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DenseZDD : A Compact and Fast Index for Families of Sets

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Title: DenseZDD : A Compact and Fast Index for Families of Sets
Authors: Denzumi, Shuhei Browse this author
Kawahara, Jun Browse this author
Tsuda, Koji Browse this author
Arimura, Hiroki Browse this author →KAKEN DB
Minato, Shin-ichi Browse this author →KAKEN DB
Sadakane, Kunihiko Browse this author
Keywords: zero-suppressed binary decision diagram
succinct data structure
set family
Issue Date: Aug-2018
Publisher: MDPI
Journal Title: Algorithms
Volume: 11
Issue: 8
Start Page: a11080128
Publisher DOI: 10.3390/a11080128
Abstract: In this article, we propose a succinct data structure of zero-suppressed binary decision diagrams (ZDDs). A ZDD represents sets of combinations efficiently and we can perform various set operations on the ZDD without explicitly extracting combinations. Thanks to these features, ZDDs have been applied to web information retrieval, information integration, and data mining. However, to support rich manipulation of sets of combinations and update ZDDs in the future, ZDDs need too much space, which means that there is still room to be compressed. The paper introduces a new succinct data structure, called DenseZDD, for further compressing a ZDD when we do not need to conduct set operations on the ZDD but want to examine whether a given set is included in the family represented by the ZDD, and count the number of elements in the family. We also propose a hybrid method, which combines DenseZDDs with ordinary ZDDs. By numerical experiments, we show that the sizes of our data structures are three times smaller than those of ordinary ZDDs, and membership operations and random sampling on DenseZDDs are about ten times and three times faster than those on ordinary ZDDs for some datasets, respectively.
Type: article
Appears in Collections:国際連携研究教育局 : GI-CoRE (Global Institution for Collaborative Research and Education : GI-CoRE) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 有村 博紀

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