International Business Machines Corporation

Leo Kluger

Jeremy R Fox

Craig M Trim

Mary E Rudden

William Reilly

Kelley Anders

Imren Johar

Roberto Battaglini

Abhishek Basu

Nikolay Kadochnikov

Tomomi Yoshioka

Daksh Sikri

Thevuthasan Senthuran

Aman Buttan

Aboozar Hadavand

Sampoorna Dasgupta

A computer-implemented method for generating a description of a target skill set using domain specific language, a computer program product, and a system. Embodiments may comprise, on a processor, ingesting a data set related to the target skill from a data store, semantically analyzing the data set to generate a skill ontology, generating a hyperplane to separate one or more priority skills from among the plurality of related skills, generating a description for the target skill from the one or more priority skills, and presenting the generated description to a user. The skill ontology may include relationships between the target skill and a plurality of related skills.

Hyperplane optimization in high dimensional ontology

A method of analyzing one or more asset repositories to determine a developer's competency and ability to collaborate in a team environment. The method includes obtaining metadata artifacts as documents from one or more asset repositories. The documents are represented using a tree structure that has nodes to form a knowledge graph that is suitable for input vectorization. Each node is configured in the tree structure to represent a content region within the one or more documents, with a root node representing the entire document and each node representing an aggregation of all its children nodes. One or more computing devices are used to receive an input vector that includes a set of descriptive statistics about data in asset repositories and a vectorized form of one or more concepts. The developer's competency and ability to collaborate in a team environment is automatically estimated using output values of a trained neural network.

System and method for analysis of workplace churn and replacement

Embodiments for cognitively identifying a propensity for obtaining white space target entities by a processor. Similarities between one or more potential target entities and current entities may be compared according to structured data and unstructured data. A propensity to engage in a transaction agreement may be determined for the one or more potential target entities according to the similarities and historical transaction agreements.

Cognitively identifying a propensity for obtaining prospective entities

A computer-implemented method includes: receiving, by a computing device, information identifying the presence of a crowdsourcing event; receiving, by a computing device, indirect crowdsourcing data including user device activity from a plurality of user devices within a geofence associated with the crowdsourcing event; generating, by the computing device, indirect crowdsourcing data records based on the indirect crowdsourcing data; receiving, by the computing device, a query relating to the crowdsourcing event; generating, by the computing device, a query response in response to the query based on the indirect crowdsourcing data records; and providing by the computing device, the query response.

Indirect crowdsourcing by associating data from multiple data sources

An embodiment of the invention provides a method for monitoring for and detecting a leak in a line where a mobile detector having an audio sensor and an odor sensor transiting along the line. A sample audio reading is obtained by the audio sensor; and, a sample odor reading is obtained by the odor sensor. A processor compares the sample audio reading to a baseline audio reading to determine whether the line is leaking. The processor compares the sample odor reading to a baseline odor reading to determine whether the line is leaking. The location of the mobile detector is identified by a GPS device on the mobile detector when the processor determines that the line is leaking. The location of the mobile detector, the sample audio reading, and the sample odor reading is sent to a central monitoring center by a communications device on the mobile detector.

Automated gas detection and reporting system

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Spring Valley, NY, United States of America

Leo Kluger