Data Stream Grouping Aggregate Algorithms Based on Compound Sliding Window

Grouping aggregate queries Based on Sliding Window is a focus problem in data stream research. Among existing research works, the grouping aggregate algorithms are presented for immediate continuous queries, and they didn¿t take into consideration the effect of data structure in the sliding window on the performance of the algorithms, the data structure in the sliding window is all chained list. In this paper, an incremental grouping aggregate algorithm based on compound sliding window is presented, which is specially designed for periodic continuous queries, this method organizes the basic window in the compound sliding windows into hash table according to their grouping properties, the last aggregate value is preserved in the hash table, for every time that there is new data in the data stream, it will be insert to the basic window, when the basic window is full, they will be scanned using the grouping property value, the aggregate value of its group will be updated. Theoretical analysis and experiment results show that the algorithm that organizes the basic windows in the compound sliding windows into hash tables has good performance.