System and method for managing knowledge
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Abstract
An intelligence system is provided that is comprised of the following basic components. First, a system for converting incoming unstructured data into a well described normalized form. Since the incoming data is multimedia and may represent some data type for which support is provided by the underlying OS platform, this normalized form include the ability to fully describe and manipulate arbitrarily complex native or non-native binary structures and collections. This support is preferably provided by a dedicated ‘mining’ language tied intimately to a system ontology. Second, a system for accessing and manipulating data held either in memory or in persistent storage in its normalized binary form so that small executables, or ‘widgets’, within the system can freely and effectively operate on data types they have never before encountered simply by knowledge of the ‘type’ of data involved. Third, an ‘ontology’ or world model that represents and contains the items and fields necessary for the target system to perform its function. The ontology would preferably fully specify the form of the normalized binary data. Fourth, a memory system, tied to the ontology, which defines the structure of and access to any persistent storage containers that are required to contain the data. Fifth, a memory management system for splitting incoming data into those portions to be directed to each container. Sixth, a query system for querying each container to retrieve portions of such a composite object. Preferably, all database tables and queries are auto-generated from the ontology, thereby eliminating the role of the conventional Database Administrator (DBA). Seventh, a UI to display and interact with data within the system. In the preferred embodiment, the UI is automatically generated and its behaviors automatically handled by the underlying substrate thus removing this programming burden from the developer (thereby largely eliminating the role of the GUI programmer). Finally, a memory system that forms collections of datums, and enables manipulation and exchange of these collections both within the local machine as well as across the network. In the preferred embodiment, such collections support the ability to attach arbitrary tags or annotations to the binary data they contain without in any way altering the binary representation itself. Additionally, the system supports the concept of either null or dirty (i.e., has been changed locally) datum.
648 Citations
25 Claims
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1. (canceled)
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2. A method for facilitating meta-analysis of data captured for intelligence purposes using a computer network and implemented as an unconstrained system, the method comprising the steps of:
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(a) establishing a distributed acquisition server architecture within the computer network responsive to a data-flow driven environment;
(b) sampling a plurality of streams of unstructured data by said distributed acquisition server architecture;
(c) converting said plurality of streams of unstructured data into a well described normalized form of binary data via a dedicated mining language tied to a current system ontology;
(d) storing said converted binary data in a memory system tied to said current system ontology within said computer network, wherein said memory system defines a plurality of persistent storage containers required to contain said converted binary data;
(e) directing said storing step with a memory management system which splits said converted binary data into an appropriate one of said plurality of persistent storage containers;
(f) executing one or more control and/or data-flow based programs, called widgets, on said converted binary data stored in said plurality of persistent storage containers, wherein execution of said one or more widgets begins when a matching set of data objects or tokens from said converted binary data appear on an input data-flow pin of said one or more widgets;
(g) producing a set of resultant data tokens on an output data-flow pin of said one or more widgets, wherein said set of resultant data tokens become part of said data-flow driven environment in said persistent storage containers or in a memory of a computer within the computer network;
(h) querying a registered search capability of one or more said plurality of persistent storage containers producing a list of hits;
(i) querying said list of hits with Boolean and other operators to specify logical combinations of said list of hits;
(j) displaying and interacting with said plurality of streams of unstructured data, said list of hits, and said logical combinations of said list of hits through a user interface on a display device within the computer network;
(k) forming collections of datums from said logical combinations of said list of hits through a memory collections system that forms and enables manipulation and exchange of said collections of datums both within a local computer as well as across the computer network;
(l) delivering said collections of datums for meta-analysis to a user accessing the computer network through said user interface; and
(In) based upon said meta-analysis by said user, revising said querying steps (h) and (i) repeating steps (j), (k) and (l). - View Dependent Claims (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A system for facilitating meta-analysis of data captured for intelligence purposes within a computer network, which is implemented as an unconstrained system, the system comprising:
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a distributed acquisition server architecture within the computer network responsive to a data-flow driven environment;
a plurality of streams of unstructured data which are sampled by said distributed acquisition server architecture;
a dedicated mining language tied to a current system ontology for converting said plurality of streams of unstructured data into a well described normalized form of binary data;
a memory system tied to said current system ontology within said computer network for storing said converted binary data, wherein said memory system defines a plurality of persistent storage containers required to contain said converted binary data;
a memory management system for splitting and directing said converted binary data into an appropriate one of said plurality of persistent storage containers;
one or more control and/or data-flow based programs, called widgets, each said widget having at least one input data-flow pin and at least one output data-flow pin, wherein said one or more widgets are executed on said converted binary data stored in said plurality of persistent storage containers when a matching set of data objects or tokens from said converted binary data appear on said at least one input data-flow pin of said one or more widgets;
a set of resultant data tokens produced on said output data-flow pins of said one or more widgets, wherein said set of resultant data tokens become part of said data-flow driven environment in said persistent storage containers or in a memory of a computer within the computer network;
a user interface having a lower querying layer and an upper querying layer, wherein said lower querying layer queries one or more registered search capability for each of said plurality of persistent storage containers which produces a list of hits, and further wherein said upper querying layer queries said list of hits with Boolean and other operators to specify logical combinations of said list of hits;
a display device within the computer network for displaying and interacting with said plurality of streams of unstructured data, said list of hits, and said logical combinations of said list of hits through said user interface; and
a memory collections system that forms collections of datums from said logical combinations of said list of hits and enables manipulation and exchange of said collections of datums both within a local computer as well as across the computer network, wherein a user accesses through said user interface said collections of datums for meta-analysis, and based upon said meta-analysis by said user, said user can revise said queries to refine said collections of datums. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25)
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Specification