FreshJoin: An Efficient and Adaptive Algorithm for Set Containment Join
Data Science and Engineering(2019)
摘要
This paper revisits set containment join (SCJ) problem, which uses the subset relationship (i.e., ⊆ ) as condition to join set-valued attributes of two relations and has many fundamental applications in commercial and scientific fields. Existing in-memory algorithms for SCJ are either signature-based or prefix-tree-based. The former incurs high CPU cost because of the enumeration of signatures, while the latter incurs high space cost because of the storage of prefix trees. This paper proposes a new adaptive parameter-free in-memory algorithm, named as fre quency-ha sh join or 𝖥𝗋𝖾𝗌𝗁𝖩𝗈𝗂𝗇 in short, to evaluate SCJ efficiently. 𝖥𝗋𝖾𝗌𝗁𝖩𝗈𝗂𝗇 builds a flat index on-the-fly to record three kinds of signatures (i.e., two least frequent elements and a hash signature whose length is determined adaptively by the frequencies of elements in the universe set). The index consists of two sparse inverted indices and two arrays which record hash signatures of all sets in each relation. The index is well organized such that 𝖥𝗋𝖾𝗌𝗁𝖩𝗈𝗂𝗇 can avoid enumerating hash signatures. The rationality of this design is explained. And, the time and space cost of the proposed algorithm, which provide a rule to choose 𝖥𝗋𝖾𝗌𝗁𝖩𝗈𝗂𝗇 from existing algorithms, are analyzed. Experiments on 16 real-life datasets show that 𝖥𝗋𝖾𝗌𝗁𝖩𝗈𝗂𝗇 usually reduces more than 50
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关键词
Set containment join,Frequency hash,Join algorithm
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