Finding Attribute-Aware Similar Region for Data Analysis

Kaiyu Feng, Gao Cong, Christian S. Jensen, Tao Guo

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)
20 Downloads (Pure)


With the proliferation of mobile devices and location-based services, increasingly massive volumes of geo-tagged data are becoming available. This data typically also contains non-location information. We study how to use such information to characterize a region and then how to find a region of the same size and with the most similar characteristics. This functionality enables a user to identify regions that share characteristics with a user-supplied region that the user is familiar with and likes. More specifically, we formalize and study a new problem called the attribute-aware similar region search (ASRS) problem. We first define so-called composite aggregators that are able to express aspects of interest in terms of the information associated with a user-supplied region. When applied to a region, an aggregator captures the region's relevant characteristics. Next, given a query region and a composite aggregator, we propose a novel algorithm called DS-Search to find the most similar region of the same size. Unlike any previous work on region search, DS-Search repeatedly discretizes and splits regions until an split region either satisfies a drop condition or it is guaranteed to not contribute to the result. In addition, we extend DS-Search to solve the ASRS problem approximately. Finally, we report on extensive empirical studies that offer insight into the efficiency and effectiveness of the paper's proposals.
Original languageEnglish
JournalProceedings of the VLDB Endowment
Issue number11
Pages (from-to)1414-1426
Publication statusPublished - 2019

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