Business Objects Software Limited

Michael Tsumura

Viren Suresh Kumar

Ivailo Ivanov

Yuru Wang

JohnPaul Burbank

Jennifer Sam

George Sun

Terry Wayne Penner

David Michael MacLean

Francis Wai Gee Lui

Wai Sing Wang

In some example embodiments, a graphical user interface (GUI) is caused to be displayed on a computing device of a user. The GUI can be configured to enable the user to submit an identification of a dataset and at least one configuration parameter. The identification of the data source, the at least one configuration parameter, and the at least one wrangling parameter can be received via the GUI on the computing device. A sampling algorithm can be configured based on the at least one configuration parameter. A sample of data from the dataset can be generated using the configured sampling algorithm. At least one data wrangling operation can be performed on the sample of data based on the at least one wrangling parameter.

System and method of data wrangling

In some example embodiments, a graphical user interface (GUI) is caused to be displayed on a computing device of a user. The GUI can be configured to enable the user to submit an identification of a dataset and at least one configuration parameter. The identification of the data source and the at least one configuration parameter can be received via the GUI on the computing device. A sampling algorithm can be configured based on the at least one configuration parameter. A sample of data from the dataset can be generated using the configured sampling algorithm. The time parameter can limit an execution time of the generating of the sample of data to within a maximum amount of time indicated by the user.

Example embodiments involve a system, computer-readable storage medium storing at least one program, and computer-implemented method for behaviorally consistent data wrangling. A local client device selects a set of raw sample data from a remote datastore. A local execution engine then applies one or more local data wrangling operations to the raw sample data. If the results of the local data wrangling operations are satisfactory, the local data wrangling operations may then be transferred to a remote data wrangling cluster. A remote execution engine being executed by the remote data wrangling cluster then applies the data wrangling operations to the larger set of raw data from which the sample raw data was obtained. As the remote execution engine and the local execution engine are of the same type, the data wrangling behavior exhibited by the local execution engine is reflected in the data wrangling behavior of the remote execution engine.

Behaviorally consistent cluster-wide data wrangling based on locally processed sampled data

Techniques for obtaining data from unstructured data for a structured data collection include receiving unstructured data that includes text; identifying an attribute associated with a structured data collection; obtaining at least one of historical data associated with the attribute or additional data associated with a user of the computing system; identifying one or more terms from the unstructured data as being associated with the attribute based on at least one of the historical data or the additional data; and storing the identified one or more terms in a data record of the unstructured data collection.

Obtaining data from unstructured data for a structured data collection

Techniques for processing semi-structured data include receiving semi-structured data, a value of the semi-structured data being associated with a key; identifying a structured data collection, the structured data collection being associated with a category and having a plurality of attributes; obtaining at least one of historical data associated with the plurality of attributes or additional data associated with a user of the computing system; mapping the key to an attribute of the plurality of attributes based on at least one of the historical data or the additional data; and storing the value in a cell of a data record of the structured data collection, the cell being associated with the attribute that was mapped to the key.

Processing semi-structured data

Techniques for disambiguating data using contextual and historical information include determining that data is potentially associated with two or more classifications of a plurality of classifications; obtaining contextual information associated with the data; obtaining historical information associated with a user of the computing system; and determining that the data is more likely associated with one classification of the two or more classifications than other classifications of the two or more classifications based on the contextual information and the historical information.

Disambiguating data using contextual and historical information

A computer readable storage medium comprises a publication definition module to create and define properties for a publication object. The publication definition module includes executable instructions to associate a set of static documents and a set of report templates with the publication object. Executable instructions define a conditional delivery rule for the publication object. The conditional delivery rule specifies a condition for the inclusion of a report template from the set of report templates to be merged with a static document from the set of static documents.

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Richmond, CA, United States of America

Michael Tsumura