Abstract
Lately, the amount of mobility data recorded by GPS-enabled (and other) devices has increased drastically, entailing the necessity of efficient processing and analysis methods. In many cases, not only the geographic position, but also additional time-dependent information are traced and/or generated, according to the purpose of the evaluation. For example, in the field of animal behavior research, besides the position of the monitored animal, biologists are interested in further data like the altitude or the temperature at every measuring point. Other application domains comprise the names of streets, places of interest, or transportation modes that can be recorded along with the geographic position of a person. In this paper, we present in detail a framework for analyzing datasets with arbitrarily many time-dependent attributes. This can be considered as a major extension of our previous work, a comprehensive framework for pattern matching on symbolic trajectories with index support. For an efficient processing of different data types, a variable number of indexes of four different types that correspond to the data types of the attributes are applied. We demonstrate the expressiveness and efficiency of our approach by querying a real dataset representing taxi trips in Rome and, particularly, with a broad series of experiments using trajectories generated by BerlinMOD combined with geological raster data.
Similar content being viewed by others
Notes
The definition in [14] has an additional condition requesting that such a function has only a finite number of continuous components, omitted here.
The concept of user-defined set relations was introduced in our previous work, please refer to [36] for details.
Note that in the implementation, variables are represented as integer values for the sake of efficiency. For a better understanding, we chose to use strings in this paper.
The database object fontanaditrevi is a small rectangle around the Trevi Fountain.
Secondo considers the speed of an object in meters per second; 30 m/sec approximately equal 67 mph.
References
de Almeida V T, Güting R H, Behr T (2006) Querying moving objects in Secondo. In: MDM, pp 47–51
Andrienko G L, Andrienko N V, Heurich M (2011) An event-based conceptual model for context-aware movement analysis. Int J Geogr Inf Sci 25(9):1347–1370
Bayer R, McCreight E M (1972) Organization and maintenance of large ordered indices. Acta Inf 1:173–189
Bracciale L, Bonola M, Loreti P, Bianchi G, Amici R, Rabuffi A (2014). CRAWDAD dataset roma/taxi (v. 2014-07-17). Downloaded from http://crawdad.org/roma/taxi/20140717
Chang J W, Song M S, Um J H (2010) Tmn-tree: New trajectory index structure for moving objects in spatial networks. In: CIT, pp 1633–1638
Comer D (1979) Ubiquitous B-Tree. ACM Comput Surv 11(2):121–137
Damiani M L, Issa H, Güting R H, Valdés F (2014) Hybrid queries over symbolic and spatial trajectories: A usage scenario. In: MDM, pp 341–344
Damiani M L, Issa H, Güting R H, Valdés F (2015) Symbolic trajectories and application challenges. SIGSPATIAL Special 7(1):51–58
De La Briandais R (1959) File searching using variable length keys. IRE-AIEE-ACM (Western):295–298
Düntgen C, Behr T, Güting R H (2009) BerlinMOD: a benchmark for moving object databases. VLDB J 18(6):1335–1368
Erwig M, Güting R H, Schneider M, Vazirgiannis M (1999) Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica 3(3):269–296
Forlizzi L, Güting R H, Nardelli E, Schneider M (2000) A data model and data structures for moving objects databases. In: ACM SIGMOD, pp 319–330
Güting R H, Behr T, Düntgen C (2010) Secondo: a platform for moving objects database research and for publishing and integrating research implementations. IEEE Data Eng Bull 33(2):56–63
Güting R H, Böhlen M H, Erwig M, Jensen C S, Lorentzos N A, Schneider M, Vazirgiannis M (2000) A foundation for representing and querying moving objects. ACM TODS 25(1):1–42
Güting R H, Schneider M (2005) Moving objects databases morgan kaufmann
Güting R H, Valdés F, Damiani M L (2015) Symbolic trajectories. ACM TSAS 1(2):7:1–7:51
Guttman A (1984) R-trees: A dynamic index structure for spatial searching. In: SIGMOD, pp 47–57
Hadjieleftheriou M, Kollios G, Bakalov P, Tsotras V J (2005) Complex spatio-temporal pattern queries. In: PVLDB, pp 877–888
Hopcroft J E, Motwani R, Ullman J D (2001) Introduction to automata theory, languages, and computation - (2. ed.). Addison-wesley series in computer science Addison-Wesley-Longman
Issa H, Damiani M L (2016) Efficient access to temporally overlaying spatial and textual trajectories. In: IEEE MDM, pp 262–271
(2016). Microsoft: http://research.microsoft.com/en-us/projects/geolife/
du Mouza C, Rigaux P (2004) Multi-scale classification of moving objects trajectories. In: Proceedings on SSDBM, pp 307–316
du Mouza C, Rigaux P (2005) Mobility patterns. GeoInformatica 9(4):297–319
Navarro G, Raffinot M (2002) Flexible pattern matching in strings - practical on-line search algorithms for texts and biological sequences, Cambridge University Press
Newson P, Krumm J (2009) Hidden markov map matching through noise and sparseness. In: ACM SIGSPATIAL. ACM, pp 336–343
Nguyen-Dinh L, Aref W G, Mokbel M F (2010) Spatio-temporal access methods: Part 2 (2003 - 2010). IEEE Data Eng Bull 33(2):46–55
(2016). OpenStreetMap: http://www.openstreetmap.org
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 (2013) Semantic trajectories modeling and analysis. ACM Comput Surv 45(4):42
Pelekis N, Theodoridis Y (2014) Mobility data management and exploration springer
Pfoser D, Jensen C S, Theodoridis Y (2000) Novel approaches in query processing for moving object trajectories. In: VLDB, pp 395–406
Quddus M A, Ochieng W Y, Noland R B (2007) Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies 15(5):312–328
(2016). Secondo website: http://dna.fernuni-hagen.de/Secondo.html
Spaccapietra S, Parent C, Damiani M L, de Macêdo J A F, Porto F, Vangenot C (2008) A conceptual view on trajectories. Data Knowl Eng 65(1):126–146
(2016). U.S. Geological Survey: http://srtm.usgs.gov/
Valdés F, Damiani M L, Güting R H (2013) Symbolic trajectories in Secondo: Pattern matching and rewriting. In: DASFAA, pp 450–453
Valdés F, Güting R H (2014) Index-supported pattern matching on symbolic trajectories. In: ACM SIGSPATIAL, pp 53–62
Valdés F, Güting R H, Ossi F (2016) Efficient trajectory analysis for several time-dependent attributes: A case study for roe deer. In: IEEE MDM, pp 337–340
Vazirgiannis M, Theodoridis Y, Sellis T K (1998) Spatio-temporal composition and indexing for large multimedia applications. Multimedia Syst 6(4):284–298
Vieira M R, Bakalov P, Tsotras V J (2010) Querying trajectories using flexible patterns Proceedings of the EDBT, pp 406–417
Vieira M R, Bakalov P, Tsotras V J (2011) Flextrack: a system for querying flexible patterns in trajectory databases. In: SSTD, pp 475–480
Vlachos M, Gunopulos D, Kollios G (2002) Discovering similar multidimensional trajectories. In: ICDE, pp 673–684
Yan Z, Chakraborty D, Parent C, Spaccapietra S, Aberer K (2013) Semantic trajectories: Mobility data computation and annotation. ACM TIST 4(3):49
Zhang C, Han J, Shou L, Lu J, La Porta T F (2014) Splitter: Mining fine-grained sequential patterns in semantic trajectories. PVLDB 7(9):769–780
Zheng K, Shang S, Yuan N J, Yang Y (2013) Towards efficient search for activity trajectories. In: ICDE, pp 230–241
Zheng Y, Zhou X (eds) (2011) Computing with Spatial Trajectories. Springer
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Valdés, F., Güting, R.H. Index-supported pattern matching on tuples of time-dependent values. Geoinformatica 21, 429–458 (2017). https://doi.org/10.1007/s10707-016-0286-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10707-016-0286-6