International Business Machines Corporation

Kirill M Osipov

James R Kraemer

Robert R Friedlander

Robin Lougee

Achille Belly Fokoue-Nkoutche

Aditya Anand Kalyanpur

Kavitha Srinivas

Min Wang

Mark Wayne VanderWiele

German Sergio Goldszmidt

Robin Lougee-Heimer

A method, system and computer program product for enhancing privacy of event data. Event sensor data (e.g., body temperature, heart rate data) is received and analyzed by a subscriber to form a probability of assigning a user identity to the received event sensor data. The user of the event sensor data is then assigned with a temporary membership to a cohort (related group of users that share common characteristic(s) or experience(s)) to hide the identity of the user in response to the probability of assigning the user identity to the received event sensor data exceeding a threshold. Actions may then be performed based on the temporary membership to the cohort in order to ensure that the probability of assigning a user identity to the received event sensor data does not exceed the threshold. In this manner, privacy of the user's sensitive data is enhanced.

Enhancing privacy of sensor data from devices using ephemeral cohorts

A computer implemented method, program product, and/or system allocate human resources to a cohort. At least one attribute held by each member of a group of human resources is identified. A request is received, from a planned cohort, for multiple human resources that collectively possess a set of predefined attributes, wherein no single human resource possesses all of the predefined attributes. The set of human resources that satisfies the request is identified and assigned to the planned cohort.

Generating candidate inclusion/exclusion cohorts for a multiply constrained group

A method, system and computer program product for efficiently utilizing resources in processing recurring events. Recurring events from one or more event type sources (heart rate monitor) sensed by various sensors are detected. An event type (e.g., heart rate data) for each detected recurring event is identified. A user profile associated with the identified event type is then analyzed to determine whether the associated sensed recurring event is to be transmitted to the event processing device. The user profile contains a set of conditions which need to be satisfied before the recurring event is transmitted to the event processing device. If the set of conditions in the user profile is not satisfied, then the recurring event is not transmitted to the event processing device. In this manner, by not transmitting the recurring event, power and consumption utilization are reduced for both the event emitting device and the event processing device.

Utilizing a profile to prevent recurring events from being transmitted to the event processing device that are not necessary to be processed by event processing device

A processor-implemented method, system and/or computer program product allocates multiple resources from multiple organizations. A series of requests for multiple resources from multiple organizations is received. The multiple resources are required to accomplish a specific task, and each of the multiple resources is assigned a probability of consumption. Probabilities of availability of the multiple resources are then determined and transmitted to the organizations.

Probabilistic optimization of resource discovery, reservation and assignment

A computer implemented method, system and/or computer program product allocate multiple resources from multiple organizations. A series of requests for multiple resources from multiple organizations is received. The multiple resources are required to accomplish a specific task, and each of the multiple resources is assigned a probability of consumption. Probabilities of availability of the multiple resources are then determined and transmitted to the organizations.

Methods, apparatus and systems, including computer program products, for reducing an error rate when mapping entities between a first ontology and a second ontology. One or more of a general language dictionary and an industry-specific dictionary are provided. Natural language processing of the first ontology is performed to identify one or more candidate relationship entities in the first ontology. Each candidate relationship entity includes a compound name having two or more semantic labels, and each candidate relationship entity has a name that neither exists in the general language dictionary or the industry-specific dictionary. Each of the one or more candidate relationship entities in the first ontology is mapped to one or more entities in the second ontology using one or more configurable computer-implemented mapping algorithms.

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Ormond Beach, FL, United States of America

Kirill M Osipov