%0 Book %1 Galic16 %A Galić, Zdravko %B SpringerBriefs in Computer Science %C New York %D 2016 %I Springer %K 01624 103 springer book ai temporal spatial data processing analysis pattern recognition zzz.big %R 10.1007/978-1-4939-6575-5 %T Spatio-Temporal Data Streams %X 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. %@ 978-1-4939-6573-1

@book{Galic16, abstract = {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.}, added-at = {2016-10-13T10:31:45.000+0200}, address = {New York}, author = {Gali\'{c}, Zdravko}, biburl = {https://www.bibsonomy.org/bibtex/2a3c3fc53e88c45b2d449b9d6e1d3aea4/flint63}, doi = {10.1007/978-1-4939-6575-5}, file = {SpringerLink:2016/Galic16.pdf:PDF;Springer Product page:http\://www.springer.com/978-1-4939-6573-1:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/1493965735/:URL}, groups = {public}, interhash = {dbe0755cd6cde0091ee292968c86975f}, intrahash = {a3c3fc53e88c45b2d449b9d6e1d3aea4}, isbn = {978-1-4939-6573-1}, issn = {2191-5768}, keywords = {01624 103 springer book ai temporal spatial data processing analysis pattern recognition zzz.big}, publisher = {Springer}, series = {SpringerBriefs in Computer Science}, timestamp = {2017-07-13T18:11:10.000+0200}, title = {Spatio-Temporal Data Streams}, username = {flint63}, year = 2016 }