Resource Sharing in Continuous Extreme Values Monitoring on Sliding Windows

We address the problem of resource sharing in continuous extreme values monitoring (MAX or MIN) over sliding windows. Firstly, we develop an effective pruning technique called key points (KP) to minimize the number of elements to be kept for all queries. It can be shown that on average the cardinality of KP satisfies M = O(logN), where N is the number of points contained in the widest window. An efficient algorithm called MCEQP is proposed for continuously monitor K queries with different sliding window width. Analytical analysis and experimental evidences show the efficiency of proposed approach both on storage reduction and efficiency improvement.