Multi-Granularity Aggregation Index for Data Stream

Aggregate information is important for data stream processing systems, especially, to get any user-specified time window queries and evolution analysis over data stream. To solve this problem, in this paper, we propose an integrated structure for managing summarized information of snapshots under geometry timeframe, which facilitates analyzing the temporal evolving of data stream. By using our method, the cost of update operations and the errors can be controlled within acceptable scope. Evaluation shows the our structure can respond to arbitrary time window aggregate queries within small errors, efficiently.