Abstract: The present subject matter discloses a system and a method for detecting an event from time-series data sequences. The system receives time-series data sequences generated by sensors, wherein the time-series data sequences comprise sample points. The system pairs the sample points with one another for determining pairs of the sample points. The system computes Euclidean distances and angles between the sample points for determining distance matrix and angle matrix corresponding to the sample points. Further, the system determines global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points represent 2D shape histogram for the time-series data sequence. Further, the system concatenates the 2D shape histogram for each time-series data sequence to generate a concatenated shape histogram. Finally the system matches the concatenated shape histogram to pre-stored shape histograms for determining the event.

Abstract: Disclosed is a system and method for converting a voluminous stream of unstructured short text messages into event-objects of specific event type that may be of potential interest to users at far away locations. The method of structuring involves detecting long tail of events in spite of their sparsity. This is followed by extracting and correlating detected short text messages that describe same event type to create structured event objects.

Abstract: The present subject matter discloses a system and a method for detecting an event from time-series data sequences. The system receives time-series data sequences generated by sensors, wherein the time-series data sequences comprise sample points. The system pairs the sample points with one another for determining pairs of the sample points. The system computes Euclidean distances and angles between the sample points for determining distance matrix and angle matrix corresponding to the sample points. Further, the system determines global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points represent 2D shape histogram for the time-series data sequence. Further, the system concatenates the 2D shape histogram for each time-series data sequence to generate a concatenated shape histogram. Finally the system matches the concatenated shape histogram to pre-stored shape histograms for determining the event.

Abstract: The present invention envisages a system and method for converting a voluminous stream of unstructured short text messages into event-objects of specific event type that may be of potential interest to users at far away locations. The method of structuring involves detecting long tail of events in spite of their sparsity. This is followed by extracting and correlating detected short text messages that describe same event type to create structured event objects.