Abstract: A semantic extraction system leverages domain expert knowledge, to impart meaningful business information aiding ordinary knowledge consumers in understanding large/complex data volumes and models thereof. Certain embodiments may comprise a layered structure comprising an information uplifting layer, a semantic processing layer, and a visual representation layer. Referencing domain knowledge model(s) created by human domain experts, the information uplifting layer extracts and maintains meaningful information in a semantic structure. The semantic processing layer then processes this extracted information for various different business analysis purposes. Finally, the visual representation layer allows the analyzed and aggregated information to be arranged and visualized via a range of interactive tools. The overall layered structure is powered by the domain knowledge models, which capture specialized knowledge from experts in different domains.

Abstract: A method, medium, and system to receive an event stream, the event stream including a plurality of events, the events being semantically modeled; receive domain insights specifying a relationship between two events, the domain insights being semantically modeled and defined by a specified time limit and a comparison of event attributes using the specified time limit with a logical operator; retrieve stored representations of events referenced in the received domain insights; process the event stream, the received domain insights, and the retrieved stored events to produce a temporal processing result; and store the temporal processing result.

Abstract: A method, medium, and system to receive an event stream, the event stream including a plurality of events, the events being semantically modeled; receive domain insights specifying a relationship between two events, the domain insights being semantically modeled and defined by a specified time limit and a comparison of event attributes using the specified time limit with a logical operator; retrieve stored representations of events referenced in the received domain insights; process the event stream, the received domain insights, and the retrieved stored events to produce a temporal processing result; and store the temporal processing result.

Abstract: Examples of application customization through linked embedded areas are provided herein. Linked embedded areas can be used to customize an application in a way that allows both user customization and application provider application updates. In response to receiving a request to initiate an application, static content portions of the application can be accessed, and embedded areas of the application can be identified. Embedded areas are linked to content variants that include information representing content displayable in the embedded area. The content variant linked to the embedded area of the application can be retrieved, and a user-specific instance of the application can be generated. The user-specific instance includes the static content portions and the embedded area. The content displayed in the embedded area reflects the content variant.

Abstract: A semantic extraction system leverages domain expert knowledge, to impart meaningful business information aiding ordinary knowledge consumers in understanding large/complex data volumes and models thereof. Certain embodiments may comprise a layered structure comprising an information uplifting layer, a semantic processing layer, and a visual representation layer. Referencing domain knowledge model(s) created by human domain experts, the information uplifting layer extracts and maintains meaningful information in a semantic structure. The semantic processing layer then processes this extracted information for various different business analysis purposes. Finally, the visual representation layer allows the analyzed and aggregated information to be arranged and visualized via a range of interactive tools. The overall layered structure is powered by the domain knowledge models, which capture specialized knowledge from experts in different domains.