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A Survey of Parallel Indexing Techniques for Large-Scale Moving Object Databases
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Author(s): Eleazar Leal (University of Minnesota – Duluth, USA), Le Gruenwald (University of Oklahoma, USA)and Jianting Zhang (City College of New York, USA)
Copyright: 2019
Pages: 34
Source title:
Utilizing Big Data Paradigms for Business Intelligence
Source Author(s)/Editor(s): Jérôme Darmont (Université Lumière Lyon 2, France)and Sabine Loudcher (Université Lumière Lyon 2, France)
DOI: 10.4018/978-1-5225-4963-5.ch003
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Abstract
A moving object database is a database that tracks the movements of objects. As such, these databases have business intelligence applications in areas like trajectory-based advertising, disease control and prediction, hurricane path prediction, and drunk-driver detection. However, in order to extract knowledge from these objects, it is necessary to efficiently query these databases. To this end, databases incorporate special data structures called indexes. Multiple indexing techniques for moving object databases have been proposed. Nonetheless, indexing large sets of objects poses significant computational challenges. To cope with these challenges, some moving object indexes are designed to work with parallel architectures, such as multicore CPUs and GPUs (graphics processing units), which can execute multiple instructions simultaneously. This chapter discusses business intelligence applications of parallel moving object indexes, identifies issues and features of these techniques, surveys existing parallel indexes, and concludes with possible future research directions.
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