Microsoft Technology Licensing, LLC

Anja Gruenheid

Kris Kuppuswamy Ganjam

Yeye He

Shaleen Deep

Ling Zhang

Regular expression ('regex') decomposition and evaluation is disclosed. In an aspect of the disclosure, a literal component and a pattern component in a regex are determined. A plurality of evaluation processes is determined based on the regex, including a first evaluation process configured to identify text that matches the literal component and the pattern component. The evaluation processes are executed with respect to a sample of data to determine performance characteristics including a performance characteristic respective to each evaluation process. An evaluation process of the evaluation processes is selected based on the determined performance characteristics. The selected evaluation process is executed with respect to further data to retrieve results that satisfy the selected evaluation process. In another aspect, the pattern component is decomposed into subcomponents. In another aspect, the evaluation processes include an evaluation process that specifies a process to provide the regex to a regex engine for execution thereof.

Regular expression decomposition and evaluation

Correcting data in a dataset. A set of data tokens from a tabular data store are grouped into a plurality of different clusters based on similarity of tokens. A reference cluster is selected from among the plurality of different clusters such that the plurality of clusters includes a reference cluster and one or more other clusters. One or more tokens in the one or more other clusters are transformed. The effect on the reference cluster of adding the transformed tokens to the reference cluster is determined. Using this information, a correction for a token in the dataset is identified. The data store is updated to correct the token using the identified correction.

Repairing data through domain knowledge

Correcting data in a dataset. A set of data tokens from a tabular data store are grouped into a plurality of different clusters based on similarity of tokens. A reference cluster is selected from among the plurality of different clusters such that the plurality of clusters includes a reference cluster and one or more other clusters, one or more tokens in the one or more other clusters are transformed. Transforming tokens is performed based on a cost of transforming tokens. The effect on the reference cluster of adding the transformed tokens to the reference cluster is determined. Using this information, a correction for a token in the dataset is identified. The data store is updated to correct the token.

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Zurich, Switzerland

Anja Gruenheid