Microsoft Technology Licensing, LLC

Saswat Padhi

Sumit Gulwani

Oleksandr Polozov

Prateek Jain

Daniel Adam Perelman

Benjamin Zorn

Rishabh Singh

Daniel Galen Simmons

Kevin David James Grealish

Pallavi Choudhury

Marc Manuel Johannes Brockschmidt

Ranvijay Kumar

Kevin Michael Ellis

Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.

Systems, methods, and computer-readable media for improved table identification using a neural network

A computing device includes a storage machine holding instructions executable by a logic machine to generate multi-string clusters, each containing alphanumeric strings of a dataset. Further multi-string clusters are generated via iterative performance of a combination operation in which a hierarchically-superior cluster is generated from a set of multi-string clusters. The combination operation includes, for candidate pairs of multi-string clusters, generating syntactic profiles describing an alphanumeric string from each multi-string cluster of the candidate pair. For each of the candidate pairs, a cost factor is determined for at least one of its syntactic profiles. Based on the cost factors determined for the syntactic profiles, one of the candidate pairs is selected. The multi-string clusters from the selected candidate pair are combined to generate the hierarchically-superior cluster including all of the alphanumeric strings from the selected candidate pair of multi-string clusters.

Syntactic profiling of alphanumeric strings

A method for generating a smaller dataset from a larger dataset, each dataset holding a plurality of records, includes profiling the larger dataset to identify a plurality of patterns, each of which is descriptive of one or more records held in the larger dataset. A plurality of slots of the smaller dataset is filled with records held in the larger dataset. Multiple records held in the larger dataset are individually retrieved, and for each retrieved record it is determined whether to place the retrieved record into a slot of the smaller dataset and evict a record already occupying that slot, or not place the retrieved record into the smaller dataset. This determination is based on a pattern of the retrieved record and a representation status of the pattern in the smaller dataset.

Record profiling for dataset sampling

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Saswat Padhi