Abstract: The present disclosure describes methods, systems, and computer program products for importing data to a semantic graph. One computer-implemented method includes collecting, at a client, data from an information source; receiving, from a server, a query for information associated with the semantic graph; and sending, in response to the query, the collected data to the server in accordance to a communication protocol, wherein the collected data is imported to the semantic graph.

Abstract: Data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes series time-stamped frames for each of the sensors and at least one of the sources has two or more associated sensors. The received data can be sorted and processed, for each sensor on a sensor-by-sensor basis, using a sliding window. The sorted and processed data can then be correlated and written into a data storage application. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes series time-stamped frames for each of the sensors and at least one of the sources has two or more associated sensors. The received data can be sorted and processed, for each sensor on a sensor-by-sensor basis, using a sliding window. The sorted and processed data can then be correlated and written into a data storage application. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes series time-stamped frames for each of the sensors and at least one of the sources has two or more associated sensors. The received data can be sorted and processed, for each sensor on a sensor-by-sensor basis, using a sliding window. The sorted and processed data can then be correlated and written into a data storage application. Related apparatus, systems, techniques and articles are also described.

Abstract: Correlated and processed data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes a series of time-stamped frames for each of the sensors. Subsequently, events of interest are identified, in real-time, based on relative positions of the sources within the zone of interest prior to the data being written to a data storage application. Data can then be provided (e.g., loaded, stored, displayed, transmitted, etc.), in real-time, that characterize the events of interest. Related apparatus, systems, techniques and articles are also described.

Abstract: The present disclosure describes methods, systems, and computer program products for importing data to a semantic graph. One computer-implemented method includes collecting, at a client, data from an information source; receiving, from a server, a query for information associated with the semantic graph; and sending, in response to the query, the collected data to the server in accordance to a communication protocol, wherein the collected data is imported to the semantic graph.

Abstract: Correlated and processed data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes a series of time-stamped frames for each of the sensors. Subsequently, events of interest are identified, in real-time, based on relative positions of the sources within the zone of interest prior to the data being written to a data storage application. Data can then be provided (e.g., loaded, stored, displayed, transmitted, etc.), in real-time, that characterize the events of interest. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes series time-stamped frames for each of the sensors and at least one of the sources has two or more associated sensors. The received data can be sorted and processed, for each sensor on a sensor-by-sensor basis, using a sliding window. The sorted and processed data can then be correlated and written into a data storage application. Related apparatus, systems, techniques and articles are also described.

Abstract: A cascading learning system as a normalized semantic search is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application. The request analyzer has a normalization manager, a semantic parser, and a context builder. The normalization manager normalizes the search terms and generates a normalized semantic request based on a context. The request dispatcher and classifier classifies and dispatches the normalized semantic request to a corresponding domain-specific module that generates a prediction with a trained probability of an expected output. The terminology manager receives the normalized semantic request from the request dispatcher and classifier, and manages terminology stored in a contextual network.

Abstract: A method and apparatus for detection of relationships between objects in a meta-model semantic network is described. Semantic objects and semantic relations of a meta-model of business objects are generated from a meta-model semantic network. The semantic relations are based on connections between the semantic objects. A probability model of terminology usage in the semantic objects and the semantic relations is generated. A neural network is formed based on usage of the semantic objects, the semantic relations, and the probability model. The neural network is integrated with the semantic objects, the semantic relations, and the probability model to generate a contextual network. The generated probability model is integrated with semantic objects and neural networks for form parallel networks.