Abstract: 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.

Abstract: 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.

Abstract: A method and system for adaptive classification during information retrieval from unstructured data are provided. The method includes receiving input from a user defining a classification. A sample set of unstructured data based on the user defined classification defined is determined. The sample set of unstructured data is analyzed to determine a classification mapping that maps attributes of the sample set of unstructured data to class labels for the classification. The attributes of a set of data objects in a second set of unstructured data are indexed and one or more data objects in the set of data objects are mapped to the class label based on the classification mapping. Feedback based on the user's response to an interaction with results is determined using the class label. Finally, adaptive classification mapping is performed based on analysis of feedback by adjusting the sample set of data objects.

Abstract: 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.