Towards best region search for data exploration

Kaiyu Feng, Gao Cong, Sourav S. Bhowmick, Wen-Chih Peng, Chunyan Miao

研究成果: Conference contribution同行評審

41 引文 斯高帕斯(Scopus)

摘要

The increasing popularity and growth of mobile devices and locationbased services enable us to utilize large-scale geo-tagged data to support novel location-based applications. This paper introduces a novel problem called the best region search (BRS) problem and provides efficient solutions to it. Given a set O of spatial objects, a submodular monotone aggregate score function, and the size a × b of a query rectangle, the BRS problem aims to find a×b rectangular region such that the aggregate score of the spatial objects inside the region is maximized. This problem is fundamental to support several real-world applications such as most influential region search (e.g., the best location for a signage to attract most audience) and most diversified region search (e.g., region with most diverse facilities). We propose an efficient algorithm called SliceBRS to find the exact answer to the BRS problem. Furthermore, we propose an approximate solution called CoverBRS and prove that the answer found by it is bounded by a constant. Our experimental study with real-world datasets and applications demonstrates the effectiveness and superiority of our proposed algorithms.

原文English
主出版物標題SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
發行者Association for Computing Machinery
頁面1055-1070
頁數16
ISBN(電子)9781450335317
DOIs
出版狀態Published - 26 6月 2016
事件2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
持續時間: 26 6月 20161 7月 2016

出版系列

名字Proceedings of the ACM SIGMOD International Conference on Management of Data
26-June-2016
ISSN(列印)0730-8078

Conference

Conference2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
國家/地區United States
城市San Francisco
期間26/06/161/07/16

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