 | Allen et al. | Mar 2010 | A1 |
| 20100077033 | Lowry | Mar 2010 | A1 |
| 20100088322 | Chowdhury et al. | Apr 2010 | A1 |
| 20100125604 | Martinez et al. | May 2010 | A1 |
| 20100125605 | Nair et al. | May 2010 | A1 |
| 20100131293 | Linthicum et al. | May 2010 | A1 |
| 20100131379 | Dorais et al. | May 2010 | A1 |
| 20100169137 | Jastrebski et al. | Jul 2010 | A1 |
| 20100169758 | Thomsen | Jul 2010 | A1 |
| 20100174692 | Meyer et al. | Jul 2010 | A1 |
| 20100179933 | Bai et al. | Jul 2010 | A1 |
| 20100191743 | Perronnin et al. | Jul 2010 | A1 |
| 20100191747 | Ji et al. | Jul 2010 | A1 |
| 20100241644 | Jackson et al. | Sep 2010 | A1 |
| 20100257198 | Cohen et al. | Oct 2010 | A1 |
| 20100268747 | Kern et al. | Oct 2010 | A1 |
| 20100274785 | Procopiuc et al. | Oct 2010 | A1 |
| 20110040724 | Dircz | Feb 2011 | A1 |
| 20110066649 | Berlyant et al. | Mar 2011 | A1 |
| 20110077048 | Busch | Mar 2011 | A1 |
| 20110078143 | Aggarwal | Mar 2011 | A1 |
| 20110087678 | Frieden et al. | Apr 2011 | A1 |
| 20110093479 | Fuchs | Apr 2011 | A1 |
| 20110098056 | Rhoads et al. | Apr 2011 | A1 |
| 20110123087 | Nie et al. | May 2011 | A1 |
| 20110137882 | Weerasinghe | Jun 2011 | A1 |
| 20110161073 | Lesher et al. | Jun 2011 | A1 |
| 20110194744 | Wang et al. | Aug 2011 | A1 |
| 20110208688 | Ivanov et al. | Aug 2011 | A1 |
| 20110246483 | Darr et al. | Oct 2011 | A1 |
| 20110246498 | Forster | Oct 2011 | A1 |
| 20110252045 | Garg | Oct 2011 | A1 |
| 20110282888 | Koperski et al. | Nov 2011 | A1 |
| 20110299427 | Chu et al. | Dec 2011 | A1 |
| 20110301967 | Friedlander et al. | Dec 2011 | A1 |
| 20110314155 | Narayanaswamy et al. | Dec 2011 | A1 |
| 20120004891 | Rameau et al. | Jan 2012 | A1 |
| 20120005239 | Nevin, III | Jan 2012 | A1 |
| 20120016715 | Brown et al. | Jan 2012 | A1 |
| 20120023141 | Holster | Jan 2012 | A1 |
| 20120072468 | Anthony et al. | Mar 2012 | A1 |
| 20120079493 | Friedlander et al. | Mar 2012 | A1 |
| 20120109640 | Anisimovich et al. | May 2012 | A1 |
| 20120110004 | Meijer | May 2012 | A1 |
| 20120110016 | Phillips | May 2012 | A1 |
| 20120131139 | Siripurapu et al. | May 2012 | A1 |
| 20120131468 | Friedlander et al. | May 2012 | A1 |
| 20120166373 | Sweeney | Jun 2012 | A1 |
| 20120191704 | Jones | Jul 2012 | A1 |
| 20120209858 | Lamba et al. | Aug 2012 | A1 |
| 20120221439 | Sundaresan et al. | Aug 2012 | A1 |
| 20120233194 | Ohyu et al. | Sep 2012 | A1 |
| 20120239761 | Linner et al. | Sep 2012 | A1 |
| 20120240080 | O'Malley | Sep 2012 | A1 |
| 20120246148 | Dror | Sep 2012 | A1 |
| 20120259841 | Hsiao et al. | Oct 2012 | A1 |
| 20120278897 | Ang et al. | Nov 2012 | A1 |
| 20120281830 | Stewart et al. | Nov 2012 | A1 |
| 20120290950 | Rapaport et al. | Nov 2012 | A1 |
| 20120297278 | Gattani et al. | Nov 2012 | A1 |
| 20120311587 | Li et al. | Dec 2012 | A1 |
| 20120316821 | Levermore et al. | Dec 2012 | A1 |
| 20120330880 | Arasu et al. | Dec 2012 | A1 |
| 20120330958 | Xu et al. | Dec 2012 | A1 |
| 20130019084 | Orchard et al. | Jan 2013 | A1 |
| 20130031302 | Byom et al. | Jan 2013 | A1 |
| 20130060696 | Martin et al. | Mar 2013 | A1 |
| 20130103389 | Gattani et al. | Apr 2013 | A1 |
| 20130124564 | Oztekin et al. | May 2013 | A1 |
| 20130173292 | Friedlander et al. | Jul 2013 | A1 |
| 20130173585 | Friedlander et al. | Jul 2013 | A1 |
| 20130191392 | Kumar | Jul 2013 | A1 |
| 20130238667 | Carvalho et al. | Sep 2013 | A1 |
| 20130246562 | Chong et al. | Sep 2013 | A1 |
| 20130254202 | Friedlander et al. | Sep 2013 | A1 |
| 20130291051 | Balinsky et al. | Oct 2013 | A1 |
| 20130291098 | Chung et al. | Oct 2013 | A1 |
| 20130331473 | Safovich et al. | Nov 2013 | A1 |
| 20130326412 | Treiser | Dec 2013 | A1 |
| 20130339379 | Ferrari et al. | Dec 2013 | A1 |
| 20140006411 | Boldyrev et al. | Jan 2014 | A1 |
| 20140012884 | Bornea et al. | Jan 2014 | A1 |
| 20140025702 | Curtiss et al. | Jan 2014 | A1 |
| 20140074833 | Adams et al. | Mar 2014 | A1 |
| 20140074885 | Adams et al. | Mar 2014 | A1 |
| 20140074886 | Medelyan | Mar 2014 | A1 |
| 20140074892 | Adams et al. | Mar 2014 | A1 |
| 20140081939 | Adams et al. | Mar 2014 | A1 |
| 20140172417 | Monk et al. | Jun 2014 | A1 |
| 20140184500 | Adams et al. | Jul 2014 | A1 |
| 20140188960 | Adams et al. | Jul 2014 | A1 |
| 20140214865 | Adams et al. | Jul 2014 | A1 |
| 20140214871 | Adams et al. | Jul 2014 | A1 |
| 20140250111 | Morton et al. | Sep 2014 | A1 |
| 20140344718 | Rapaport et al. | Nov 2014 | A1 |
| Number | Date | Country |
|---|---|---|
| 101866342 | Oct 2010 | CN |
| 102201043 | Sep 2011 | CN |
| 102236701 | Nov 2011 | CN |
| 102385483 | Mar 2012 | CN |
| 1566752 | Aug 2005 | EP |
| 1843259 | Oct 2007 | EP |
| 2006086179 | Aug 2006 | WO |
| 2007044763 | Apr 2007 | WO |
| Entry |
|---|
| U.S. Appl. No. 13/861,058 Non-Final Office Action dated Apr. 25, 2016. |
| U.S. Appl. No. 13/648,801 Examiner's Answer dated Oct. 1, 2015. |
| U.S. Appl. No. 13/610,523—Non-Final Office Action dated Apr. 30, 2015. |
| U.S. Appl. No. 13/540,267—Non-Final Office Action dated Jun. 4, 2015. |
| U.S. Appl. No. 13/609,710—Examiner's Answer dated Jun. 9, 2015. |
| U.S. Appl. No. 13/780,779—Non-Final Office Action dated Apr. 3, 2015. |
| U.S. Appl. No. 13/896,461—Non-Final Office Action dated Apr. 21, 2015. |
| U.S. Appl. No. 13/569,366—Non-Final Office Action dated Jun. 30, 2015. |
| Faulkner, Paul, “Common Patterns for Synthetic Events in Websphere Business Events,” Jan. 15, 2011, http://www.ibm.com/developerworks/websphere/bpmjournal/1101_faulkner2/1101_faulkner2.html, pp. 1-6. |
| Evaggelio Pitoura et al., “Context in Databases”, University of Ioannina, Greece, 2004, pp. 1-19. |
| Avinash Kaushik, “End of Dumb Tables in Web Analytics Tools! Hello: Weighted Sort”, Sep. 7, 2010, www.kaushik.net, pp. 1-15. |
| Lorenzo Alberton, “Graphs in the Database: SQL Meets Social Networks,” Techportal, Sep. 7, 2009, http://techportal.inviqa.com/2009/09/07/graphs-in-the-database-sql-meets-social-networks/, pp. 1-11. |
| Visual Paradigm, “DB Visual Architect 4.0 Designer's Guide: Chapter 6—Mapping Object Model to Data Model and Vice Versa”, 2007, pp. 6-2-6-26. |
| U.S. Appl. No. 13/609,710—Non-Final Office Action dated Jan. 27, 2014. |
| U.S. Appl. No. 13/540,295—Non-Final Office Action dated Jan. 30, 2014. |
| U.S. Appl. No. 13/540,230—Non-Final Office Action dated Jan. 30, 2014. |
| U.S. Appl. No. 13/540,267—Non-Final Office Action dated Feb. 4, 2014. |
| U.S. Appl. No. 13/628,853—Notice of Allowance dated Mar. 4, 2014. |
| U.S. Appl. No. 13/595,356—Non-Final Office Action dated Apr. 14, 2014. |
| “Ninth New Collegiate Dictionary”, Merriam-Webster Inc., 1991, p. 77 and 242. |
| “The American Heritage College Dictionary”, Fourth Edition, Houghton Mifflin Company, 2004, pp. 44 and 262. |
| U.S. Appl. No. 13/680,832—Non-Final Office Action dated Apr. 8, 2014. |
| U.S. Appl. No. 13/592,905—Notice of Allowance dated Oct. 25, 2013. |
| U.S. Appl. No. 13/342,406—Non-Final Office Action dated Sep. 27, 2013. |
| U.S. Appl. No. 13/610,347—Non-Final Office Action dated Jul. 19, 2013. |
| U.S. Appl. No. 13/610,347—Notice of Allowance dated Aug. 19, 2013. |
| M.J. Flynn, et al., “Sparse Distributed Memory Principles of Operation”, Research Institute for Advanced Computer Science, 1989, pp. 1-60. |
| P. Kanerva, “Hyperdimensional Computing: An Introduction to Computing in Distributed Representation With High-Dimensional Random Vectors”, Springer Science+Business Media, LLC, Cogn Comput, 1, 2009, pp. 139-159. |
| P. Kanerva, “What We Mean When We Say “What's the Dollar of Mexico?”: Prototypes and Mapping in Concept Space”, Quantum Informatics for Cognitive, Social, and Semantic Processes: Papers From the AAAI Fall Symposium, Association for the Advancement of Artificial Intelligence, 2010, pp. 2-6. |
| M. Yu, et al., “Secure and Robust Error Correction for Physical Unclonable Functions”, Verifying Physical Trustworthiness of ICS and Systems, IEEE Design & Test of Computers, IEEE, Jan./Feb. 2010, pp. 48-64. |
| A. Jin, et al., “Biohashing: Two Factor Authentication Featuring Fingerprint Data and Tokenised Random Number,” Pattern Recognition 37, Elsevier Ltd., 2004, pp. 2245-2255. |
| N. Saxena et al., “Data remanence effects on memory-based entropy collection for RFID systems”, International Journal of Information Security 10.4 (2011), pp. 213-222. |
| A. Birrell et al., “A design for high-performance flash disks.” ACM SIGOPS Operating Systems Review 41.2 (2007), pp. 88-93. |
| Richard Saling, “How to Give a Great Presentation! From the HP Learning Center”, Jul. 28, 2008, <http://rsaling.wordpress.com/2008/07/28/how-to-give-a-great-presentation/>, pp. 1-28. |
| K. Matterhorn, “How to Share Data Between a Host Computer & Virtual Machine,” Ehow, pp. 1-3, <http://www.ehow.com/how_7385388_share-host-computer-virtual-machine.html>, Retrieved Feb. 17, 2013. |
| W. Caid et al., “Context Vector-Based Text Retrieval”, Fair Isaac Corporation, Aug. 2003, pp. 1-20. |
| Anonymous “Fraud Detection Using Data Analytics in the Banking Industry,” ACL Services Ltd., 2010, pp. 1-9 <http://www.acl.com/pdfs/DP_Fraud_detection_BANKING.pdf>. |
| J. Cheng et al., “Context-Aware Object Connection Discovery in Large Graphs”, Data Engineering, 2009. ICDE '09. IEEE 25th International Conferen. |
| R. Angles et al., “Survey of Graph Database Models”, ACM Computing Surveys, vol. 40, No. 1, Article 1, Feb. 2008, pp. 1-65. |
| U.S. Appl. No. 13/592,905—Non-Final Office Action dated May 8, 2013. |
| U.S. Appl. No. 13/628,853—Non-Final Office Action dated Nov. 7, 2013. |
| U.S. Appl. No. 13/342,406—Notice of Allowance dated March 20, 2014. |
| U.S. Appl. No. 13/755,623—Notice of Allowance dated May 27, 2014. |
| S. Alam et al., “Interoperability of Security-Enabled Internet of Things”, Springer, Wireless Personal Communications, Dec. 2011, vol. 61, pp. 567-586. |
| U.S. Appl. No. 13/648,801—Non-Final Office Action dated Jul. 1, 2014. |
| U.S. Appl. No. 13/609,710—Final Office Action dated Jul. 24, 2014. |
| U.S. Appl. No. 13/861,058—Non-Final Office Action dated Dec. 11, 2014. |
| U.S. Appl. No. 13/733,052—Non-Final Office Action dated Sep. 18, 2014. |
| U.S. Appl. No. 13/755,987—Non-Final Office Action dated Jan. 2, 2015. |
| U.S. Appl. No. 13/648,801—Final Office Action dated Jan. 13, 2015. |
| U.S. Appl. No. 13/609,710 Decision on Appeal Mailed Nov. 4, 2016. |
| U.S. Appl. No. 13/733,066 Examiner's Answer dated Dec. 20, 2016. |
| U.S. Appl. No. 13/861,058 Final Office Action dated Dec. 29, 2016. |
| U.S. Appl. No. 13/648,801 Decision on Appeal Mailed Jan. 18, 2017. |
| G. Begelman et al., “Automated Tag Clustering: Improving Search and Exploration in the TagSpace”, Collaborative Tagging Workshop, WWW2006, Edinburgh, Scotland, May 2006, pp. 1-29. |
| U.S. Appl. No. 13/621,931—Non-Final Office Action dated Jan. 28, 2015. |
| U.S. Appl. No. 13/732,567—Non-Final Office Action dated Jan. 30, 2015. |
| U.S. Appl. No. 14/078,135—Notice of Allowance dated Feb. 24, 2015. |
| U.S. Appl. No. 13/756,051—Notice of Allowance dated Feb. 27, 2015. |
| U.S. Appl. No. 13/732,567—Non-Final Office Action dated Mar. 26, 2015. |
| L. Du et al., “A Unified Object-Oriented Toolkit for Discrete Contextual Computer Vision”, IEEE, IEEE Colloquium on Pattern Recognition, Feb. 1997, pp. 3/1-3/5. (Abstract Only). |
| S. Ceri et al., “Model-Driven Development of Context-Aware Web Applications”, ACM, ACM Transactions on Internet Technology, 2007, (Abstract Only). |
| Graham Pryor, “Attitudes and Aspirations in a Diverse World: The Project Store Perspective on Scientific Repositories”. Ukoln, University of Bath, Digital Curation Center. The International Journal of Digital Curation, Issue 1, vol. 2, 2007. Nov. 2006. |
| Filippova, Katja and Keith B. Hall, “Improved Video Categorization From Text Metadata and User Comments”. Proceedings of the 34th International SCM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2011. |
| U.S. Appl. No. 13/896,506 Non-Final Office Action dated Oct. 26, 2015. |
| U.S. Appl. No. 13/342,305, filed Jan. 3, 2012. |
| U.S. Appl. No. 13/562,714, filed Jul. 31, 2012. |
| U.S. Appl. No. 15/223,296 Notice of Allowance dated Jun. 30, 2017. |
| U.S. Appl. No. 13/861,058 Non-Final Office Action dated Aug. 28, 2017. |
| Number | Date | Country | |
|---|---|---|---|
| 20160299962 A1 | Oct 2016 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 14695247 | Apr 2015 | US |
| Child | 15190361 | US | |
| Parent | 13756051 | Jan 2013 | US |
| Child | 14695247 | US |
