Pruning techniques for parallel processing of reverse top-k queries

• Published in: Distributed and parallel databases : an international journal Vol. 39; no. 1; pp. 169 - 199
• Main Authors: Nikitopoulos, Panagiotis, Sfyris, Georgios A., Vlachou, Akrivi, Doulkeridis, Christos, Telelis, Orestis
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2021; Springer Nature B.V
• Subjects: Algorithms; Computer Science; Data Structures; Database Management; Datasets; Information Systems Applications (incl.Internet); Memory Structures; Operating Systems; Parallel processing; Queries; Query processing; Parallel processing; Distributed data; Pruning techniques; Reverse top-k query
• ISSN: 0926-8782; 1573-7578
• DOI: 10.1007/s10619-020-07297-9

In this paper, we address the problem of processing reverse top- k queries in a parallel setting. Given a database of objects, a set of user preferences, and a query object q , the reverse top- k query returns the subset of user preferences for which the query object belongs to the top- k results. Although recently the reverse top- k query operator has been studied extensively, its CPU-intensive nature results in prohibitively expensive processing cost, when applied on vast-sized data sets. This limitation motivates us to explore a scalable parallel processing solution, in order to enable reverse top- k processing over distributed large sets of input data in reasonable execution time. We present an algorithmic framework for the problem, in which different algorithms can be instantiated, targeting a generic parallel setting. We describe a parallel algorithm (DiPaRT) that exploits basic pruning properties and is provably correct, as an instantiation of the framework. Furthermore, we introduce novel pruning properties for the problem, and propose DiPaRT+ as another instance of the algorithmic framework, which offers improved efficiency and scales gracefully. All algorithms are implemented in MapReduce, and we provide a wide set of experiments that demonstrate the improved efficiency of DiPaRT+ using data sets that are four orders of magnitude larger than those handled by centralized approaches.