PrefJoin: An efficient preference-aware join operator

Abstract

Abstract-Preference queries are essential to a wide spectrum of applications including multi-criteria decision-making tools and personalized databases. Unfortunately, most of the evaluation techniques for preference queries assume that the set of preferred attributes are stored in only one relation, waiving on a wide set of queries that include preference computations over multiple relations. This paper presents PrefJoin, an efficient preferenceaware join query operator, designed specifically to deal with preference queries over multiple relations. PrefJoin consists of four main phases: Local Pruning, Data Preparation, Joining, and Refining that filter out, from each input relation, those tuples that are guaranteed not to be in the final preference set, associate meta data with each non-filtered tuple that will be used to optimize the execution of the next phases, produce a subset of join result that are relevant for the given preference function, and refine these tuples respectively. An interesting characteristic of PrefJoin is that it tightly integrates preference computation with join hence we can early prune those tuples that are guaranteed not to be an answer, and hence it saves significant unnecessary computations cost. PrefJoin supports a variety of preference function including skyline, multi-objective and k-dominance preference queries. We show the correctness of PrefJoin. Experimental evaluation based on a real system implementation inside PostgreSQL shows that PrefJoin consistently achieves from one to three orders of magnitude performance gain over its competitors in various scenarios.

Efficient Processing of Skyline-Join Queries over Multiple Data Sources

“…Skyline-Join Query Processing. Prior work on skylines over multiple data sources include [Jin et al 2007;Sun et al 2008;Vlachou et al 2011;Khalefa et al 2011;Raghavan et al 2011;Raghavan and Rundensteiner 2010;Bhattacharya and Teja 2010;Levandoski et al 2010;Jin et al 2010]. Jin et al [2007] were the first to coin and study the skyline-join problem in the context of multi-relational databases.…”

“…These algorithms locally process skylines as much as possible before carrying out the join between tuples. PrefJoin [Khalefa et al 2011], on the other hand, is a more general framework that supports preference-aware join query operations designed specifically to deal with preference queries over multiple relations. Vlachou et al [2011] introduced a sort-first-skyline-join (SFSJ) algorithm that fuses the identification of skyline tuples with the computation of the join.…”

“…We evaluate the efficiency and effectiveness of the two-way and M-way versions of S 2 J and S 3 J by varying various key parameters and by comparing against alternative schemes, including iterative skyline-join [Sun et al 2008], PrefJoin [Khalefa et al 2011], and the SFSJ-RR and SFSJ-SC algorithms [Vlachou et al 2011]. …”

“…Recently, various attempts have been made to tackle this challenge [Jin et al 2007;Sun et al 2008;Khalefa et al 2011;Raghavan et al 2011;Vlachou et al 2011]. For instance, the skyline-join operator proposed in Sun et al [2008] is a hybrid of the previous skyline and join operators and leverages several pruning opportunities during its operation for faster execution.…”

Efficient Subgraph Skyline Search Over Large Graphs

“…Instead of performing tuple-to-tuple dominance checks, S 2 J (skylinesensitive join) [11] employs a layer/region pruning strategy. There are some other works aiming to compute the joinbased skyline, such as FlexPref [10], SKIN [13], and Prefjoin [9].…”