Abstract: The k-dominated skyline algorithm weakens the domination relationship between data points and is more suitable for high-dimensional data. k-dominated skyline bodies are suitable for multiple users to query with the k-dominated skyline algorithm, but the existing solution algorithms need to be improved in terms of time efficiency and code scalability. Therefore, this paper proposes an optimization algorithm for solving k-dominated skyline bodies. This algorithm stores the candidate set and the intermediate set for each user separately, and stores the non-k-dominated skyline points in the candidate set to the intermediate set of the corresponding user in the order of appearance of data points in the two sets during the k-domination checking process, so that the next user can filter and use them, which can reduce the number of comparisons between data points, avoids double counting, and improve query efficiency. A multi-user k-dominated skyline body parallel solving algorithm is also proposed, which effectively reduces the comparison time of data points through the Apache Flink parallel processing framework. The theoretical study and experimental data show that the proposed algorithm is highly efficient and can handle the multi-user k-dominated skyline problem well.

Key words: k-dominant, skyline query, multi-user, Apache Flink, parallel query