Progressive evaluation of nested aggregate queries

In many decision-making scenarios, decision makers require rapid feedback t o their queries, which typically involve aggregates. The traditional blocki ng execution model can no longer meet the demands of these users. One promi sing approach in the literature, called online aggregation, evaluates an ag gregation query progressively as follows: as soon as certain data have been evaluated, approximate answers are produced with their respective running confidence intervals; as more data are examined, the answers and their corr esponding running confidence intervals are refined. In this paper, we exten d this approach to handle nested queries with aggregates (i.e., at least on e inner query block is an aggregate query) by providing users with (approxi mate) answers progressively as the inner aggregation query blocks are evalu ated. We address the new issues pose by nested queries. In particular, the answer space begins with a superset of the final answers and is refined as the aggregates from the inner query blocks are refined. For the intermediar y answers to be meaningful, they have to be interpreted with the aggregates from the inner queries. We also propose a multi-threaded model in evaluati ng such queries: each query block is assigned to a thread, and the threads can be evaluated concurrently and independently. The time slice across the threads is nondeterministic in the sense that the user controls the relativ e rate at which these subqueries are being evaluated. For enumerative neste d queries, we propose a priority-based evaluation strategy to present answe rs that are certainly in the final answer space first, before presenting th ose whose validity may be affected as the inner query aggregates are refine d. We implemented a prototype system using Java and evaluated our system. R esults for nested queries with a single level and multiple levels of nestin g are reported. Our results show the effectiveness of the proposed mechanis ms in providing progressive feedback that reduces the initial waiting time of users significantly without sacrificing the quality of the answers.