Pg-Trajectory: A PostgreSQL/PostGIS Based Data Model for Spatiotemporal Trajectories

Due to the tremendous advances in GPS and location-based web services, highly available spatiotemporal trajectory data poses an important challenge - knowledge discovery from trajectories. Knowledge discovery tasks on trajectory big data such as classification, clustering and outlier detection require a dedicated data model, which can support various utility functions and provide a robust object-relational mapping. This paper introduces PG-TRAJECTORY, a data model extension for the popular open source database management system PostgreSQL. Our data model is built on PostGIS, the spatial database extender of PostgreSQL. Apart from providing a data model, PG-TRAJECTORY contains a wide range of functions for storing and manipulating spatiotemporal trajectories. Throughout the paper, we discuss the basic structure of our data model with the working principles of the functions, and show a set of real life query examples. Finally, we present the results of our experimental evaluation to demonstrate the scalability and effectiveness of our data model.

[1]  Karthik Ganesan Pillai,et al.  Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

[2]  Karthik Ganesan Pillai,et al.  A filter-and-refine approach to mine spatiotemporal co-occurrences , 2013, SIGSPATIAL/GIS.

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

[4]  Nikos Pelekis,et al.  HERMES: A Trajectory DB Engine for Mobility-Centric Applications , 2015, Int. J. Knowl. Based Organ..

[5]  D. Behringer,et al.  A Long-Term, High-Quality, High-Vertical-Resolution GPS Dropsonde Dataset for Hurricane and Other Studies , 2015 .

[6]  Daniele Quercia,et al.  Recommending Social Events from Mobile Phone Location Data , 2010, 2010 IEEE International Conference on Data Mining.

[7]  Ralf Hartmut Güting,et al.  Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases , 1999, GeoInformatica.

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

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

[10]  Rafal A. Angryk,et al.  Time-efficient significance measure for discovering spatiotemporal co-occurrences from data with unbalanced characteristics , 2015, SIGSPATIAL/GIS.

[11]  Wei-Ying Ma,et al.  Understanding mobility based on GPS data , 2008, UbiComp.

[12]  Bettina Speckmann,et al.  Context-Aware Similarity of Trajectories , 2012, GIScience.

[13]  Bertil Schmidt,et al.  CUDA-Accelerated Alignment of Subsequences in Streamed Time Series Data , 2014, 2014 43rd International Conference on Parallel Processing.

[14]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[15]  Dimitrios Gunopulos,et al.  Rotation invariant distance measures for trajectories , 2004, KDD.

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

[17]  Xiaoyong Du,et al.  Elite: an elastic infrastructure for big spatiotemporal trajectories , 2016, The VLDB Journal.

[18]  Rafal A. Angryk,et al.  Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases , 2016 .

[19]  Fabio Porto,et al.  A conceptual view on trajectories , 2008, Data Knowl. Eng..

[20]  Ralf Hartmut Güting,et al.  Modeling and querying moving objects in networks , 2006, The VLDB Journal.

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

[22]  Dieter Pfoser,et al.  Synthetic and Real Spatiotemporal Datasets , 2003, IEEE Data Eng. Bull..

[23]  Deok-Hwan Kim,et al.  Similarity search for multidimensional data sequences , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[24]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[25]  Karthik Ganesan Pillai,et al.  ERMO-DG: Evolving Region Moving Object Dataset Generator , 2014, FLAIRS Conference.

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

[27]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.

[28]  Enrique Vidal,et al.  Computation of Normalized Edit Distance and Applications , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Karthik Ganesan Pillai,et al.  A large-scale solar image dataset with labeled event regions , 2013, 2013 IEEE International Conference on Image Processing.