PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search

IF 2.8 2区计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Vldb Journal Pub Date : 2021-07-03 DOI:10.1007/s00778-021-00680-7

Bolong Zheng, Xi Zhao, Lianggui Weng, Nguyen Quoc Viet Hung, Hang Liu, Christian S. Jensen

引用次数: 5

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PM-LSH：一种快速准确的高维近似神经网络和最接近对搜索的内存框架

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Vldb Journal 工程技术-计算机：信息系统

CiteScore

12.30

自引率

4.80%

发文量

审稿时长

>12 weeks

期刊介绍： The journal is dedicated to the publication of scholarly contributions in areas of data management such as database system technology and information systems, including their architectures and applications. Further, the journal’s scope is restricted to areas of data management that are covered by the combined expertise of the journal’s editorial board. Submissions with a substantial theory component are welcome, but the VLDB Journal expects such submissions also to embody a systems component. In relation to data mining, the journal will handle submissions where systems issues play a significant role. Factors that we use to determine whether a data mining paper is within scope include: The submission targets systems issues in relation to data mining, e.g., by covering integration with a database engine or with other data management functionality. The submission’s contributions build on (rather than simply cite) work already published in database outlets, e.g., VLDBJ, ACM TODS, PVLDB, ACM SIGMOD, IEEE ICDE, EDBT. The journal''s editorial board has the necessary expertise on the submission''s topic. Traditional, stand-alone data mining papers that lack the above or similar characteristics are out of scope for this journal. Criteria similar to the above are applied to submission from other areas, e.g., information retrieval and geographical information systems.