Scalable Processing of Spatial Alarms

Abstract

Spatial alarms can be modeled as location-based triggers which are fired whenever the subscriber enters the spatial region around the location of interest associated with the alarm. Alarm processing requires meeting two demanding objectives: high accuracy, which ensures zero or very low alarm misses, and system scalability, which requires highly efficient processing of spatial alarms. Existing techniques like periodic evaluation or continuous query-based approach, when applied to the spatial alarm processing problem, lead to unpredictable inaccuracy in alarm processing or unnecessarily high computational costs or both. In order to deal with these weaknesses, we introduce the concept of safe 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 alarms and motion behavior of the mobile users, which reduce safe period computation costs at the server side. An evaluation of the scalability and accuracy of our approach using a road network simulator shows that the proposed approach offers significant performance enhancements for the alarm processing server.