CloST: A hadoop-based storage system for big spatio-temporal data analytics

Haoyu Tan, Wuman Luo, Lionel M. Ni

研究成果: Conference contribution同行評審

51 引文 斯高帕斯(Scopus)

摘要

During the past decade, various GPS-equipped devices have generated a tremendous amount of data with time and location information, which we refer to as big spatio-temporal data. In this paper, we present the design and implementation of CloST, a scalable big spatio-temporal data storage system to support data analytics using Hadoop. The main objective of CloST is to avoid scan the whole dataset when a spatio-temporal range is given. To this end, we propose a novel data model which has special treatments on three core attributes including an object id, a location and a time. Based on this data model, CloST hierarchically partitions data using all core attributes which enables efficient parallel processing of spatio-temporal range scans. According to the data characteristics, we devise a compact storage structure which reduces the storage size by an order of magnitude. In addition, we proposes scalable bulk loading algorithms capable of incrementally adding new data into the system. We conduct our experiments using a very large GPS log dataset and the results show that CloST has fast data loading speed, desirable scalability in query processing, as well as high data compression ratio.

原文English
主出版物標題CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
頁面2139-2143
頁數5
DOIs
出版狀態Published - 2012
對外發佈
事件21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
持續時間: 29 10月 20122 11月 2012

出版系列

名字ACM International Conference Proceeding Series

Conference

Conference21st ACM International Conference on Information and Knowledge Management, CIKM 2012
國家/地區United States
城市Maui, HI
期間29/10/122/11/12

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