International Business Machines Corporation

Green Market Square Limited

Patrick G O'Sullivan

Pedro Miguel Barbas

Austin Clifford

Konrad Emanowicz

Julia Blyumen

Michael J Loughran

An embodiment generates, using a trained document embedding model, a plurality of table embeddings, each table embedding in the plurality of table embeddings representing a table in a dataset. An embodiment generates, using the trained document embedding model, a query embedding, the query embedding representing a natural language query regarding the dataset. An embodiment identifies a set of responsive tables, the set of responsive tables comprising a first table in the dataset, the first table in the dataset represented by a first table embedding, the first table embedding and the query embedding having a similarity score above a similarity score threshold.

Embedding based heterogenous dataset evaluation

A computer-implemented method, computer program product and system for identifying pseudonymized data within data sources. One or more data repositories within one or more of the data sources are selected. One or more privacy data models are provided, where each of the privacy data models includes pattern(s) and/or parameter(s). One or more of the one or more privacy data models are selected. Data identification information is generated, where the data identification information indicates a presence or absence of pseudonymized data and of non-pseudonymized data within the one or more of the data sources. The data identification information is generated utilizing the pattern(s) and/or the parameter(s) to determine pseudonymized data.

Runtime control of automation accuracy using adjustable thresholds

Identification of pseudonymized data within data sources

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Mulhuddart, Ireland

Patrick G O'Sullivan