Abstract: Data from one or more sensors is input to a workflow and fragmented to produce HyperFragments. The HyperFragments of input data are processed by a plurality of Distributed Experts, who make decisions about what is included in the HyperFragments or add details relating to elements included therein, producing tagged HyperFragments, which are maintained as tuples in a Semantic Database. Algorithms are applied to process the HyperFragments to create an event definition corresponding to a specific activity. Based on related activity included in historical data and on ground truth data, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with the current input data to more accurately detect when the specific activity is being carried out.

Abstract: Aspects of the disclosed subject matter are directed to facilitating peer-to-peer data exchange in a common domain. In accordance with one embodiment, a method is provided for obtaining content from one or more peers that are connected to the domain. The method includes registering a peer with a super-peer when a connection to the domain is established. Then, the connecting peer obtains data that describes various network conditions and identifies chunks of content available from other peers. In downloading content from other peers, heuristics are applied to select between available chunks that are potentially encoded at different bitrates. The heuristics account for the network conditions between peers and balance the potential need to quickly access content with the desire to obtain high quality content.

Abstract: A directed graph and a semantic ontology are simultaneously employed to enable tagging of data. Tags from the directed graph contain special properties linking them to a semantic ontology such that activating a node on the graph, will provide specific actions relating to one or more ontologies. Humans or machine executable algorithms can use the directed graph as a classification system, which enables a decision making process to occur, one step at a time. Such an approach enables complex problems sets to be broken down into smaller directed graph processes. Each process can then be either automated using computer executed algorithms, manual using humans, or a combination of both. In this way an n-tiered workflow system can be developed that enables large scale asynchronous and distributed tagging.

Abstract: Data from one or more sensors is input to a workflow and fragmented to produce HyperFragments. The HyperFragments of input data are processed by a plurality of Distributed Experts, who make decisions about what is included in the HyperFragments or add details relating to elements included therein, producing tagged HyperFragments, which are maintained as tuples in a Semantic Database. Algorithms are applied to process the HyperFragments to create an event definition corresponding to a specific activity. Based on related activity included in historical data and on ground truth data, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with the current input data to more accurately detect when the specific activity is being carried out.

Abstract: Aspects of the disclosed subject matter are directed to facilitating peer-to-peer data exchange in a common domain. In accordance with one embodiment, a method is provided for obtaining content from one or more peers that are connected to the domain. The method includes registering a peer with a super-peer when a connection to the domain is established. Then, the connecting peer obtains data that describes various network conditions and identifies chunks of content available from other peers. In downloading content from other peers, heuristics are applied to select between available chunks that are potentially encoded at different bitrates. The heuristics account for the network conditions between peers and balance the potential need to quickly access content with the desire to obtain high quality content.

Abstract: Data from one or more sensors is input to a workflow and fragmented to produce HyperFragments. The HyperFragments of input data are processed by a plurality of Distributed Experts, who make decisions about what is included in the HyperFragments or add details relating to elements included therein, producing tagged HyperFragments, which are maintained as tuples in a Semantic Database. Algorithms are applied to process the HyperFragments to create an event definition corresponding to a specific activity. Based on related activity included in historical data and on ground truth data, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with the current input data to more accurately detect when the specific activity is being carried out.

Abstract: Data from one or more sensors is input to a workflow and fragmented to produce HyperFragments. The HyperFragments of input data are processed by a plurality of Distributed Experts, who make decisions about what is included in the HyperFragments or add details relating to elements included therein, producing tagged HyperFragments, which are maintained as tuples in a Semantic Database. Algorithms are applied to process the HyperFragments to create an event definition corresponding to a specific activity. Based on related activity included in historical data and on ground truth data, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with the current input data to more accurately detect when the specific activity is being carried out.

Abstract: Data from one or more sensors are used to detect an event corresponding to an activity by a person. The detection of the event is then used to loosely model the event to produce an initial event definition. The initial event definition is used in searching historical sensor data to detect other prospective occurrences of the event. Based on the detection of these additional occurrences, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with current sensor data to accurately detect when the activity is being carried out. For example, the activity may be the use of a video imaging device for cheating, and a casino can use the refined event definition with video imaging to detect when people are using the video imaging device for such purposes.

Abstract: Data from one or more sensors are used to detect an event corresponding to an activity by a person. The detection of the event is then used to loosely model the event to produce an initial event definition. The initial event definition is used in searching historical sensor data to detect other prospective occurrences of the event. Based on the detection of these additional occurrences, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with current sensor data to accurately detect when the activity is being carried out. For example, the activity may be the use of a video imaging device for cheating, and a casino can use the refined event definition with video imaging to detect when people are using the video imaging device for such purposes.