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Spatio-Temporal Data StreamsSpatio-Temporal Data Streams and Big Data Paradigm

Spatio-Temporal Data Streams: Spatio-Temporal Data Streams and Big Data Paradigm [Recent rapid development of wireless communication, mobile computing, global navigational satellite systems (GNSS), and spatially enabled sensors is leading to an exponential growth of available spatio-temporal data produced continuously at hight speed. Spatio-temporal data streams, i.e. real-time, transient, time-varying sequences of spatiotemporal data items, demonstrates at least two Big Data core features: volume and velocity. To handle the volumes of data and computation they involve, these applications need to be distributed over clusters. However, despite substantial work on cluster programming models for batch computation, there are few similarly high-level tools for stream processing. Obviously, there is a clear need for highly scalable spatio-temporal stream computing framework that can operate at high data rates and process massive amounts of big spatio-temporal data streams. In this chapter we present our approach and framework for an integrated big spatio-temporal data stream processing. The key concept here is that streaming data and persistent data are not intrinsically different - the persistent spatio-temporal data is simply streaming data that has been entered into the persistent structures.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Spatio-Temporal Data StreamsSpatio-Temporal Data Streams and Big Data Paradigm

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Publisher
Springer New York
Copyright
© The Author(s) 2016
ISBN
978-1-4939-6573-1
Pages
47 –69
DOI
10.1007/978-1-4939-6575-5_3
Publisher site
See Chapter on Publisher Site

Abstract

[Recent rapid development of wireless communication, mobile computing, global navigational satellite systems (GNSS), and spatially enabled sensors is leading to an exponential growth of available spatio-temporal data produced continuously at hight speed. Spatio-temporal data streams, i.e. real-time, transient, time-varying sequences of spatiotemporal data items, demonstrates at least two Big Data core features: volume and velocity. To handle the volumes of data and computation they involve, these applications need to be distributed over clusters. However, despite substantial work on cluster programming models for batch computation, there are few similarly high-level tools for stream processing. Obviously, there is a clear need for highly scalable spatio-temporal stream computing framework that can operate at high data rates and process massive amounts of big spatio-temporal data streams. In this chapter we present our approach and framework for an integrated big spatio-temporal data stream processing. The key concept here is that streaming data and persistent data are not intrinsically different - the persistent spatio-temporal data is simply streaming data that has been entered into the persistent structures.]

Published: Aug 27, 2016

Keywords: Big data; Data stream architectures; GeoStreaming; Mobility data; Parallel processing; Real-time distributed; Spatio-temporal data streams

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