Pruning techniques for parallel processing of reverse top-k queries

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.