Oracle International Corporation

Swathi Uppala

Ramchand Raman

Usha Arora

Sreeram Hariharan

Ashish Pathak

Bryan Siu Him So

Gopal Raja Ratnam

Pushkala Nagasuri

Sangeetha Mani

Mohammed Hussain Shirkol

Ganga Mohan Nookala

Kashif Suleman

Krishna Jonnalagadda

Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.

Sensor time series data: functional segmentation for effective machine learning

Systems, methods, and computer-readable mediums are presented that may provide for an interface to a relational database. A request may be received for data stored in a relationship database, wherein the request is received from a spreadsheet application. In response to the request for data stored in the relational database, a plurality of tables in the relational database may be accessed to retrieve the data indicated in the request. The retrieved data may be translated from the plurality of tables of the relational database into a format for output to the spreadsheet application. The translated data may be output to the spreadsheet application.

Interfacing with a relational database for multi-dimensional analysis via a spreadsheet application

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Bengaluru, India

Swathi Uppala