Research on K maximum dominant skyline and ε-GA algorithm based on data stream environment

摘要

With the continuous development of database technology, the data volume that can be stored and processed by the database is increasing. How to dig out information that people are interested in from the massive data is one of the important issues in the field of database research. This article starts from the user demand analysis, and makes an in-depth study of various query expansion problems of skylines. Then, according to different application scenarios, this paper proposes efficient and targeted solutions to effectively meet the actual needs of people. Based on k- representative skyline query problem in the data stream environment, a k-representative skyline selection standard k-LDS is presented which is applicable for data stream environment. k-LDS hopes to select the skyline subset with the largest dominant area (containing k skyline tuples only) as k- representative skyline set in data stream. And for the 3-dimensionalal and multidimensional k-LDS problems, this paper also proposes the approximation algorithm, namely GA algorithm. Finally, through the experiment, it is proved that k-LDS is more suitable for the data stream environment, and the algorithm proposed can effectively solve k-LD problems under the data stream environment.