International Business Machines Corporation

Mercan Topkara

Jennifer Ceil Lai

Stephen Peter Wood

Shimei Pan

Jeffrey Scott Boston

Anni Rosa Coden

Zhen Wen

Jie Lu

Yurdaer Nezihi Doganata

Arun Hampapur

Ahmet Emir Dirik

Bemice E Rogowitz

William D Pfelfer, Jr

A method includes generating, as executed by a processor on a computer, a plurality of topic-specific user knowledge models for each user of a plurality of users, each topic-specific user knowledge model representing a level of knowledge possessed by a respective user on a single topic from a set of globally defined topics shared among the plurality of users, generating a plurality of topic-specific expert knowledge models, each topic-specific expert knowledge model representing an aggregate level of knowledge possessed by a plurality of expert users on a single topic from a set of globally defined topics shared among the plurality of users, comparing the topic-specific user knowledge model of the first user with the topic-specific expert knowledge model for a respective topic to determine a distance between a user knowledge level and an aggregate expert knowledge level for the topic.

Systems, methods, and computer program products for expediting expertise

A system includes a user model module that generates a plurality of topic-specific user knowledge models for each user of a plurality of users, each topic-specific user knowledge model representing a level of knowledge possessed by a respective user on a single topic from a set of globally defined topics shared among the plurality of users, a expertise model building module that generates a plurality of topic-specific expert knowledge models, each topic-specific expert knowledge model representing an aggregate level of knowledge possessed by a plurality of expert users on a single topic from a set of globally defined topics shared among the plurality of users, and a processor of a computer that executes instructions for comparing the topic-specific user knowledge model of the first user with the topic-specific expert knowledge model for a respective topic to determine a distance between a user knowledge level and an aggregate expert knowledge level for the topic.

Provenance techniques are disclosed for managing a history of a meeting. For example, a method for managing a history associated with a meeting comprises the following steps. Data associated with the meeting is collected. Provenance data is generated based on at least a portion of the collected data, wherein the provenance data is indicative of a lineage of one or more data items. A provenance graph is generated that defines a visual representation of the generated provenance data, wherein graph elements comprise one or more nodes and one or more edges between nodes, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. One or more applications are associated with at least one graph element and are selectable to invoke functionality. The generated provenance graph is stored in a repository for use in analyzing the meeting.

Management of a history of a meeting

A system including a global topic model building module that builds a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, a user model building module that builds a plurality of user models, each user model being built based on an activity of a respective user, an expertise model building module that builds an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, a processor to compare a user model of the first user with the expertise model for the topic; and an expertise assessment and learning recommendation module that recommends an activity associated with the set of data to the first user based on the comparison.

A method including generating a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, the global topic model including a topic representation for a topic, generating a plurality of user models, each user model being generated based on the activity of a respective user, generating an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, comparing a user model of the first user with the expertise model for the topic, the comparing being performed by a processor of a computer system, and recommending an activity associated with the set of data to the first user based on the comparison.

An approach that evaluates an effectiveness of a monitoring system is provided. In one embodiment, there is a performance evaluation tool, including an evaluation component configured to receive visual media at a monitoring system monitored by a set of users, and evaluate a monitoring performance of the set of users in response to a simulated alert within the visual media. The performance evaluation tool further comprises an analysis component configured to predict an effectiveness of the monitoring system based on the performance of the set of users in response to the simulated alert within the visual media.

Evaluating an effectiveness of a monitoring system

Automatically generating a meeting digest of a set of meetings is provided. A set of topics of interest to the parties to the set of meetings is detected utilizing a user model associated with a user that is based on at least one of communications, relationships, and roles of the parties to the set of meetings. Topic-related content associated with the set of topics of interest to the parties is extracted from meeting data corresponding to the set of meetings. Then, the meeting digest of the set of meetings is generated using the topic-related content associated with the set of topics of interest to the parties extracted from the meeting data corresponding to the set of meetings.

Automatically generating a personalized digest of meetings

Techniques are disclosed for automatic static summarization of videos. For example, a method of creating a static summary of a video comprises the following steps. Shots in the video are detected, wherein the detected shots are frames of the video having a correlation. The detected shots are clustered into clusters based on similarity. The clusters of shots are ranked. At least a portion of the shots are selected based on cluster ranking for inclusion in the static summary. The static summary is generated by combining thumbnail images of the selected shots. Prior to the ranking step, the method may further comprise detecting a presence of slides in any of the shots, and the ranking of a given shot is based in part on whether the shot is a slide. By way of example, such static summaries can be shared in emails and in calendar applications.

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Thornwood, NY, United States of America

Mercan Topkara