Abstract
This paper revisits set containment join (SCJ), which has many fundamental applications in commercial and scientific fields. To improve the performance further, this paper proposes a new adaptive parameter-free in-memory algorithm for SCJ, named as \(\mathsf {FreshJoin}\). It accomplishes this by exploiting two flat indices, which record three kinds of signatures (i.e., the two least frequent elements and a hash signature). Experiments on 16 real-life datasets show that \(\mathsf {FreshJoin}\) usually reduces more than 50% of space costs while remains as competitive as the state-of-the-art algorithms in running time.
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Luo, J. et al. (2019). FreshJoin: An Efficient and Adaptive Algorithm for Set Containment Join. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_14
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DOI: https://doi.org/10.1007/978-3-030-26075-0_14
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