International Business Machines Corporation

Anuja Deedwaniya

Yuk Lung Chan

Robert M Abrams

Thomas Edwin Murphy, Jr

Mark A Nelson

Kevin H Peters

Louis R Ruggiero

Richard Tallman

Eric Lynn Porter

Barry L Lichtenstein

David J Sudlik

Chauho Magdalen Leung

Gisela C Cheng

Olivier Boehler

Shayne M Grant

Zamir G Gonzalez

Jagadish B Kotra

Examples described herein provide a computer-implemented method that includes receiving a list of objects with anomaly detection results associated therewith. The method further includes generating a resource graph, wherein the resource graph comprises at least two nodes, each of the at least two nodes being associated with one of the objects. The method further includes determining a relationship between the at least two nodes. The method further includes assigning an anomaly score to at least one of the objects. The method further includes calculating for at least one of the nodes, an overall anomaly score for the at least one of the nodes based on the anomaly score for the at least one of the objects and based on the anomaly score for another node related to the at least one of the nodes.

Aggregating results from multiple anomaly detection engines

Examples described herein provide a computer-implemented method that includes defining a key performance indicator associated with a non-instrumented object of a processing system. The method further includes determining a current anomaly level of the key performance indicator for an instrumented object having a relationship with the non-instrumented object. The method further includes assigning an anomaly level to the non-instrumented object based on the current anomaly level.

Assigning an anomaly level to a non-instrumented object

Examples described herein provide a computer-implemented method that includes detecting an anomaly associated with an object of a computer system and determining an importance classification of the object. An object relationship of the object is determined with respect to one or more other objects of the computer system. An impact score of the anomaly is determined based on the importance classification and the object relationship. An anomaly report is output with the impact score.

Anomaly detection with impact assessment

Embodiments of the disclosure relate to processing email having symbolic variables in the address. Aspects include receiving, by an email server, an email comprising an email address for an intended recipient and determining whether the email address includes a symbolic variable. Based on determining that the email address includes the symbolic variable, the symbolic variable from the email address are extracted, an email address for an additional recipient by querying a database based on the symbolic variable is identified, and the email is transmitted to the email address of the intended recipient and the email address of the additional recipient. Based on determining that the email address does not include the symbolic variable, the email is transmitted to the email address of the intended recipient.

Symbolic variables within email addresses

A method, system, and computer program product for intelligent command prediction are provided. The method includes determining a command prediction preference associated with a user from user profile data, and selecting one or more command history repositories responsive to the command prediction preference. The one or more command history repositories include command history data collected from a plurality of users and classification data associated with the plurality of users. The method also includes calculating command probabilities for commands in the command history data of the selected one or more command history repositories as a function of the classification data associated with the plurality of users in relation to the user. The method additionally includes presenting a next suggested command as a command from the command history data of the selected one or more command history repositories with a highest calculated command probability.

Intelligent command prediction

A system for packaging a dynamically linked computer program function includes a compiler configured to compile a source file iteratively to create a plurality of corresponding object files, the source file associated with a specific computer program function having an established an attribute, with each attribute exhibiting a plurality of at least one of variations, characteristics and parameters, wherein each iteration of the compiling the source file results in the plurality of object files having the specific computer program function but with each object file having distinct attributes from one another; and a linker configured to link the plurality of resulting object files to create a single executable file, such that the single executable file comprises different versions of the specific computer program function; and wherein the single executable file is configured to facilitate choice of a selected version of the specific computer function.

Multi-attribute dynamic link library packaging

A method of packaging a dynamically linked computer program function comprising: establishing an attribute, each attribute exhibiting a plurality of at least one of variations, characteristics and parameters associated with the dynamically linked computer program function; obtaining a source file associated with the dynamically linked computer program function; and compiling and linking the source file iteratively to create a single executable file based on the at least one of variations, characteristics, and parameters for each attribute. The single executable file is configured to facilitate choice of a selected version of the function based on a particular at least one of variations, characteristics, and parameters for each attribute.

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Poughkeepsie, NY, United States of America

Anuja Deedwaniya