Date of Award

1-1-2013

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Information Science

Content Description

1 online resource (x, 61 pages) : illustrations (some color)

Dissertation/Thesis Chair

Catherine T Lawson

Committee Members

Jeong-Hyon Hwang, S.S. Ravi

Keywords

Benchmark, Compression, GPS, Trajectories, Global Positioning System, Data compression (Computer science), Data compression (Telecommunication)

Subject Categories

Computer Sciences | Geographic Information Sciences

Abstract

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object's path. The exponential increase in the amount of such trajectory data has caused three major problems. First, transmission of large amounts of data is expensive and time-consuming. Second, queries on large amounts of trajectory data require computationally expensive operations to extract useful patterns and information. Third, GPS trajectories often contain large amounts of redundant data that waste storage and cause increased disk I/O time. These issues can be addressed by algorithms that reduce the size of trajectory data. This dissertation provides a comprehensive overview of trajectory compression algorithms, evaluation metrics and data generators in conjunction with detailed discussions on their unique benefits and relevant application scenarios. Furthermore, this dissertation presents a benchmarking framework for efficiently, conveniently, and accurately comparing trajectory compression algorithms. A key requirement for these algorithms is to minimize the loss of information essential to location-based applications. To address this requirement, this research introduces a new compression method called SQUISH (Spatial QUalIty Simplification Heuristic) that provides improved run-time performance and usability. A comprehensive comparison of SQUISH with other algorithms is carried out using the introduced benchmarking framework across three types of real-world datasets and three synthetic data generators.

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