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Specifying and detecting spatio-temporal events in the internet of things
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Mondrian tree: A fast index for spatial alarm processing
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Spatial alarms can be modeled as location-based triggerswhich are fired whenever the subscriber enters the spatial region aroundthe location of interest associated with the alarm. Alarm processing requiresmeeting two demanding objectives: high accuracy, which ensureszero or very low alarm misses, and system scalability, which requireshighly efficient processing of spatial alarms. Existing techniques like periodicevaluation or continuous query-based approach, when applied tothe spatial alarm processing problem, lead to unpredictable inaccuracyin alarm processing or unnecessarily high computational costs or both.In order to deal with these weaknesses, we introduce the concept ofsafe period to minimize the number of unnecessary spatial alarm evaluations,increasing the throughput and scalability of the server. Further,we develop alarm grouping techniques based on locality of the alarmsand motion behavior of the mobile users, which reduce safe period computationcosts at the server side. An evaluation of the scalability andaccuracy of our approach using a road network simulator shows that theproposed approach offers significant performance enhancements for thealarm processing server.