International Business Machines Corporation

Praveen Devarao

Kush Baronj

Trent Gray-Donald

Arnaj Das

Alok Mitra

Smitha Pambalath

Mario D Briggs

Swati Pandey

Kumar Sudarshan

Abu Kaf Qasim

Ashwin Jain

Ashok V Jose

Abhigyan Agrawal

A screen sharing session of a host computing device is detected. The screen sharing session includes a first video stream of a graphical user interface. The screen sharing session is directed to a set of one or more target computing devices. The screen sharing session is monitored for a set of one or more content elements. A first content element of the set of content elements in the first video stream is identified in response to the monitoring. The identification is based on the screen sharing session. The first video stream is intercepted in response to the identification of the first content element. Based on the first content element, the first video stream is altered.

Composite analysis content privacy

A method, apparatus, system, and computer program code for real-time visual guidance. A set of actions for a user instance of an application is captured during an operation of the user instance of the application by a user. A visual guidance of a set of steps performed to use a feature in the user instance of the application is generated in response to a user input requesting assistance with the feature. The visual guidance takes into account the set of actions and includes a context of a graphical user interface present for the user instance of the application displayed on a display system when the user input requesting the assistance with the feature is received during the operation of the user instance of the application. The visual guidance of the set of steps performed to use the feature is displayed on the display system.

Real-time context preserving visual guidance

A method, system and computer program product for forecasting a storage requirement of a database management system (DBMS). The storage-related operations (e.g., create, delete, update) of the applications connected to the DBMS are monitored. The impact on the storage usage of the DBMS based on these storage-related operations performed by the applications is monitored. Furthermore, the applications are categorized into groups of applications based on the monitored storage-related operations. A mathematical model is then built to forecast the storage requirement of the DBMS based on the monitored impact on the storage usage of the DBMS by the monitored storage-related operations of the applications and the categorization of the applications. The storage requirement of the DBMS is then forecasted based on the built mathematical model. In this manner, the storage requirements of the DBMS may be accurately predicted to ensure that there is available storage thereby preventing performance degradation.

Predicting storage requirements of a database management system based on application behavior and behavior of database queries

An embodiment of the present invention evaluates plural expressions. A model is generated and configured to evaluate a plurality of expressions each including one or more expression tokens and indicating a data pattern. The model includes a plurality of nodes with one or more of the nodes associated with an expression token and one or more links between the nodes. The links are associated with information indicating each expression including each expression token associated with nodes connected by the links. Data including one or more data tokens is applied to the model. The nodes of the model are traversed over one or more corresponding links based on the one or more data tokens within the data corresponding to expression tokens associated with the nodes. Expressions corresponding to the data are determined based on the expressions associated with the one or more corresponding links.

Evaluation of plural expressions corresponding to input data

In an approach for semantically translating data. Aspects of an embodiment of the present invention include an approach for semantically translating data, wherein the approach includes a processor selecting a first node. A processor identifies a parent node of the first node. A processor determines that a value of the first node is unknown. A processor responsive to determining that the value of the first node is unknown, annotates the first node to indicate that the first node is at least partially unknown. A processor identifies a common table expression of the first node. A processor determines that the common table expression of the first node matches, within a predetermined threshold, a common table expression of the second node. A processor merges information from the common table expression of the second node with the common table expression of the first node.

Traversing a SPARQL query and translation to a semantic equivalent SQL

A method, computer system, and a computer program product for managing a single database management system (DBMS) which interfaces with both a first and second application program is provided. A first data set corresponding to a first data format may be received by a DBMS interface engine. A second data set corresponding to a second data format may be received by the DBMS interface engine. A holistic data model to represent the first data set and the second dataset may be determined by analyzing the first data set and the second data set using the DBMS interface engine. The holistic data model may be structured by the DBMS interface engine with respect to a single database of the single DBMS.

Managing a single database management system

A method is provided for receiving source code that includes an application programming interface (API) defined query written to communicate with an API. The API is configured to provide access to an object relational model (ORM) for converting, during execution of the source code, the API defined query into a dynamic structured query language (SQL) query comprising SQL statements. The method also includes generating a static SQL query that includes SQL statements. The static SQL query is generated in response to the ORM and to the API, by a computer, prior to the source code being executed.

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Bengaluru, India

Praveen Devarao