International Business Machines Corporation

Kunjavihari Madhav Kashalikar

Yannick Saillet

Namit Kabra

Jean-Claude Mamou

Rong Qu

Kishore Patel

Cassio S Dos Santos

Hong X Zhang

Albert Maier

Smitkumar Narotambhai Marvaniya

Peter Gerstl

Bernhard Mitschang

Neerju Neerju

Likhitha Maddirala

Cassio Dos Santos

Cassio S D Santos

Ketki Ramesh Purandare

A method of data classification includes: identifying a cluster of data classes; classifying columns of a current data set; identifying the cluster in the current data set; determining, based on the cluster, an expected column is missing from the current data set; determining a neighboring data set; identifying the expected column in the neighboring data set; classifying the expected column in the neighboring data set; creating a new data class in the current data set; and classifying an unclassified column in the current data set or the neighboring data set with the new data class.

Deep data classification using governance and machine learning

Aspects of the present disclosure relate to controlling virtual reality (VR) content displayed on a VR head mounted display (HMD). Communication can be established between a computer system, a VR HMD, and a mobile device. A user input configured to control VR content displayed on a display of the VR HMD can be received on the mobile device. The VR content displayed on the VR HMD can then be controlled based on the user input received on the mobile device.

Mobile device based VR content control

Methods and systems for data quality evaluation are disclosed. A method includes: receiving, by a computing device, at least one data set and a list of rule expressions to bind; building, by the computing device, candidate binding combinations between columns of the at least one data set and variables of each rule expression in the list of rule expressions; building, by the computing device, a new bound rule expression candidate based on the candidate binding combinations; generating, by the computing device, a new bound rule expression based on the new bound rule expression candidate and a data transformation applied to at least one of the columns of the at least one data set; and storing, by the computing device, the new bound rule expression.

Data quality evaluation

The present disclosure relates to a method for accessing data of one or more data sources using a discovery engine. The method comprises: determining a discovery space content from initial metadata of a data source indicated in a data exploration request. The discovery space content may be rendered. The rendered content may be used for determining a set of one or more tasks for generating further metadata from at least part of the data of the data source, wherein the set of tasks comprises a combination of API calls. The API calls may be issued to the discovery engine. Discovery results of the issued API calls may be received. A data discovery status may be devalued using the discovery results. The discovery space content may be augmented using the further metadata and the data discovery status. The augmented discovery space content may be rendered for receiving further API calls.

Method and system for a discovery engine

A method may include applying periodically a data validation rule to data transformed through a data processing system, wherein the data validation rule applies aspects selected from a group consisting of data value range, specific data values, and relationship with other data entries; responsive to detecting a violation of the data validation rule, identifying a portion of the transformed data for lineage assessment; examining the identified transformed data iteratively upstream at a previous transformation node in a lineage graph, until the method detects a node where the violation of the data validation rule can't be reproduced; creating a separate node in a distributed network for each of the previous transformation nodes in the lineage graph; and identifying the separate node in the distributed network introducing the violation of the data validation rule.

Systematic approach to determine source of data quality issue in data flow in an enterprise

A view schema is generated from a view definition, wherein the view schema includes one or more relational views that are created by translating one or more object queries in the view definition into one or more relational queries. A report design schema is generated from the view definition by: creating one or more report tables that correspond to the one or more relational views of the view schema and extending the one or more report tables with one or more primary key constraints and one or more foreign key constraints. A reporting model is generated by reverse engineering the report design schema, wherein the reporting model includes the one or more primary key constraints and the one or more foreign key constraints. A report is generated using the reporting model, wherein the report includes the one or more primary key constraints and the one or more foreign key constraints.

Reporting tools for object-relational databases

Growing community of inventors

Hudson, MA, United States of America

Kunjavihari Madhav Kashalikar