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Spatio-Temporal Data Streams

  • Zdravko Galić

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Zdravko Galić
    Pages 1-15
  3. Zdravko Galić
    Pages 17-45
  4. Zdravko Galić
    Pages 71-103
  5. Back Matter
    Pages 105-107

About this book

Introduction

This SpringerBrief presents the fundamental concepts of a specialized class of data stream, spatio-temporal data streams, and demonstrates their distributed processing using Big Data frameworks and platforms. It explores a consistent framework which facilitates a thorough understanding of all different facets of the technology, from basic definitions to state-of-the-art techniques. Key topics include spatio-temporal continuous queries, distributed stream processing, SQL-like language embedding, and trajectory stream clustering. 
 
Over the course of the book, the reader will become familiar with spatio-temporal data streams management and data flow processing, which enables the analysis of huge volumes of location-aware continuous data streams. Applications range from mobile object tracking and real-time intelligent transportation systems to traffic monitoring and complex event processing.
 
Spatio-Temporal Data Streams is a valuable resource for researchers studying spatio-temporal data streams and Big Data analytics, as well as data engineers and data scientists solving data management and analytics problems associated with this class of data.


Keywords

Spatio-temporal Data streams Geostreaming Big spatial data Distributed processing Complex event processing Streaming analytics Cluster computing Real-time analytics Data flow processing Geographic information systems

Authors and affiliations

  • Zdravko Galić
    • 1
  1. 1.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-6575-5
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4939-6573-1
  • Online ISBN 978-1-4939-6575-5
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site