International Business Machines Corporation

Noor Mohammed Ashrafi

Srinivasan S Muthuswamy

Mohan Nagraj Dani

Ratul Sarkar

Ankit Kumar Singh

Balaji Rishi Sundarajan

Sharath C Athrey

A system, computer program product, and method are presented for leveraging structured data and unstructured data, and, more specifically, to ranking documentation from unstructured data sources through leveraging insights provided by the structured data to facilitate associated business risk inquiries. The method includes identifying, by researching subject business entities, one or more structured data sources that include relevant structured data directed to the subject business entities. The method also include extracting the relevant structured data directed toward the subject business entities and leveraging the relevant structured data to identify unstructured data sources. The method further includes identifying documents from the unstructured data sources that have relevant information, thereby identifying relevant unstructured data, and leveraging the relevant structured data to determine relationships with the relevant unstructured data. The method also includes scoring each relationship and ranking each document from the unstructured data sources as a function of the scoring.

Leveraging structured data to rank unstructured data

A computer-implemented system, method and computer program product for capturing data transitions and selecting machine learning models that includes: providing a machine learning model trained with a previous training data set; receiving a new data set; comparing the new data set to the previous data set; and identifying and recording new, removed, and/or changed set of attributes added to the new data set. Future possible data state transitions are generated based upon the present data state; and an implication tree is generated based upon the present data state, the pass-through data states, and the future possible data state transitions. Performance metrics of each node in the implication tree are clustered, the nodes demonstrating a high variance optionally are discarded; reachability scores for remaining nodes are calculated; and a node (representing a machine learning model to run) is selected based upon its reachability score.

System and method to capture data transitions and select optimal data intensive machine learning model

Growing community of inventors

Bengaluru, India

Noor Mohammed Ashrafi