Group spatiotemporal pattern queries

Group spatiotemporal patterns are certain formations, in space and time, shown by groups of moving objects, such as flocks, concurrence, encounter, etc. A large number of recent applications focus on the collective behavior of moving objects, rather than the individual movements. Therefore finding such groups in moving object databases is crucial. There exist, in the literature, smart algorithms for matching some of these patterns. These solutions, however, address specific patterns and require specialized data representation and indexes. They share too little to be integrated into a single system. There is a need for a generic query method that allows users to fill in pattern descriptions, and retrieve the set of matches. In this paper, we propose generic query operators that can consistently express and match a wide range of group spatiotemporal patterns. We formally define these operators, illustrate the evaluation algorithms, and discuss the issues of their integration with moving object database (MOD) systems. These operators have been implemented in the context of Secondo MOD system, and the implementation is available online as open source. Several examples are given to showcase the expressive power of the operators. We have made available scripts that can be invoked from the Secondo interface to automatically repeat some of the experiments in this paper.

[1]  Nikos Pelekis,et al.  Hermes - A Framework for Location-Based Data Management , 2006, EDBT.

[2]  Markus Schneider,et al.  A foundation for representing and querying moving objects , 2000, TODS.

[3]  Jae-Gil Lee,et al.  MoveMine: mining moving object databases , 2010, SIGMOD Conference.

[4]  Arvind Ramanathan,et al.  An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations , 2009, RECOMB.

[5]  Hermann Helbig,et al.  The readability checker delite: technical report , 2008 .

[6]  Claus Udo Hönig Optimales Task-Graph-Scheduling für homogene und heterogene Zielsysteme , 2008 .

[7]  Ralf Hartmut Güting,et al.  User defined topological predicates in database systems , 2010, GeoInformatica.

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

[9]  Andre Osterloh,et al.  A Lower Bound for Oblivious Dimensional Routing , 2009, Euro-Par.

[10]  Ralf Hartmut Güting,et al.  SECONDO: an extensible DBMS architecture and prototype , 2004 .

[11]  International symposium on spatial data handling , 1984 .

[12]  Srinivasan Parthasarathy,et al.  An event-based framework for characterizing the evolutionary behavior of interaction graphs , 2009, ACM Trans. Knowl. Discov. Data.

[13]  Henry A. Kautz,et al.  Constraint propagation algorithms for temporal reasoning: a revised report , 1989 .

[14]  Dino Pedreschi,et al.  GeoPKDD Geographic Privacy-aware Knowledge Discovery , 2005 .

[15]  Ralf Hartmut Güting,et al.  Algorithms for Moving Objects Databases , 2003, Comput. J..

[16]  Ralf Hartmut Güting Second-order signature: a tool for specifying data models, query processing, and optimization , 1992 .

[17]  Nicholas Jing Yuan,et al.  Online Discovery of Gathering Patterns over Trajectories , 2014, IEEE Transactions on Knowledge and Data Engineering.

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

[19]  Bettina Speckmann,et al.  Efficient detection of motion patterns in spatio-temporal data sets , 2004, GIS '04.

[20]  Informatik Berichte,et al.  Relational Approaches to Knowledge Representation and Learning , 2009 .

[21]  Nikos Pelekis,et al.  The DAEDALUS framework: progressive querying and mining of movement data , 2008, GIS '08.

[22]  Dino Pedreschi,et al.  A Query Language for Mobility Data Mining , 2011, Int. J. Data Warehous. Min..

[23]  Gennady L. Andrienko,et al.  Designing Visual Analytics Methods for Massive Collections of Movement Data , 2007, Cartogr. Int. J. Geogr. Inf. Geovisualization.

[24]  Roberto Trasarti Mastering the Spatio-temporal Knowledge Discovery Process , 2010 .

[25]  Ralf Hartmut Güting,et al.  Spatiotemporal pattern queries , 2011, GeoInformatica.

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

[27]  Mohamed Y. Eltabakh,et al.  STEPQ: Spatio-Temporal Engine for Complex Pattern Queries , 2013, SSTD.

[28]  Chun Zhang,et al.  Storing and querying ordered XML using a relational database system , 2002, SIGMOD '02.

[29]  Heng Tao Shen,et al.  Convoy Queries in Spatio-Temporal Databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[30]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[31]  Stefan Wrobel,et al.  Visual analytics tools for analysis of movement data , 2007, SKDD.

[32]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[33]  Arvind Ramanathan,et al.  An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations , 2010, J. Comput. Biol..

[34]  Nicholas Jing Yuan,et al.  On discovery of gathering patterns from trajectories , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[35]  Ralf Hartmut Güting,et al.  SECONDO: A Platform for Moving Objects Database Research and for Publishing and Integrating Research Implementations , 2010, IEEE Data Eng. Bull..

[36]  Ralf Hartmut Güting,et al.  SECONDO: an extensible DBMS platform for research prototyping and teaching , 2005, 21st International Conference on Data Engineering (ICDE'05).

[37]  David Eppstein,et al.  Dynamic graph algorithms , 2010 .

[38]  Ralf Hartmut Güting,et al.  Efficient k-nearest neighbor search on moving object trajectories , 2010, The VLDB Journal.

[39]  Srinivasan Parthasarathy,et al.  An event-based framework for characterizing the evolutionary behavior of interaction graphs , 2007, KDD '07.

[40]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[41]  James Caverlee,et al.  Transient crowd discovery on the real-time social web , 2011, WSDM '11.

[42]  Marc J. van Kreveld,et al.  Finding REMO - Detecting Relative Motion Patterns in Geospatial Lifelines , 2004, SDH.

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

[44]  Jing Yuan,et al.  On Discovery of Traveling Companions from Streaming Trajectories , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[45]  Ralf Hartmut Güting,et al.  A data model and data structures for moving objects databases , 2000, SIGMOD '00.

[46]  Afonso Ferreira,et al.  Computing Shortest, Fastest, and Foremost Journeys in Dynamic Networks , 2003, Int. J. Found. Comput. Sci..

[47]  Peter F. Fisher,et al.  Developments in Spatial Data Handling, 11th International Symposium on Spatial Data Handling, Leicester, UK, August 23-25, 2004 , 2005, SDH.

[48]  Ralf Hartmut Güting,et al.  Operator-Based Query Progress Estimation , 2008 .

[49]  Reynold Cheng,et al.  On querying historical evolving graph sequences , 2011, Proc. VLDB Endow..

[50]  Bo Xu,et al.  Moving objects databases: issues and solutions , 1998, Proceedings. Tenth International Conference on Scientific and Statistical Database Management (Cat. No.98TB100243).

[51]  Wentong Cai,et al.  Crowd modeling and simulation technologies , 2010, TOMC.

[52]  Eddie Schwalb,et al.  Temporal Constraints: A Survey , 1998, Constraints.

[53]  Ralf Hartmut Güting,et al.  BerlinMOD: a benchmark for moving object databases , 2009, The VLDB Journal.

[54]  Jörg Keller,et al.  Das GCA-Modell im Vergleich zum PRAM-Modell , 2009 .

[55]  Robert Weibel,et al.  Discovering relative motion patterns in groups of moving point objects , 2005, Int. J. Geogr. Inf. Sci..