Aumni Data, Inc.

Alumni Data Inc.

Joan Wrabetz

Aloke Guha

Shumin Wu

Venky Madireddi

Various embodiments of the present invention disclose a method for Business Intelligence (BI) metrics on unstructured data. Unstructured data is collected from numerous data sources that include unstructured data as ingested data. The ingested data is indexed and represents hyperlink and extracted data and metadata for each document. Thereafter, the ingested data is automatically classified into one or more relevance classes. Further, numerous analytics are performed on the classified data to generate business intelligence metrics that may be presented on an access device operated by a user.

Method and system for business intelligence analytics on unstructured data

In one embodiment, input is received from a user defining a classification and an analytic for the classification. Multiple classifications and analytics may be defined by a user. A definition of relevance parameters is determined that characterize the classification and a set of analytics measures associated with the analytic. The definition may be for the classification. Unstructured data and structured data are analyzed based on the definition of the relevance parameters to determine relevant data in the unstructured data and the structured data. The relevant data being data that is determined to be relevant to the classification defined by the user. An index of the terms from the relevant data is determined. The index is useable by an analytics tool to provide results for queries of the unstructured data and structured data. The query may be used within the classification such that targeted results are provided using the index and the relevant data to the classification. Thus, queries from different classifications may be performed efficiently using data determined to be relevant to the classification.

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Long Lake, MN, United States of America

Joan Wrabetz