Year: 2017
Type: conference-paper
Abstract: Complex queries for massive data analysis jobs have become increasingly commonplace. Many such queries contain common subexpressions, either within a single query or among multiple queries submitted as a batch. Conventional query optimizers do not exploit these subexpressions and produce sub-optimal plans. The problem of multi-query optimization (MQO) is to generate an optimal combined evaluation plan by computing common subexpressions once and reusing them. Exhaustive algorithms for MQO explore an O(nn) search space. Thus, this problem has primarily been tackled using various heuristic algorithms, without providing any theoretical guarantees on the quality of their solution.
Authors: Tarun Kathuria, S. Sudarshan
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2586902511