Spatio-Temporal Data Streams

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.

[1]  C. Zaniolo,et al.  Introducing Stream Mill : User-Guide to the Data Stream Management System , its Expressive Stream Language ESL , and the Data Stream Mining Workbench SMM , 2010 .

[2]  Yan Huang,et al.  New Data Types and Operations to Support Geo-streams , 2008, GIScience.

[3]  Lionel M. Ni,et al.  CloST: a hadoop-based storage system for big spatio-temporal data analytics , 2012, CIKM '12.

[4]  Mohamed Sarwat,et al.  Interactive and Scalable Exploration of Big Spatial Data -- A Data Management Perspective , 2015, 2015 16th IEEE International Conference on Mobile Data Management.

[5]  Timos K. Sellis,et al.  Monitoring continuous queries over streaming locations , 2008, GIS '08.

[6]  Dennis McLeod,et al.  Geostreaming in cloud , 2011, IWGS '11.

[7]  Zhengping Qian,et al.  TimeStream: reliable stream computation in the cloud , 2013, EuroSys '13.

[8]  Jae-Gil Lee,et al.  MoveMine: Mining moving object data for discovery of animal movement patterns , 2011, TIST.

[9]  Ira Assent,et al.  The ClusTree: indexing micro-clusters for anytime stream mining , 2011, Knowledge and Information Systems.

[10]  Jim Melton,et al.  Advanced SQL:1999: Understanding Object-Relational and Other Advanced Features , 2002 .

[11]  Dean Wampler,et al.  Programming Hive , 2012 .

[12]  Kresimir Krizanovic,et al.  Geospatial data streams: Formal framework and implementation , 2014, Data Knowl. Eng..

[13]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[14]  Lukasz Golab,et al.  Data Stream Management , 2017, Data Stream Management.

[15]  Christopher N. Eichelberger,et al.  Spatio-temporal indexing in non-relational distributed databases , 2013, 2013 IEEE International Conference on Big Data.

[16]  Christian S. Jensen,et al.  Discovery of convoys in trajectory databases , 2008, Proc. VLDB Endow..

[17]  Felix Naumann,et al.  The Stratosphere platform for big data analytics , 2014, The VLDB Journal.

[18]  Slava Kisilevich,et al.  A conceptual framework and taxonomy of techniques for analyzing movement , 2011, J. Vis. Lang. Comput..

[19]  Beng Chin Ooi,et al.  Continuous Clustering of Moving Objects , 2007, IEEE Transactions on Knowledge and Data Engineering.

[20]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[21]  Yan Huang,et al.  Querying geospatial data streams in SECONDO , 2009, GIS.

[22]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[23]  Huan Wang,et al.  Online clustering for trajectory data stream of moving objects , 2013, Comput. Sci. Inf. Syst..

[24]  Marcos R. Vieira,et al.  Spatio-Temporal Databases: Complex Motion Pattern Queries , 2013 .

[25]  Weixiang Xu,et al.  A New Measurement Method to Calculate Similarity of Moving Object Spatio-Temporal Trajectories by Compact Representation , 2011, Int. J. Comput. Intell. Syst..

[26]  Ling Liu,et al.  Encyclopedia of Database Systems , 2009, Encyclopedia of Database Systems.

[27]  Carlo Zaniolo,et al.  Query Languages and Data Models for Database Sequences and Data Streams , 2004, VLDB.

[28]  Craig Chambers,et al.  The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing , 2015, Proc. VLDB Endow..

[29]  Ying Li,et al.  Microsoft CEP Server and Online Behavioral Targeting , 2009, Proc. VLDB Endow..

[30]  Dino Pedreschi,et al.  Unveiling the complexity of human mobility by querying and mining massive trajectory data , 2011, The VLDB Journal.

[31]  Tom White,et al.  Hadoop - The Definitive Guide: Storage and Analysis at Internet Scale (4. ed., revised & updated) , 2012 .

[32]  Jia Yu,et al.  GeoSpark : A Cluster Computing Framework for Processing Spatial Data , 2015 .

[33]  Lei Chen,et al.  On the Marriage of Edit Distance and Lp Norms , 2004, VLDB 2004.

[34]  Sergei Vassilvitskii,et al.  Scalable K-Means++ , 2012, Proc. VLDB Endow..

[35]  Christian S. Jensen,et al.  Trajectory Pattern Mining , 2011, Computing with Spatial Trajectories.

[36]  Jiawei Han,et al.  An overview of clustering methods in geographic data analysis , 2009 .

[37]  Alexandre M. Bayen,et al.  Large-Scale Estimation in Cyberphysical Systems Using Streaming Data: A Case Study With Arterial Traffic Estimation , 2013, IEEE Transactions on Automation Science and Engineering.

[38]  Leslie Lamport,et al.  Distributed snapshots: determining global states of distributed systems , 1985, TOCS.

[39]  Wang Qin,et al.  Continuous clustering trajectory stream of moving objects , 2013, China Communications.

[40]  Joachim Gudmundsson,et al.  Reporting flock patterns , 2008, Comput. Geom..

[41]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[42]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[43]  Martin Grund,et al.  Impala: A Modern, Open-Source SQL Engine for Hadoop , 2015, CIDR.

[44]  Xing Xie,et al.  GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory , 2010, IEEE Data Eng. Bull..

[45]  Michael Stonebraker,et al.  The 8 requirements of real-time stream processing , 2005, SGMD.

[46]  Carlo Zaniolo,et al.  Relational languages and data models for continuous queries on sequences and data streams , 2011, TODS.

[47]  Jiong Yang,et al.  STING: A Statistical Information Grid Approach to Spatial Data Mining , 1997, VLDB.

[48]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[49]  Hans-Dieter Ehrich,et al.  Specification of abstract data types , 1996 .

[50]  Yao Zhao,et al.  An Extensibility Approach for Spatio-temporal Stream Processing Using Microsoft StreamInsight , 2011, SSTD.

[51]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[52]  Robert Weibel,et al.  Towards a taxonomy of movement patterns , 2008, Inf. Vis..

[53]  Dino Pedreschi,et al.  Time-focused clustering of trajectories of moving objects , 2006, Journal of Intelligent Information Systems.

[54]  Ahmed Eldawy,et al.  A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data , 2013, Proc. VLDB Endow..

[55]  Mark Last,et al.  Discovering regular groups of mobile objects using incremental clustering , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.

[56]  Nikos Pelekis,et al.  Clustering Trajectories of Moving Objects in an Uncertain World , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[57]  Timos K. Sellis,et al.  Managing Trajectories of Moving Objects as Data Streams , 2004, STDBM.

[58]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[59]  Timos K. Sellis,et al.  Event Processing and Real-Time Monitoring over Streaming Traffic Data , 2012, W2GIS.

[60]  Michael J. Franklin,et al.  Continuous Analytics: Rethinking Query Processing in a Network-Effect World , 2009, CIDR.

[61]  Ahmed Eldawy,et al.  Sphinx: distributed execution of interactive SQL queries on big spatial data , 2015, SIGSPATIAL/GIS.

[62]  Jing Yuan,et al.  A framework of traveling companion discovery on trajectory data streams , 2013, ACM Trans. Intell. Syst. Technol..

[63]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[64]  Giandomenico Spezzano,et al.  SPARROW: A Spatial Clustering Algorithm using Swarm Intelligence , 2003, Applied Informatics.

[65]  Xiaofeng Meng,et al.  Moving Objects Management: Models, Techniques and Applications , 2010 .

[66]  Markus Schneider,et al.  Spatial Data Types for Database Systems: Finite Resolution Geometry for Geographic Information Systems , 1997 .

[67]  Michael Stonebraker,et al.  S-Store: Streaming Meets Transaction Processing , 2015, Proc. VLDB Endow..

[68]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[69]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[70]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Data stream clustering: A survey , 2013, CSUR.

[71]  Jae-Gil Lee,et al.  Incremental Clustering for Trajectories , 2010, DASFAA.

[72]  Walid G. Aref,et al.  Tornado: A Distributed Spatio-Textual Stream Processing System , 2015, Proc. VLDB Endow..

[73]  Chris Jermaine,et al.  Closest-Point-of-Approach Join for Moving Object Histories , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[74]  Walid G. Aref,et al.  Continuous Query Processing of Spatio-Temporal Data Streams in PLACE , 2005, GeoInformatica.

[75]  Uwe Fink Postgresql Up And Running A Practical Guide To The Advanced Open Source Database , 2016 .

[76]  Leonardo Neumeyer,et al.  S4: Distributed Stream Computing Platform , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[77]  Jiawei Han,et al.  MoveMine 2.0: Mining Object Relationships from Movement Data , 2014, Proc. VLDB Endow..

[78]  Panos Kalnis,et al.  On Discovering Moving Clusters in Spatio-temporal Data , 2005, SSTD.

[79]  JÜRGEN KRÄMER,et al.  Semantics and implementation of continuous sliding window queries over data streams , 2009, TODS.

[80]  Kresimir Krizanovic,et al.  OCEANUS: a spatio-temporal data stream system prototype , 2012, IWGS '12.

[81]  Nikos Pelekis,et al.  Mobility Data Management and Exploration , 2014, Springer New York.

[82]  Badrish Chandramouli,et al.  Spatio-Temporal Stream Processing in Microsoft StreamInsight , 2010, IEEE Data Eng. Bull..

[83]  Markus Schneider,et al.  Similarity measurement of moving object trajectories , 2012, IWGS '12.

[84]  Badrish Chandramouli,et al.  Trill: A High-Performance Incremental Query Processor for Diverse Analytics , 2014, Proc. VLDB Endow..

[85]  Michael Stonebraker,et al.  Fault-tolerance in the borealis distributed stream processing system , 2008, ACM Trans. Database Syst..

[86]  Ajith Abraham,et al.  Intelligent Systems - A Modern Approach , 2011, Intelligent Systems Reference Library.

[87]  Ugur Demiryurek,et al.  Geospatial stream query processing using Microsoft SQL Server StreamInsight , 2010, Proc. VLDB Endow..

[88]  Alain Biem,et al.  IBM infosphere streams for scalable, real-time, intelligent transportation services , 2010, SIGMOD Conference.

[89]  Walid G. Aref,et al.  SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams , 2008, The VLDB Journal.

[90]  Stefano Spaccapietra,et al.  Advanced Data Mining Method for Discovering Regions and Trajectories of Moving Objects: "Ciconia Ciconia" Scenario , 2008, AGILE Conf..

[91]  Joachim Gudmundsson,et al.  Reporting Leaders and Followers among Trajectories of Moving Point Objects , 2008, GeoInformatica.

[92]  A. U.S. FOURIER TRANSFORM FOR THE SPATIAL QUINCUNX LATTICE , 2006 .

[93]  M. Abadi,et al.  Naiad: a timely dataflow system , 2013, SOSP.

[94]  Timos K. Sellis,et al.  Maintaining consistent results of continuous queries under diverse window specifications , 2011, Inf. Syst..

[95]  Aoying Zhou,et al.  Query processing of massive trajectory data based on mapreduce , 2009, CloudDB@CIKM.

[96]  Philip S. Yu,et al.  A Framework for Clustering Evolving Data Streams , 2003, VLDB.

[97]  Edward Y. Chang,et al.  MOIST: A Scalable and Parallel Moving Object Indexer with School Tracking , 2012, Proc. VLDB Endow..

[98]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.