Can database systems be enhanced to efficiently handle multiple queries simultaneously? This paper examines the problem of multiple-query optimization, driven by the need to support database systems with inference capabilities. The main focus lies in developing algorithms that process multiple queries together, capitalizing on shared data among queries to reduce execution costs. The paper presents a systematic analysis of the problem, proposing and evaluating various algorithms for multiple-query optimization. Experimental results demonstrate that these algorithms can significantly reduce execution costs compared to processing queries individually. The work contributes to the development of more efficient and scalable database systems, improving performance for complex queries and data-intensive applications.
As a publication in ACM Transactions on Database Systems, this research is highly relevant to the journal's scope on database management and optimization. The study of algorithms for multiple-query processing and the presentation of experimental results align with the journal's focus on improving database system performance and efficiency.