International Business Machines Corporation

Airbnb, Inc.

Benjamin Halberstadt

Roger Kenneth Hecker

Boris Melamed

Michael Fankhauser

Joshua Fox

Shlomo Steinhart

Ortal Nizri

Marc N Haber

Erel Sharf

Ilan D Prager

Guy Yitzhaki

Hayden Marchant

Efrat Hexter

Eliezer Israel

Yehuda Kossowsky

Zvi Schreiber

Ruth M Amaru

Michael Frankhauser

Joanne Friedman

Rannen Meir

Joseph Edelstein

Ziv Zalman Hellman

Ruthie M Amaru

David Cavenor

Ahuva Hazan-Fuchs

Shira Gellman

Text including at least a first term can be presented on a display. An enterprise glossary can be queried to identify at least a first curation parameter assigned to the first term. A first score can be determined for the first term based, at least in part, on the first curation parameter assigned to the first term. The first score can be assigned to a first data value. The first data value can be presented on the display.

Identifying and scoring data values

Text including at least a first term can be presented on a display. A first score can be determined for the first term. The first score can be based, at least in part, on a lifecycle of the first term. The first score can be assigned to a first data value. The first data value can be presented on the display.

An example system includes a processor that can generate a first lineage graph based on a first set of monitored assets and processes used to produce a data asset. The processor can detect a data quality issue at the data asset. The processor can also generate a second lineage graph including a second set of monitored assets and processes that produced the data asset with the data quality issue. The processor can further compare the second lineage graph with the first lineage graph to detect a potential root cause of the data quality issue. The processor can also further modify an asset or process corresponding to the potential root cause of the data quality issue.

Detecting potential root causes of data quality issues using data lineage graphs

A principal model is configured within a memory to facilitate automatic generation of at least one resource for use by a computer-executable application. At least one model item required for a task that is absent from the principal model is identified. The absent model item is instantiated in a decoration model that operates with the principal model to augment operational functionality of the principal model.

Model augmentation in a model-driven application development environment

According to an aspect, data asset reconstruction includes receiving a data lineage for a data asset, where the data lineage identifies a data source connected to the data asset by an intermediate process. It is determined whether the intermediate process can be used to reconstruct a lost data asset. Based on determining that the intermediate process can be used to reconstruct the lost data asset, reconstructing the lost data asset with the intermediate process and the data source.

Data asset reconstruction

Text including at least a first term can be presented on a display. An enterprise glossary is queried to identify other terms that match the first term. Data assets to which each of the other terms are linked and which include data values for the other terms can be identified. A first score indicating a level of relevance of the respective data asset to an enterprise is assigned to each of the data assets. A frequency distribution of the data values in the data assets is determined. Based at least on the first scores indicating the level of relevance of the respective data assets to the enterprise and the frequency distribution of the data values in the data assets, second scores are assigned to each of the data values. A plurality the data values which are assigned highest of the second scores are presented on the display.

Transform governance rules for a data asset to apply to a set of related data assets. Establishing a governance rule over a first data asset based on a second governance rule applied to either an upstream or a downstream data asset.

Establishing governance rules over data assets

Optimizing operational costs in a computing environment includes identifying high-cost jobs that are executed to generate one or more reports in the computing environment, identifying one or more reports the generation of which is dependent on the execution of the high-cost jobs, and culling at least a first job from among the high-cost jobs, in response to determining that a benefit achieved from the reports that depend on the first job does not justify costs associated with generating the reports.

Eliminating execution of jobs-based operational costs of related reports

A principal model is configured to facilitate automatic generation of at least one resource for use by a computer-executable application. The principal model includes a group of classes, references, attributes, and associations between any of the classes. At least one model item required for a task that is absent from the principal model is identified. The absent model item is instantiated in a decoration model that operates with the principal model to augment operational functionality of the principal model. The decoration model includes a class, a reference, and an attribute for any corresponding one of the group of classes, references, and attributes of the principal model.

Growing community of inventors

Jerusalem, Israel

Benjamin Halberstadt