Abstract: Embodiments relate to systems and methods employing personalized query expansion to suggest measures and dimensions allowing iterative building of consistent queries over a data warehouse. Embodiments may leverage one or more of: semantics defined in multi-dimensional domain models, user profiles defining preferences, and collaborative usage statistics derived from existing repositories of Business Intelligence (BI) documents (e.g. dashboards, reports). Embodiments may utilize a collaborative co-occurrence value derived from profiles of users or social network information of a user.

Abstract: A question is received to be answered by a question answering (QA) system. The question may be a business intelligence question that is expressed in a natural language. The question is parsed. The parsed question is matched to a pattern from a number of patterns. A technical query associated with the matched pattern is processed to retrieve data relevant to the question from a number of data sources. The QA system generates an answer to the question based on retrieved data. In one aspect, the QA system generates answers based contextual information.

Abstract: Embodiments relate to systems and methods employing personalized query expansion to suggest measures and dimensions allowing iterative building of consistent queries over a data warehouse. Embodiments may leverage one or more of: semantics defined in multi-dimensional domain models, user profiles defining preferences, and collaborative usage statistics derived from existing repositories of Business Intelligence (BI) documents (e.g. dashboards, reports). Embodiments may utilize a collaborative co-occurrence value derived from profiles of users or social network information of a user.

Abstract: Situational recommendations in heterogeneous system environment are described herein. An event is received, where the event represents an interaction between an agent and a first resource from a number of resources available at the heterogeneous system environment. At least one impacted situation of a number of situations is determined based on the event. At least one operator from a number of operators is determined to apply on the at least one impacted situation. The at least one operator updates the at least one impacted situation. Recommendation of a second resource from the number of resources is generated based on the updated situation.

Abstract: Embodiments relate to systems and methods employing personalized query expansion to suggest measures and dimensions allowing iterative building of consistent queries over a data warehouse. Embodiments may leverage one or more of: semantics defined in multi-dimensional domain models, user profiles defining preferences, and collaborative usage statistics derived from existing repositories of Business Intelligence (BI) documents (e.g. dashboards, reports). Embodiments may utilize a collaborative co-occurrence value derived from profiles of users or social network information of a user.

Abstract: A question is received to be answered by a question answering (QA) system. The question may be a business intelligence question that is expressed in a natural language. The question is parsed. The parsed question is matched to a pattern from a number of patterns. A technical query associated with the matched pattern is processed to retrieve data relevant to the question from a number of data sources. The QA system generates an answer to the question based on retrieved data. In one aspect, the QA system generates answers based contextual information.

Abstract: Embodiments relate to systems and methods employing personalized query expansion to suggest measures and dimensions allowing iterative building of consistent queries over a data warehouse. Embodiments may leverage one or more of: semantics defined in multi-dimensional domain models, user profiles defining preferences, and collaborative usage statistics derived from existing repositories of Business Intelligence (BI) documents (e.g. dashboards, reports). Embodiments may utilize a collaborative co-occurrence value derived from profiles of users or social network information of a user.

Abstract: Situational recommendations in heterogeneous system environment are described herein. An event is received, where the event represents an interaction between an agent and a first resource from a number of resources available at the heterogeneous system environment. At least one impacted situation of a number of situations is determined based on the event. At least one operator from a number of operators is determined to apply on the at least one impacted situation. The at least one operator updates the at least one impacted situation. Recommendation of a second resource from the number of resources is generated based on the updated situation.

Abstract: A computer-implemented system may include determination of a similarity of a semantic projection of a set of interests of a first user to a semantic projection of a first resource, wherein the semantic projection of the set of interests of the first user and the semantic projection of the first resource conforms to a common semantic projection model. Also included may be determination of a semantic interest of the first user for the first resource in a first context based on the similarity of the semantic projection of the set of the interests of the first user to the semantic projection of the first resource, and determination of an interest measure associated with the first user, the first resource and the first context based on a rating prediction for the first user to the first resource in the first context and on the determined semantic interest of the first user for the first resource in the first context.

Abstract: A computer-implemented system may include determination of a similarity of a semantic projection of a set of interests of a first user to a semantic projection of a first resource, wherein the semantic projection of the set of interests of the first user and the semantic projection of the first resource conforms to a common semantic projection model. Also included may be determination of a semantic interest of the first user for the first resource in a first context based on the similarity of the semantic projection of the set of the interests of the first user to the semantic projection of the first resource, and determination of an interest measure associated with the first user, the first resource and the first context based on a rating prediction for the first user to the first resource in the first context and on the determined semantic interest of the first user for the first resource in the first context.