Enterprise data processing
First Claim
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1. An enterprise data processing module, comprising:
- a non-transitory computer readable medium storing a sequence of instructions;
a memory storing one or more big-data databases; and
an analysis engine operable to execute the sequence of instructions to;
collect a plurality of data pieces from a plurality of data sources, analyze the plurality of data pieces to determine a cross-source relationship, wherein the data pieces are stored in the one or more big-data databases as blocks of data according to the cross-source relationship, wherein the cross-source relationship comprises a plurality of dimensions, each dimension corresponding to a different data characteristic, andgenerating a weighted combination of the plurality of dimensions of the cross-source relationship, each metric of a plurality of metrics corresponding to a unique dimension of the plurality of dimensions, the weighted combination being a single composite weight that combines the plurality of metrics, wherein the cross-source relationship comprises a degree of correlation that is determined by a correlation intensity algorithm, andwherein the correlation intensity algorithm determines a level of similarity with respect to the number of unique concepts in each data piece.
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Abstract
An enterprise data processing module and method are described herein. The enterprise data processing module comprises at least one collector and at least one analyzer. The collectors may be operable to collect data pieces from a plurality of data sources. The analyzers may be operable to analyze the collected data pieces to determine cross-source relationships that exist between the data pieces collected from the plurality of sources. The analyzed data pieces may be stored in one or more big-data databases as blocks of data according to the cross-source relationships.
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Citations
16 Claims
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1. An enterprise data processing module, comprising:
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a non-transitory computer readable medium storing a sequence of instructions;
a memory storing one or more big-data databases; andan analysis engine operable to execute the sequence of instructions to; collect a plurality of data pieces from a plurality of data sources, analyze the plurality of data pieces to determine a cross-source relationship, wherein the data pieces are stored in the one or more big-data databases as blocks of data according to the cross-source relationship, wherein the cross-source relationship comprises a plurality of dimensions, each dimension corresponding to a different data characteristic, and generating a weighted combination of the plurality of dimensions of the cross-source relationship, each metric of a plurality of metrics corresponding to a unique dimension of the plurality of dimensions, the weighted combination being a single composite weight that combines the plurality of metrics, wherein the cross-source relationship comprises a degree of correlation that is determined by a correlation intensity algorithm, and wherein the correlation intensity algorithm determines a level of similarity with respect to the number of unique concepts in each data piece. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for operating an enterprise data processing module, the enterprise data processing module comprising a memory, an analysis engine, and a non-transitory computer readable medium storing a sequence of instructions, the method comprising:
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collecting data pieces into the memory from a plurality of data sources; determining a cross-source relationship that exists between data pieces collected from different sources of the plurality of sources, the determining being performed by the analysis engine, wherein the cross-source relationship comprises a plurality of dimensions, each dimension corresponding to a different data characteristic; assigning one or more weights to each dimension of the plurality of dimensions of the cross-source relationship, the assigning being performed by the analysis engine; generating conclusion data that is based on the request from the user, the data pieces and the cross-source relationship, the conclusion data being a single composite weight that combines the one or more weights from each dimension of the plurality of dimensions of the cross-source relationship, the generating being performed by the analysis engine; generating one or more data globs, each data glob including the data pieces, the cross-source relationship and one or more access rules, the generating being performed by the analysis engine; and storing the one or more data globs in one or more big-data databases in a memory, wherein determining a cross-source relationship comprises determining a degree of correlation according to a correlation intensity algorithm, and wherein the correlation intensity algorithm determines a level of similarity with respect to the number of unique concepts in each data piece. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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Specification