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Distributed Evaluation of Top-k Temporal Joins


We study a particular kind of join, coined Ranked Temporal Join (RTJ), featuring predicates that compare time intervals and a scoring function associated with each predicate to quantify how well it is satisfied. RTJ queries are prevalent in a variety of applications such as network traffic monitoring , task scheduling, and tweet analysis. RTJ queries are often best interpreted as top-k queries where only the best matches are returned. We show how to exploit the nature of temporal predicates and the properties of their associated scoring semantics to design TKIJ , an efficient query evaluation approach on a distributed Map-Reduce architecture. TKIJ relies on an offline statistics computation that, given a time partitioning into granules, computes the distribution of intervals' endpoints in each granule, and an online computation that generates query-dependent score bounds. Those statistics are used for workload assignment to reducers. This aims at reducing data replication, to limit I/O cost. Additionally , high-scoring results are distributed evenly to enable each reducer to prune unnecessary results. Our extensive experiments on synthetic and real datasets show that TKIJ outperforms state-of-the-art competitors and provides very good performance for n-ary RTJ queries on temporal data.
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Dates and versions

hal-01266188 , version 1 (02-02-2016)
hal-01266188 , version 2 (03-02-2016)



Julien Pilourdault, Vincent Leroy, Sihem Amer-Yahia. Distributed Evaluation of Top-k Temporal Joins. 2016. ⟨hal-01266188v2⟩
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