Continuous Skyline Query Processing Algorithm Based on Sharding Technology Under Sliding Window Model

Abstract

Continuous query processing over sliding window is an important problem in stream data management. Given the sliding window W and the continuous query q, q monitors to the multidimensional data objects in the window. When the window slides, q returns all the skyline objects in the window. Many scholars have carried out researches on such problems. The core idea is to delete objects that cannot be query results by using the temporal sequence relationship between objects in the window, and when the window slides, the algorithm can find the query results from the rest. However, the algorithm is sensitive to data timing relationships such as problems. In the worst case, the size of the candidate object equals to the size of the data in the window. In this paper, we propose a partition-based framework to support continuous skyline query over sliding window. It partitions the window into a group of sub-window, and maintain the skyline objects in each sub-window. In this way, it could effectively overcome the impact the object arrived order to the algorithm performance. In addition, we propose a self-adaptive algorithm to partition the window according to the distribution of streaming data. A large number of experiments prove the effectiveness and high efficiency of the proposed algorithm.