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A framework for efficient multi-attribute movement data analysis

  • Fabio Valdés
  • Ralf Hartmut Güting
Regular Paper
  • 61 Downloads

Abstract

In the first two decades of this century, the amount of movement and movement-related data has increased massively, predominantly due to the proliferation of positioning features in ubiquitous devices such as cellphones and automobiles. At the same time, there is a vast number of requirements for managing and analyzing these records for economic, administrative, and private purposes. Since the growth of data quantity outpaces the efficiency development of hardware components, it is necessary to explore innovative methods of extracting information from large sets of movement data. Hence, the management and analysis of such data, also called trajectories, have become a very active research field. In this context, the time-dependent geographic position is only one of arbitrarily many recorded attributes. For several applications processing trajectory (and related) data, it is helpful or even necessary to trace or generate additional time-dependent information, according to the purpose of the evaluation. For example, in the field of aircraft traffic analysis, besides the position of the monitored airplane, also its altitude, the remaining amount of fuel, the temperature, the name of the traversed country and many other parameters that change with time are relevant. Other application domains consider the names of streets, places of interest, or transportation modes which can be recorded during the movement of a person or another entity. In this paper, we present in detail a framework for analyzing large datasets having any number of time-dependent attributes of different types with the help of a pattern language based on regular expression structures. The corresponding matching algorithm uses a collection of different indexes and is divided into a filtering and an exact matching phase. Compared to the previous version of the framework, we have extended the flexibility and expressiveness of the language by changing its semantics. Due to storage adjustments concerning the applied index structures and further optimizations, the efficiency of the matching procedure has been significantly improved. In addition, the user is no longer required to have a deep knowledge of the temporal distribution of the available attributes of the dataset. The expressiveness and efficiency of the novel approach are demonstrated by querying real and synthetic datasets. Our approach has been fully implemented in a DBMS querying environment and is freely available open source software.

Keywords

Pattern matching Multi-attribute data Indexing 

References

  1. 1.
    Alvares, L.O., Bogorny, V., Kuijpers, B., de Macêdo, J.A.F., Moelans, B., Vaisman, A.: A model for enriching trajectories with semantic geographical information. In: ACM GIS, pp. 22:1–22:8 (2007)Google Scholar
  2. 2.
    Andrienko, G.L., Andrienko, N.V., Heurich, M.: An event-based conceptual model for context-aware movement analysis. Int. J. Geograph. Inf. Sci. 25(9), 1347–1370 (2011)CrossRefGoogle Scholar
  3. 3.
    Bogorny, V., Renso, C., de Aquino, A.R., de Lucca Siqueira, F., Alvares, L.O.: Constant—a conceptual data model for semantic trajectories of moving objects. Trans. GIS 18(1), 66–88 (2014)CrossRefGoogle Scholar
  4. 4.
    Bracciale, L., Bonola, M., Loreti, P., Bianchi, G., Amici, R., Rabuffi, A.: Crawdad dataset roma/taxi. http://crawdad.org/roma/taxi/20140717 (2014). Accessed 23 Oct 2018
  5. 5.
    Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)CrossRefGoogle Scholar
  6. 6.
    Brinkhoff, T.: Network-based generator of moving objects. http://iapg.jade-hs.de/personen/brinkhoff/generator (2002). Accessed 23 Oct 2018
  7. 7.
    Cai, G., Lee, K., Lee, I.: Discovering common semantic trajectories from geo-tagged social media. In: IEA/AIE, pp. 320–332 (2016)Google Scholar
  8. 8.
    Camossi, E., Villa, P., Mazzola, L.: Semantic-based anomalous pattern discovery in moving object trajectories. CoRR arxiv:1305.1946 (2013)
  9. 9.
    Chang, J.W., Song, M.S., Um, J.H.: TMN-tree: new trajectory index structure for moving objects in spatial networks. In: CIT, pp. 1633–1638 (2010)Google Scholar
  10. 10.
    Damiani, M.L., Issa, H., Güting, R.H., Valdés, F.: Hybrid queries over symbolic and spatial trajectories: a usage scenario. In: MDM, pp. 341–344 (2014)Google Scholar
  11. 11.
    Damiani, M.L., Issa, H., Güting, R.H., Valdés, F.: Symbolic trajectories and application challenges. SIGSPATIAL Spec. 7(1), 51–58 (2015)CrossRefGoogle Scholar
  12. 12.
    Database Systems for New Applications, Fernuniversität Hagen. http://dna.fernuni-hagen.de/Secondo.html. Accessed 23 Oct 2018
  13. 13.
    de Almeida, V.T., Güting, R.H., Behr, T.: Querying moving objects in Secondo. In: MDM, pp. 47–51 (2006)Google Scholar
  14. 14.
    du Mouza, C., Rigaux, P.: Multi-scale classification of moving objects trajectories. In: SSDBM, pp. 307–316 (2004)Google Scholar
  15. 15.
    du Mouza, C., Rigaux, P.: Mobility patterns. GeoInformatica 9(4), 297–319 (2005)CrossRefGoogle Scholar
  16. 16.
    Erwig, M., Güting, R.H., Schneider, M., Vazirgiannis, M.: Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica 3(3), 269–296 (1999)CrossRefGoogle Scholar
  17. 17.
    Fileto, R., May, C., Renso, C., Pelekis, N., Klein, D., Theodoridis, Y.: The baquara\({}^{\text{2 }}\) knowledge-based framework for semantic enrichment and analysis of movement data. Data Knowl. Eng. 98, 104–122 (2015)CrossRefGoogle Scholar
  18. 18.
    Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases. In: ACM SIGMOD, pp. 319–330 (2000)CrossRefGoogle Scholar
  19. 19.
    Geofabrik GmbH and OpenStreetMap Contributors: Openstreetmap data extracts. http://download.geofabrik.de (2007). Accessed 23 Oct 2018
  20. 20.
    Gryllakis, F., Pelekis, N., Doulkeridis, C., Sideridis, S., Theodoridis, Y.: Searching for spatio-temporal-keyword patterns in semantic trajectories. In: Advances in Intelligent Data Analysis, pp. 112–124 (2017)CrossRefGoogle Scholar
  21. 21.
    Gryllakis, F., Pelekis, N., Doulkeridis, C., Sideridis, S., Theodoridis, Y.: Spatio-temporal-keyword pattern queries over semantic trajectories with hermes@neo4j. In: EDBT, pp. 678–681 (2018)Google Scholar
  22. 22.
    Güting, R.H., Behr, T., Düntgen, C.: Secondo: a platform for moving objects database research and for publishing and integrating research implementations. IEEE Data Eng. Bull. 33(2), 56–63 (2010)Google Scholar
  23. 23.
    Güting, R.H., Böhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM TODS 25(1), 1–42 (2000)CrossRefGoogle Scholar
  24. 24.
    Güting, R.H., Schneider, M.: Moving Objects Databases. Morgan Kaufmann, Los Altos (2005)zbMATHGoogle Scholar
  25. 25.
    Güting, R.H., Valdés, F., Damiani, M.L.: Symbolic trajectories. ACM TSAS 1(2), 7:1–7:51 (2015)Google Scholar
  26. 26.
    Hadjieleftheriou, M., Kollios, G., Bakalov, P., Tsotras, V.J.: Complex spatio-temporal pattern queries. In: PVLDB, pp. 877–888 (2005)Google Scholar
  27. 27.
    Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation, 2nd edn. Addison-Wesley-Longman Publishing, Reading (2001)zbMATHGoogle Scholar
  28. 28.
    Issa, H., Damiani, M.L.: Efficient access to temporally overlaying spatial and textual trajectories. In: MDM, pp. 262–271 (2016)Google Scholar
  29. 29.
    Liu, H., Xu, J., Zheng, K., Liu, C., Du, L., Wu, X.: Semantic-aware query processing for activity trajectories. In: International Conference on Web Search and Data Mining, WSDM, pp. 283–292 (2017)Google Scholar
  30. 30.
    NASA, NGA: Shuttle radar topography mission. https://lta.cr.usgs.gov/SRTM1Arc (2000). Accessed 23 Oct 2018
  31. 31.
    Navarro, G., Raffinot, M.: Flexible Pattern Matching in Strings—Practical On-Line Search Algorithms for Texts and Biological Sequences. Cambridge University Press, Cambridge (2002)CrossRefGoogle Scholar
  32. 32.
    Newson, P., Krumm, J.: Hidden markov map matching through noise and sparseness. In: ACM SIGSPATIAL, pp. 336–343. ACM (2009)Google Scholar
  33. 33.
    Nguyen-Dinh, L., Aref, W.G., Mokbel, M.F.: Spatio-temporal access methods: part 2 (2003–2010). IEEE Data Eng. Bull. 33(2), 46–55 (2010)Google Scholar
  34. 34.
    Nogueira, T.P., Braga, R.B., de Oliveira, C.T., Martin, H.: Framestep: a framework for annotating semantic trajectories based on episodes. Expert Syst. Appl. 92, 533–545 (2018)CrossRefGoogle Scholar
  35. 35.
    Openclipart: https://openclipart.org/ (2018). Accessed 23 Oct 2018
  36. 36.
    OpenStreetMap Foundation: Openstreetmap. http://www.openstreetmap.org (2004). Accessed 23 Oct 2018
  37. 37.
    Parent, C., Spaccapietra, S., Renso, C., Andrienko, G.L., Andrienko, N.V., Bogorny, V., Damiani, M.L., Gkoulalas-Divanis, A., de Macêdo, J.A.F., Pelekis, N., Theodoridis, Y., Yan, Z.: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4), 42 (2013)CrossRefGoogle Scholar
  38. 38.
    Pelekis, N., Frentzos, E., Giatrakos, N., Theodoridis, Y.: HERMES: a trajectory DB engine for mobility-centric applications. IJKBO 5(2), 19–41 (2015)Google Scholar
  39. 39.
    Pelekis, N., Theodoridis, Y.: Mobility Data Management and Exploration. Springer, Berlin (2014)CrossRefGoogle Scholar
  40. 40.
    Pfoser, D., Jensen, C.S., Theodoridis, Y.: Novel approaches in query processing for moving object trajectories. In: VLDB, pp. 395–406 (2000)Google Scholar
  41. 41.
    Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transp. Res. Part C Emerg. Technol. 15(5), 312–328 (2007)CrossRefGoogle Scholar
  42. 42.
    Sistemi Territoriali: Roma capitale, mappa dei municipi. http://www.datiopen.it/en/opendata/Municipi_di_Roma_Capitale (2012). Accessed 23 Oct 2018
  43. 43.
    Spaccapietra, S., Parent, C., Damiani, M.L., de Macêdo, J.A.F., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)CrossRefGoogle Scholar
  44. 44.
    Valdés, F., Damiani, M.L., Güting, R.H.: Symbolic trajectories in SECONDO: pattern matching and rewriting. DASFAA 2, 450–453 (2013)Google Scholar
  45. 45.
    Valdés, F., Güting, R.H.: Index-supported pattern matching on symbolic trajectories. In: ACM SIGSPATIAL, pp. 53–62 (2014)Google Scholar
  46. 46.
    Valdés, F., Güting, R.H.: Efficient multi-attribute analysis for trajectories: a case study for aircraft. In: ACM SIGSPATIAL, pp. 88:1–88:4 (2017)Google Scholar
  47. 47.
    Valdés, F., Güting, R.H.: Index-supported pattern matching on tuples of time-dependent values. GeoInformatica 21(3), 429–458 (2017)CrossRefGoogle Scholar
  48. 48.
    Valdés, F., Güting, R.H., Ossi, F.: Efficient trajectory analysis for several time-dependent attributes: a case study for roe deer. In: MDM, pp. 337–340 (2016)Google Scholar
  49. 49.
    Vazirgiannis, M., Theodoridis, Y., Sellis, T.K.: Spatio-temporal composition and indexing for large multimedia applications. ACM Multimed. Syst. 6(4), 284–298 (1998)CrossRefGoogle Scholar
  50. 50.
    Vieira, M.R., Bakalov, P., Tsotras, V.J.: Querying trajectories using flexible patterns. In: EDBT, pp. 406–417 (2010)Google Scholar
  51. 51.
    Vieira, M.R., Bakalov, P., Tsotras, V.J.: Flextrack: a system for querying flexible patterns in trajectory databases. In: SSTD, pp. 475–480 (2011)Google Scholar
  52. 52.
    Vlachos, M., Gunopulos, D., Kollios, G.: Discovering similar multidimensional trajectories. In: ICDE, pp. 673–684 (2002)Google Scholar
  53. 53.
    Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semantic trajectories: mobility data computation and annotation. ACM TIST 4(3), 49 (2013)Google Scholar
  54. 54.
    Zhang, C., Han, J., Shou, L., Lu, J., La Porta, T.F.: Splitter: mining fine-grained sequential patterns in semantic trajectories. PVLDB 7(9), 769–780 (2014)Google Scholar
  55. 55.
    Zheng, K., Shang, S., Yuan, N.J., Yang, Y.: Towards efficient search for activity trajectories. In: ICDE, pp. 230–241 (2013)Google Scholar
  56. 56.
    Zheng, K., Zheng, B., Xu, J., Liu, G., Liu, A., Li, Z.: Popularity-aware spatial keyword search on activity trajectories. World Wide Web 20(4), 749–773 (2017)CrossRefGoogle Scholar
  57. 57.
    Zheng, Y., Xie, X., Ma, W.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)Google Scholar
  58. 58.
    Zheng, Y., Zhou, X. (eds.): Computing with Spatial Trajectories. Springer, Berlin (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Database Systems for New ApplicationsFernuniversität HagenHagenGermany

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