Microsoft Technology Licensing, LLC

Steven Peter Herbert

Boris Asipov

Saikat Guha

Guoyu Hao

Aditya Gupta

Described herein is a system and method for utilizing data flow analysis to perform data classification with respect to a source dataset and a generated derived dataset. A flow confidence for a field is calculated using an adaptive algorithm in accordance with the action performed and the derived dataset. An associated derived confidence for a particular tag is calculated in accordance with an associated confidence and the flow confidence. When the associated derived confidence is greater than or equal to a first threshold, the particular tag is copied to the derived dataset. In some embodiments, when the associated derived confidence is less than or equal to a second threshold, the particular tag is not copied to the derived dataset. Otherwise an action to be taken is identified. A response to the action is received and the adaptive algorithm is modified in accordance with the received response.

Data classification using data flow analysis

According to one aspect of the present disclosure, a computing device is provided, including non-volatile memory storing a database including a plurality of database entries. The computing device may further include a processor configured to sort the plurality of database entries into a plurality of database entry length sets. For each database entry length set, each database entry included in the database entry length set may be within a predefined length range. For each database entry length set, the processor may be further configured to generate a probabilistic data structure based on the one or more database entries included in the database entry length set.

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Woodinville, WA, United States of America

Steven Peter Herbert