Spatio-Temporal Data Streams and Big Data Paradigm

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.