Dr Holdco 2, Inc.

Paxata, Inc.

Datarobot, Inc.

Dave Brewster

Victor Tze-Yeuan Tso

Nenshad Dinshaw Bardoliwalla

Jing Chen

Whitman Kwok

Lilia Gutnik

Michael Matthews

Quan Chuong Ta

Ian Timourian

Lakshman Roy Sankar

Ashley Ping Jin

Amnon Levy

Pakshi Rajan

Krupa Natarajan

Ashley Jin

Signature-based cache optimization for data preparation includes: performing a first set of sequenced data preparation operations on one or more sets of data to generate a plurality of transformation results; caching one or more of the plurality of transformation results and one or more corresponding operation signatures, a cached operation signature being derived based at least in part on a subset of sequenced operations that generated a corresponding result; receiving a specification of a second set of sequenced operations; determining an operation signature associated with the second set of sequenced operations; identifying a cached result among the cached results based at least in part on the determined operation signature; and outputting the cached result.

Signature-based cache optimization for data preparation

Using a step editor for data preparation includes: receiving an indication of a user input with respect to at least some of a set of sequenced data preparation operations on a set of data; generating, using one or more processors, a signature based at least in part on the set of sequenced data preparation operations, references to the set of data, and the user input; using the generated signature to determine whether there exists a cached result associated with the set of sequenced data preparation operations, the references to the set of data, and the user input; based at least in part on the determination, obtaining a data traversal program representing a result associated with the set of sequenced operations, the references to the set of data, and the user input; and providing output based at least in part on the result represented by the obtained data traversal program.

Step editor for data preparation

Distributed pipeline optimization for data preparation includes receiving a specification of a set of sequenced operations to be performed on a set of organized data. It further includes dividing the set of data into a plurality of work portions based on a cost function that is dependent on at least one dimension of the set of data. It further includes distributing the plurality of work portions to a plurality of processing nodes to be processed according to the specification of operations.

Distributed pipeline optimization for data preparation

Automatic append includes: identifying, based at least in part on contents of a first data set comprising a first plurality of columns and contents of a second data set comprising a second plurality of columns, a plurality of matching columns and a plurality of non-matching columns. The matching columns comprise one or more columns among the first plurality of columns; and corresponding one or more matching columns among the second plurality of columns. The non-matching columns comprise: one or more columns among the first plurality of columns that do not match with any columns among the second plurality of columns; and one or more columns among the second plurality of columns that do not match with any columns among the first plurality of columns. Automatic append further includes obtaining a user specification of a first one or more non-matching columns to be appended to a second one or more non-matching columns, the first one or more non-matching columns and the second one or more non-matching columns being selected among the plurality of non-matching columns; and appending the first data set and the second data set according to at least the identified plurality of matching columns and the user specification.

Automatic content-based append detection

Cache optimization for data preparation includes generating a data traversal program that represents a result of a set of sequenced data preparation operations performed on one or more sets of data. The data traversal program indicates how to assemble one or more affected columns in the one or more sets of data to derive the result. It further includes in response to receiving a specification of the set of sequenced operations to be performed on the one or more sets of data, accessing the data traversal program that represents the result or a stored copy of the data traversal program that represents the result. It further includes assembling the one or more affected columns in the one or more sets of data according to the data traversal program to re-generate the result. It further includes outputting the result.

Cache optimization for data preparation

Navigating to a data preparation context is disclosed. A set of data preparation operations is performed on one or more data sets to generate a set of answer sets in a first application. A final answer set in the set of answer sets is provided to a second application. In response to a user specification of a source-related query, a reference associated with the source-related query is obtained. A corresponding subset of the set of answer sets associated with one or more corresponding or relevant data preparation operations is determined according to the obtained reference. The corresponding subset of the set of answer sets associated with the one or more data preparation operations are presented in the first application according to the obtained reference.

Data preparation context navigation

Signature-based cache optimization for data preparation includes performing a first set of sequenced data preparation operations on one or more sets of data to generate a plurality of transformation results. It further includes caching one or more of the plurality of transformation results and one or more corresponding operation signatures, a cached operation signature being derived based at least in part on a subset of sequenced operations that generated a corresponding result. It further includes receiving a specification of a second set of sequenced operations. It further includes determining an operation signature associated with the second set of sequenced operations. It further includes identifying a cached result among the cached results based at least in part on the determined operation signature; and outputting the cached result.

Using a step editor for data preparation includes receiving an indication of a user input with respect to at least some of a set of sequenced data preparation operations on a set of data. It further includes generating, using one or more processors, a signature based at least in part on the set of sequenced data preparation operations, references to the set of data, and the user input. It further includes using the generated signature to determine whether there exists a cached result associated with the set of sequenced data preparation operations, the references to the set of data, and the user input. It further includes based at least in part on the determination, obtaining a data traversal program representing a result associated with the set of sequenced operations, the references to the set of data, and the user input. It further includes providing output based at least in part on the result represented by the obtained data traversal program.

Automated join detection includes: identifying a set of one or more candidate joins of a first table and a second table; evaluating a set of one or more quality measures corresponding to the set of one or more candidate joins; obtaining a set of one or more selected joins among the set of one or more candidate joins, the set of one or more selected joins being selected based at least in part on one or more corresponding quality measures; and generating a joined table, including by joining the first table and the second table according to a selected join.

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Redwood City, CA, United States of America

Dave Brewster