Improving Continuous Top Top-K K Queries Over Streaming Data
Abstract: Continuous top-kk query over streaming data is a fundamental problem in database. In this paper, we focus on the sliding window scenario, where a continuous top-kk query returns the top top-kk objects within each query window on the data stream. Existing algorith algorithms ms support this type of queries via incrementally maintaining a subset of objects in the window and try to retrieve the answer from this subset as much as possible whenever the window slides. However, since all the existing algorithms are sensitive to quer queryy parameters and data distribution, they all suffer from expensive incremental maintenance cost. In this paper, we propose a self-adaptive adaptive partition framework to support continuous top-k top query. It partitions the window into sub sub-windows and only maintains a small number of candidates with highest scores in each sub sub-window. window. Based on this framework, we have developed several partition algorithms to cater for different object distributions and query parameters. To our best knowledge, it is the first algorithm that hat achieves logarithmic complexity w.r.t. k for incrementally maintaining the candidate set even in the worstcase scenarios.