Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

MobilityDB: A Mobility Database Based on PostgreSQL and PostGIS

Published: 06 December 2020 Publication History

Abstract

Despite two decades of research in moving object databases and a few research prototypes that have been proposed, there is not yet a mainstream system targeted for industrial use. In this article, we present MobilityDB, a moving object database that extends the type system of PostgreSQL and PostGIS with abstract data types for representing moving object data. The types are fully integrated into the platform to reuse its powerful data management features. Furthermore, MobilityDB builds on existing operations, indexing, aggregation, and optimization framework. This is all made accessible via the SQL query interface.

References

[1]
Louai Alarabi. 2019. Summit: A scalable system for massive trajectory data management. SIGSPATIAL Spec. 10, 3 (2019), 2--3.
[2]
Louai Alarabi, Mohamed F. Mokbel, and Mashaal Musleh. 2017. ST-Hadoop: A MapReduce framework for spatio-temporal data. In Proceedings of the 15th International Symposium on Advances in Spatial and Temporal Databases (SSTD’17). Springer, 84--104.
[3]
Gennady Andrienko, Natalia Andrienko, Peter Bak, Daniel Keim, and Stefan Wrobel. 2013. Visual Analytics of Movement. Springer.
[4]
Gennady Andrienko, Natalia Andrienko, and Stefan Wrobel. 2007. Visual analytics tools for analysis of movement data. SIGKDD Explor. Newslett. 9, 2 (2007), 38--46.
[5]
Mohamed Bakli, Mahmoud Sakr, and Taysir Hassan A. Soliman. 2019. HadoopTrajectory: A Hadoop spatiotemporal data processing extension. J. Geogr. Syst. 21, 2 (2019), 211--235.
[6]
Rimantas Benetis, Christian S. Jensen, Gytis Karciauskas, and Simonas Saltenis. 2002. Nearest neighbor and reverse nearest neighbor queries for moving objects. In Proceedings of the 2002 International Symposium on Database Engineering 8 Applications (IDEAS’02). IEEE Computer Society, Los Alamitos, CA, 44--53.
[7]
Michael Böhlen, Johann Gamper, and Christian S. Jensen. 2006. Multi-dimensional aggregation for temporal data. In Proceedings of the International Conference on Extending Database Technology (EDBT’06). Springer, Berlin, 257--275.
[8]
Eliseo Clementini and Paolino Di Felice. 1996. A model for representing topological relationships between complex geometric features in spatial databases. Inf. Sci. 90, 1 (1996), 121--136.
[9]
Eliseo Clementini, Jayant Sharma, and Max J. Egenhofer. 1994. Modelling topological spatial relations: Strategies for query processing. Comput. Graph. 18, 6 (1994), 815--822.
[10]
OGC Open Geospatial Consortium. 2010. Simple feature access—Part 1: Common architecture. Retrieved from https://www.opengeospatial.org/standards/sfa.
[11]
OGC Open Geospatial Consortium. 2013. OGC moving features. Retrieved from https://www.opengeospatial.org/standards/movingfeatures.
[12]
OGC Open Geospatial Consortium. 2014. OGC moving features encoding extension: Simple comma separated values (CSV). Retrieved from http://docs.opengeospatial.org/is/14-084r2/14-084r2.html.
[13]
OGC Open Geospatial Consortium. 2016. OGC moving features access. Retrieved from http://docs.opengeospatial.org/is/16-120r3/16-120r3.html.
[14]
OGC Open Geospatial Consortium. 2018. OGC moving features encoding part I: XML core. Retrieved from http://docs.opengeospatial.org/is/18-075/18-075.html.
[15]
OGC Open Geospatial Consortium. 2019. OGC moving features encoding extension—JSON. Retrieved from http://docs.opengeospatial.org/is/19-045r3/19-045r3.html.
[16]
Xin Ding, Lu Chen, Yunjun Gao, Christian S. Jensen, and Hujun Bao. 2018. UlTraMan: A unified platform for big trajectory data management and analytics. Proc. VLDB Endow. 11, 7 (2018), 787--799.
[17]
Zhiming Ding and Ke Deng. 2011. Collecting and managing network-matched trajectories of moving objects in databases. In Proceedings of the 22nd International Conference on Database and Expert Systems Applications (DEXA’11). Springer, Toulouse, France, 270--279.
[18]
Christian Düntgen, Thomas Behr, and Ralf Hartmut Güting. 2009. BerlinMOD: A benchmark for moving object databases. VLDB J. 18, 6 (2009), 1335--1368.
[19]
Max J. Egenhofer, Eliseo Clementini, and Paolino Di Felice. 1994. Topological relations between regions with holes. Int. J. Geogr. Inf. Syst. 8, 2 (1994), 129--142.
[20]
M. Y. Eltabakh, R. Eltarras, and Walid G. Aref. 2006. Space-partitioning trees in PostgreSQL: Realization and performance. In Proceedings of the 22nd International Conference on Data Engineering (ICDE’06). IEEE.
[21]
Martin Erwig and Markus Schneider. 2002. Spatio-temporal predicates. IEEE Trans. Knowl. Data Eng. 14, 4 (2002), 881--901.
[22]
Luca Forlizzi, Ralf Hartmut Güting, Enrico Nardelli, and Markus Schneider. 2000. A data model and data structures for moving objects databases. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD’00). ACM, 319--330.
[23]
Elias Frentzos, Kostas Gratsias, Nikos Pelekis, and Yannis Theodoridis. 2007. Algorithms for nearest neighbor search on moving object trajectories. Geoinformatica 11, 2 (2007), 159--193.
[24]
Sören Gebbert and Edzer Pebesma. 2017. The GRASS GIS temporal framework. Int. J. Geogr. Inf. Sci. 31, 7 (2017), 1273--1292.
[25]
Stéphane Grumbach, Philippe Rigaux, Michel Scholl, and Luc Segoufin. 1998. DEDALE, a spatial constraint database. In Proceedings of the 6th International Workshop on Database Programming Languages (DBLP-6). Springer, 38--59.
[26]
Stéphane Grumbach, Philippe Rigaux, and Luc Segoufin. 2001. Spatio-temporal data handling with constraints. GeoInformatica 5, 1 (2001), 95--115.
[27]
Ralf Hartmut Güting, Victor Almeida, Dirk Ansorge, Thomas Behr, Zhiming Ding, Thomas Höse, Frank Hoffmann, Markus Spiekermann, and Ulrich Telle. 2005. SECONDO: An extensible DBMS platform for research prototyping and teaching. In Proceedings of the 21st International Conference on Data Engineering (ICDE’05). IEEE Computer Society, Los Alamitos, CA, 1115--1116.
[28]
Ralf Hartmut Güting, Thomas Behr, and Jianqiu Xu. 2010. Efficient k-nearest neighbor search on moving object trajectories. VLDB J. 19, 5 (2010), 687--714.
[29]
Ralf Hartmut Güting, Michael H. Böhlen, Martin Erwig, Christian S. Jensen, Nikos A. Lorentzos, Markus Schneider, and Michalis Vazirgiannis. 2000. A foundation for representing and querying moving objects. ACM Trans. Datab. Syst. 25, 1 (2000), 1--42.
[30]
Ralf Hartmut Güting, Teixeira de Almeida, and Zhiming Ding. 2006. Modeling and querying moving objects in networks. VLDB J. 15, 2 (2006), 165--190.
[31]
Stefan Hagedorn, Philipp Götze, and Kai-Uwe Sattler. 2017. The STARK framework for spatio-temporal data analytics on spark. In Datenbanksysteme für Business, Technologie und Web (BTW’17). Gesellschaft für Informatik, Bonn, 123--142.
[32]
Florian Heinz and Ralf Hartmut Güting. 2018. A data model for moving regions of in databases. Int. J. Geogr. Inf. Sci. 32, 9 (2018), 1737--1769.
[33]
Joseph M. Hellerstein, Jeffrey F. Naughton, and Avi Pfeffer. 1995. Generalized search trees for database systems. In Proceedings of the 21th International Conference on Very Large Data Bases (VLDB’95). Morgan Kaufmann, San Francisco, CA, 562--573.
[34]
ISO. 2008. ISO 19141:2008 Geographic information—Schema for moving features. Retrieved from https://www.iso.org/standard/41445.html.
[35]
Nick Kline and Richard T. Snodgrass. 1995. Computing temporal aggregates. Proceedings of the 11th International Conference on Data Engineering, 222--231.
[36]
Jiamin Lu and Ralf Hartmut Güting. 2013. Parallel SECONDO: Practical and efficient mobility data processing in the cloud. In Proceedings of the 2013 IEEE International Conference on Big Data. IEEE Computer Society, Los Alamitos, CA, 107--25.
[37]
Ahmed R. Mahmood, Sri Punni, and Walid G. Aref. 2019. Spatio-temporal access methods: A survey (2010--2017). GeoInformatica 23, 1 (2019), 1--36.
[38]
Mohamed F. Mokbel, Thanaa M. Ghanem, and Walid G. Aref. 2003. Spatio-temporal access methods. IEEE Data Eng. Bull. 26, 2 (2003), 40--49.
[39]
Kyriakos Mouratidis, Dimitris Papadias, and Marios Hadjieleftheriou. 2005. Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (SIGMOD’05). ACM, 634--645.
[40]
Richard G. Newell, David Theriault, and Mark Easterfield. 1992. Temporal GIS: Modeling the evolution of spatial data in time. Comput. Geosci. 18, 4 (1992), 427--433.
[41]
Long-Van Nguyen-Dinh, Walid G. Aref, and Mohamed F. Mokbel. 2010. Spatio-temporal access methods: Part 2 (2003--2010). IEEE Data Eng. Bull. 33, 2 (2010), 46--55.
[42]
Jan Kristof Nidzwetzki and Ralf Hartmut Güting. 2017. Distributed SECONDO: An extensible and scalable database management system. Distrib. Parallel Datab. 35, 3--4 (2017), 197--248.
[43]
Christine Parent, Stefano Spaccapietra, Chiara Renso, Gennady Andrienko, Natalia Andrienko, Vania Bogorny, Maria Luisa Damiani, Aris Gkoulalas-Divanis, Jose Macedo, Nikos Pelekis, Yannis Theodoridis, and Zhixian Yan. 2013. Semantic trajectories modeling and analysis. ACM Comput. Surv. 45, 4 (2013), 42:1--42:32.
[44]
Christine Parent, Stefano Spaccapietra, and Esteban Zimányi. 2006. Conceptual Modeling for Traditional and Spatio-Temporal Applications: The MADS Approach. Springer.
[45]
Nikos Pelekis, Elias Frentzos, Nikos Giatrakos, and Yannis Theodoridis. 2015. HERMES: A trajectory DB engine for mobility-centric applications. Int. J. Knowl.-Based Org. 5, 2 (2015), 19--41.
[46]
Nikos Pelekis, Babis Theodoulidis, Ioannis Kopanakis, and Yannis Theodoridis. 2004. Literature review of spatio-temporal database models. Knowl. Eng. Rev. 19, 3 (2004), 235--274.
[47]
Tuomas Pelkonen, Scott Franklin, Justin Teller, Paul Cavallaro, Qi Huang, Justin Meza, and Kaushik Veeraraghavan. 2015. Gorilla: A fast, scalable, in-memory time series database. Proc. VLDB Endow. 8, 12 (2015), 1816--1827.
[48]
Dieter Pfoser, Christian S. Jensen, and Yannis Theodoridis. 2000. Novel approaches in query processing for moving object trajectories. In Proceedings of the 26th International Conference on Very Large Data Bases (VLDB’00). Morgan Kaufmann, San Francisco, CA, 395--406.
[49]
Mahmoud Sakr and Ralf Hartmut Güting. 2011. Spatiotemporal pattern queries. GeoInformatica 15, 3 (2011), 497--540.
[50]
Mahmoud Sakr and Ralf Hartmut Güting. 2014. Group spatiotemporal pattern queries. GeoInformatica 18, 4 (2014), 699--746.
[51]
Zhexuan Song and Nick Roussopoulos. 2001. K-nearest neighbor search for moving query point. In Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases (SSTD’01). Springer, 79--96.
[52]
Yufei Tao and Dimitris Papadias. 2005. Historical spatio-temporal aggregation. ACM Trans. Inf. Syst. 23, 1 (2005), 61--102.
[53]
Alejandro A. Vaisman and Esteban Zimányi. 2019. Mobility data warehouses. ISPRS Int. J. Geo-Inf. 8, 4 (2019), 170.
[54]
Haozhou Wang, Kai Zheng, Xiaofang Zhou, and Shazia Sadiq. 2015. SharkDB: An in-memory storage system for massive trajectory data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD’15). Association for Computing Machinery, New York, NY, 1099--1104.
[55]
Jianqiu Xu and Ralf Hartmut Güting. 2013. A generic data model for moving objects. Geoinformatica 17, 1 (2013), 125--172.
[56]
Jun Yang and Jennifer Widom. 2003. Incremental computation and maintenance of temporal aggregates. VLDB J. 12, 3 (2003), 262--283.

Cited By

View all
  • (2024)Spatialyze: A Geospatial Video Analytics System with Spatial-Aware OptimizationsProceedings of the VLDB Endowment10.14778/3665844.366584617:9(2136-2148)Online publication date: 1-May-2024
  • (2024)Multi-Entry Generalized Search Trees for Indexing TrajectoriesProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691320(421-431)Online publication date: 29-Oct-2024
  • (2024)Technological and Research Challenges in Data Engineering for Sustainable AgricultureProceedings of the International Workshop on Big Data in Emergent Distributed Environments10.1145/3663741.3664786(1-6)Online publication date: 9-Jun-2024
  • Show More Cited By

Index Terms

  1. MobilityDB: A Mobility Database Based on PostgreSQL and PostGIS

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Database Systems
    ACM Transactions on Database Systems  Volume 45, Issue 4
    SIGMOD 2019 Best Paper, PODS 2019 Best Paper, and Regular Papers
    December 2020
    170 pages
    ISSN:0362-5915
    EISSN:1557-4644
    DOI:10.1145/3441631
    Issue’s Table of Contents
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 December 2020
    Accepted: 01 June 2020
    Revised: 01 June 2020
    Received: 01 June 2019
    Published in TODS Volume 45, Issue 4

    Check for updates

    Author Tags

    1. Moving object databases
    2. SQL
    3. mobility data management
    4. spatiotemporal data management

    Qualifiers

    • Research-article
    • Research
    • Refereed

    Funding Sources

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)121
    • Downloads (Last 6 weeks)20
    Reflects downloads up to 21 Nov 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Spatialyze: A Geospatial Video Analytics System with Spatial-Aware OptimizationsProceedings of the VLDB Endowment10.14778/3665844.366584617:9(2136-2148)Online publication date: 1-May-2024
    • (2024)Multi-Entry Generalized Search Trees for Indexing TrajectoriesProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691320(421-431)Online publication date: 29-Oct-2024
    • (2024)Technological and Research Challenges in Data Engineering for Sustainable AgricultureProceedings of the International Workshop on Big Data in Emergent Distributed Environments10.1145/3663741.3664786(1-6)Online publication date: 9-Jun-2024
    • (2024)Addressing Data Challenges to Drive the Transformation of Smart CitiesACM Transactions on Intelligent Systems and Technology10.1145/366348215:5(1-65)Online publication date: 7-Nov-2024
    • (2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
    • (2024)Trajectools Demo: Towards No-Code Solutions for Movement Data Analytics2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00048(235-238)Online publication date: 24-Jun-2024
    • (2024)Querying Mobile Pollution Data using MobilityDB2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00047(227-234)Online publication date: 24-Jun-2024
    • (2024)TMan: A High-Performance Trajectory Data Management System Based on Key-Value Stores2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00376(4951-4964)Online publication date: 13-May-2024
    • (2024)A Spatio-Temporal Series Data Model with Efficient Indexing and Layout for Cloud-Based Trajectory Data Management2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00313(1171-1184)Online publication date: 13-May-2024
    • (2024)A Data Model and Predicate Logic for Trajectory DataAdvances in Databases and Information Systems10.1007/978-3-031-70626-4_2(18-31)Online publication date: 28-Aug-2024
    • Show More Cited By

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media