Abstract: Architecture introduces a new pattern operator referred to as called an augmented transition network (ATN), which is a streaming adaptation of non-reentrant, fixed-state ATNs for dynamic patterns. Additional user-defined information is associated with automaton states and is accessible to transitions during execution. ATNs are created that directly model complex pattern continuous queries with arbitrary cycles in a transition graph. The architecture can express the desire to ignore some events during pattern detection, and can also detect the absence of data as part of a pattern. The architecture facilitates efficient support for negation, ignorable events, and state cleanup based on predicate punctuations.

Abstract: Architecture introduces a new pattern operator referred to as called an augmented transition network (ATN), which is a streaming adaptation of non-reentrant, fixed-state ATNs for dynamic patterns. Additional user-defined information is associated with automaton states and is accessible to transitions during execution. ATNs are created that directly model complex pattern continuous queries with arbitrary cycles in a transition graph. The architecture can express the desire to ignore some events during pattern detection, and can also detect the absence of data as part of a pattern. The architecture facilitates efficient support for negation, ignorable events, and state cleanup based on predicate punctuations.

Abstract: Architecture introduces a new pattern operator referred to as called an augmented transition network (ATN), which is a streaming adaptation of non-reentrant, fixed-state ATNs for dynamic patterns. Additional user-defined information is associated with automaton states and is accessible to transitions during execution. ATNs are created that directly model complex pattern continuous queries with arbitrary cycles in a transition graph. The architecture can express the desire to ignore some events during pattern detection, and can also detect the absence of data as part of a pattern. The architecture facilitates efficient support for negation, ignorable events, and state cleanup based on predicate punctuations.

Abstract: Architecture that obtains and utilizes collections of geographically-tagged data to discover optimal vantage points for viewsheds of entities of interest such as physical entities and conceptual entities such as landmarks, sunset, skyline, etc. The disclosed architecture discloses the utilization of at least geo-tagged image data to discover relationships between a combination of concrete entities and/or abstract concepts, and techniques for surfacing such relationships to users. The data can be crowd-sourced geo-tagged image data that are mined from social content and which can be observed or experienced from a certain location/area.

Abstract: Architecture introduces a new pattern operator referred to as called an augmented transition network (ATN), which is a streaming adaptation of non-reentrant, fixed-state ATNs for dynamic patterns. Additional user-defined information is associated with automaton states and is accessible to transitions during execution. ATNs are created that directly model complex pattern continuous queries with arbitrary cycles in a transition graph. The architecture can express the desire to ignore some events during pattern detection, and can also detect the absence of data as part of a pattern. The architecture facilitates efficient support for negation, ignorable events, and state cleanup based on predicate punctuations.

Abstract: Architecture introduces a new pattern operator referred to as called an augmented transition network (ATN), which is a streaming adaptation of non-reentrant, fixed-state ATNs for dynamic patterns. Additional user-defined information is associated with automaton states and is accessible to transitions during execution. ATNs are created that directly model complex pattern continuous queries with arbitrary cycles in a transition graph. The architecture can express the desire to ignore some events during pattern detection, and can also detect the absence of data as part of a pattern. The architecture facilitates efficient support for negation, ignorable events, and state cleanup based on predicate punctuations.

Abstract: An extensibility framework that allows a user to write user-defined modules that include user-defined operators (UDO) and user-defined aggregators (UDA) in a non-temporal fashion without the need to worry about temporal attributes of events (or event types). The temporal domain is managed on behalf of the user, and allows the user to write operators and aggregates in the temporal data streaming domain as well as to port existing libraries of non-temporal UDOs/UDAs to the temporal data streaming domain. Temporal attributes and event types are managed for non-temporal UDOs/UDAs by the extensibility framework on behalf of the UDO/UDA writer. Windows can be employed to bridge the gap between the non-temporal domains and temporal domains. Support for complex event processing (CEP) is provided in UDOs/UDAs for base classes related to a CEP operator, CEP aggregate, CEP time sensitive operator, and CEP time sensitive aggregate.

Abstract: An extensibility framework that allows a user to write user-defined modules that include user-defined operators (UDO) and user-defined aggregators (UDA) in a non-temporal fashion without the need to worry about temporal attributes of events (or event types). The temporal domain is managed on behalf of the user, and allows the user to write operators and aggregates in the temporal data streaming domain as well as to port existing libraries of non-temporal UDOs/UDAs to the temporal data streaming domain. Temporal attributes and event types are managed for non-temporal UDOs/UDAs by the extensibility framework on behalf of the UDO/UDA writer. Windows can be employed to bridge the gap between the non-temporal domains and temporal domains. Support for complex event processing (CEP) is provided in UDOs/UDAs for base classes related to a CEP operator, CEP aggregate, CEP time sensitive operator, and CEP time sensitive aggregate.

Abstract: Architecture introduces a new pattern operator referred to as called an augmented transition network (ATN), which is a streaming adaptation of non-reentrant, fixed-state ATNs for dynamic patterns. Additional user-defined information is associated with automaton states and is accessible to transitions during execution. ATNs are created that directly model complex pattern continuous queries with arbitrary cycles in a transition graph. The architecture can express the desire to ignore some events during pattern detection, and can also detect the absence of data as part of a pattern. The architecture facilitates efficient support for negation, ignorable events, and state cleanup based on predicate punctuations.