Supporting ad-hoc ranking aggregates

This paper presents a principled framework for efficient processing of ad-hoc top-k (ranking) aggregate queries, which provide the k groups with the highest aggregates as results. Essential support of such queries is lacking in current systems, which process the queries in a na?ive materialize-group-sort scheme that can be prohibitively inefficient. Our framework is based on three fundamental principles. The Upper-Bound Principle dictates the requirements of early pruning, and the Group-Ranking and Tuple-Ranking Principles dictate group-ordering and tuple-ordering requirements. They together guide the query processor toward a provably optimal tuple schedule for aggregate query processing. We propose a new execution framework to apply the principles and requirements. We address the challenges in realizing the framework and implementing new query operators, enabling efficient group-aware and rankaware query plans. The experimental study validates our framework by demonstrating orders of...

• Type: Conference
• Year: 2006
• Where: SIGMOD
• Authors: Chengkai Li, Kevin Chen-Chuan Chang, Ihab F. Ilyas
• Keywords: Aggregate Query Processing, Database, Query Processor, Rankaware Query Plans, SIGMOD 2006