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Warehousing and Mining Streams of Mobile Object Observations

Warehousing and Mining Streams of Mobile Object Observations

S. Orlando, A. Raffaetà, A. Roncato, C. Silvestri
Copyright: © 2010 |Pages: 27
ISBN13: 9781605663289|ISBN10: 160566328X|ISBN13 Softcover: 9781616922092|EISBN13: 9781605663296
DOI: 10.4018/978-1-60566-328-9.ch004
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MLA

Orlando, S., et al. "Warehousing and Mining Streams of Mobile Object Observations." Intelligent Techniques for Warehousing and Mining Sensor Network Data, edited by Alfredo Cuzzocrea, IGI Global, 2010, pp. 72-98. https://doi.org/10.4018/978-1-60566-328-9.ch004

APA

Orlando, S., Raffaetà, A., Roncato, A., & Silvestri, C. (2010). Warehousing and Mining Streams of Mobile Object Observations. In A. Cuzzocrea (Ed.), Intelligent Techniques for Warehousing and Mining Sensor Network Data (pp. 72-98). IGI Global. https://doi.org/10.4018/978-1-60566-328-9.ch004

Chicago

Orlando, S., et al. "Warehousing and Mining Streams of Mobile Object Observations." In Intelligent Techniques for Warehousing and Mining Sensor Network Data, edited by Alfredo Cuzzocrea, 72-98. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-328-9.ch004

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Abstract

In this chapter, the authors discuss how data warehousing technology can be used to store aggregate information about trajectories of mobile objects, and to perform OLAP operations over them. To this end, the authors define a data cube with spatial and temporal dimensions, discretized according to a hierarchy of regular grids. This chapter analyses some measures of interest related to trajectories, such as the number of distinct trajectories in a cell or starting from a cell, the distance covered by the trajectories in a cell, the average and maximum speed and the average acceleration of the trajectories in the cell, and the frequent patterns obtained by a data mining process on trajectories. The authors focus on some specialised algorithms to transform data, and load the measures in the base cells. Such stored values are used, along with suitable aggregate functions, to compute the roll-up operations. The main issues derive, in this case, from the characteristics of input data (i.e., trajectory observations of mobile objects), which are usually produced at different rates, and arrive in streams in an unpredictable and unbounded way. Finally, the authors also discuss some use cases that would benefit from such a framework, in particular in the domain of supervision systems to monitor road traffic (or movements of individuals) in a given geographical area.

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