Proceedings ArticleDOI
Mining, indexing, and querying historical spatiotemporal data
Nikos Mamoulis,Huiping Cao,George Kollios,Marios Hadjieleftheriou,Yufei Tao,David W. Cheung +5 more
- pp 236-245
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TLDR
This work defines the spatiotemporal periodic pattern mining problem and proposes an effective and fast mining algorithm for retrieving maximal periodic patterns, and devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotsemporal queries.Abstract:
In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data, apart from unveiling important information to the data analyst, can facilitate data management substantially. Based on this observation, we propose a framework that analyzes, manages, and queries object movements that follow such patterns. We define the spatiotemporal periodic pattern mining problem and propose an effective and fast mining algorithm for retrieving maximal periodic patterns. We also devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotemporal queries. We evaluate our methods experimentally using datasets with object trajectories that exhibit periodicity.read more
Citations
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Data Mining: Concepts and Techniques (2nd edition)
Jiawei Han,Micheline Kamber +1 more
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Proceedings ArticleDOI
Mining interesting locations and travel sequences from GPS trajectories
TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.
Proceedings ArticleDOI
Trajectory pattern mining
TL;DR: This paper develops an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects and introduces trajectory patterns as concise descriptions of frequent behaviours in terms of both space and time.
Proceedings ArticleDOI
Map-matching for low-sampling-rate GPS trajectories
TL;DR: The results show that the ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories and when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.
Book
Data Mining: The Textbook
TL;DR: This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.
References
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Fast algorithms for mining association rules
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