Supporting Top-k Join Queries in Relational Databases

Abstract

Abstract. Ranking queries, also known as top-k queries, produce results that are ordered on some computed score. Typically, these queries involve joins, where users are usually interested only in the top-k join results. Top-k queries are dominant in many emerging applications, e.g., multimedia retrieval by content, Web databases, data mining, middlewares, and most information retrieval applications. Current relational query processors do not handle ranking queries efficiently, especially when joins are involved. In this paper, we address supporting top-k join queries in relational query processors. We introduce a new rank-join algorithm that makes use of the individual orders of its inputs to produce join results ordered on a user-specified scoring function. The idea is to rank the join results progressively during the join operation. We introduce two physical query operators based on variants of ripple join that implement the rank-join algorithm. The operators are nonblocking and can be integrated into pipelined execution plans. We also propose an efficient heuristic designed to optimize a top-k join query by choosing the best join order. We address several practical issues and optimization heuristics to integrate the new join operators in practical query processors. We implement the new operators inside a prototype database engine based on PREDATOR. The experimental evaluation of our approach compares recent algorithms for joining ranked inputs and shows superior performance.

Top-k Linked Data Query Processing

“…The J* rank join, based on the A* algorithm, was proposed in [14]. Other rank join algorithms, HRJN and HRJN*, were introduced in [7] and further extended in [12]. In contrast to previous works, we aim at the Linked Data context.…”

“…However, previous work on the top-k join problem uses pull-based processing, i.e., join operators actively "pull" their inputs in order to produce an output [7,17,20]. In compliance with [17], we adapt the pull/bound rank join (PBRJ) algorithm template for a push-based execution in the Linked Data setting.…”

“…Besides the source index employed for Linked Data query processing, we need a ranking function as well as a sorted access for top-k processing [7,14,17].…”

“…Top-k query processing [14,7,17] aims at a more efficient query execution by focusing on the k best results, while skipping the computation of remaining results. This early termination can lead to a significant reduction in the number of inputs to be read and processed, which translates to performance improvements.…”

Speeding Up the NRA Algorithm

“…The top-k algorithms embedded in RDBMS [14,15] are concerned with augmenting the query optimizer to considering rank-joins during plan evaluation. The rank-join algorithms require ordered data on input similarly to the previous middleware algorithms.…”