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Erschienen in: GeoInformatica 4/2014

01.10.2014

Group spatiotemporal pattern queries

verfasst von: Mahmoud Attia Sakr, Ralf Hartmut Güting

Erschienen in: GeoInformatica | Ausgabe 4/2014

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Abstract

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.

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Fußnoten
1
The set operations ⊂ , ⊆ read as ispropersubset, issubset. We intentionally avoid denoting the in operation using the symbol ∈ to differentiate between usetmset and the lifted data in mset.
 
2
We use U as input to the crosspattern operator instead of S to be able to do some pre-filtering. For example, interesting pairs of candidates from S may be those coming close to each other during their lifetime, and they can possibly be determined efficiently using indexes. Evaluating instead all pairs from S may be prohibitively expensive.
 
4
Secondo uses FLOBs (Faked Large OBjects) to store moving objects. FLOBs may be stored in memory or on disk as decided by the FLOB manager. The decision is made based on FLOB size. Large FLOBs are written to disk.
 
5
Copying the whole history of the active component is expensive. Some of these copies might not appear in the result if their components disappear from the pattern graph early before their duration reaches d. In our implementation, we represent R by two lists: a list that holds parts of the component history, and another list that holds indexes to the first list. Using these two lists, the parts of the history that are shared by several active components are stored only once in the first list, and referenced several times in the second list. At the time of moving an active component from R to R, the parts that constitute this active component are concatenated together.
 
6
To guarantee a unique minimal representation of msets, which is a required feature in the base moving objects model, adjacent units having the same set of elements are merged together into a single unit, whose time interval is the union of their time intervals.
 
Literatur
5.
Zurück zum Zitat Asur S, Parthasarathy S, Ucar D (2009) An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans. Knowl. Discov. Data 3(4):16:1–16:36. doi:10.1145/631162.1631164 Asur S, Parthasarathy S, Ucar D (2009) An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans. Knowl. Discov. Data 3(4):16:1–16:36. doi:10.​1145/​631162.​1631164
8.
Zurück zum Zitat Cotelo Lema JA, Forlizzi L, Güting RH, Nardelli E, Schneider M (2003) Algorithms for moving objects databases. Comput J 46(6):680–712CrossRef Cotelo Lema JA, Forlizzi L, Güting RH, Nardelli E, Schneider M (2003) Algorithms for moving objects databases. Comput J 46(6):680–712CrossRef
12.
Zurück zum Zitat Forlizzi L, Güting RH, Nardelli E, Schneider M (2000) A data model and data structures for moving objects databases. In: SIGMOD ’00: proceedings of the 2000 ACM SIGMOD international conference on management of data. ACM, New York, pp 319–330. doi:10.1145/342009.335426 CrossRef Forlizzi L, Güting RH, Nardelli E, Schneider M (2000) A data model and data structures for moving objects databases. In: SIGMOD ’00: proceedings of the 2000 ACM SIGMOD international conference on management of data. ACM, New York, pp 319–330. doi:10.​1145/​342009.​335426 CrossRef
14.
Zurück zum Zitat Giannotti F, Nanni M, Pedreschi D, Renso C, Rinzivillo S, Trasarti R (2009) Geopkdd–geographic privacy-aware knowledge discovery. In: The European future technologies conference (FET 2009) Giannotti F, Nanni M, Pedreschi D, Renso C, Rinzivillo S, Trasarti R (2009) Geopkdd–geographic privacy-aware knowledge discovery. In: The European future technologies conference (FET 2009)
15.
Zurück zum Zitat Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: KDD’07, pp 330–339 Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: KDD’07, pp 330–339
16.
Zurück zum Zitat Gudmundsson J, van Kreveld M, Speckmann B (2004) Efficient detection of motion patterns in spatio-temporal data sets. In: GIS ’04: proceedings of the 12th annual ACM international workshop on geographic information systems. ACM, New York, pp 250–257. doi:10.1145/032222.1032259 Gudmundsson J, van Kreveld M, Speckmann B (2004) Efficient detection of motion patterns in spatio-temporal data sets. In: GIS ’04: proceedings of the 12th annual ACM international workshop on geographic information systems. ACM, New York, pp 250–257. doi:10.​1145/​032222.​1032259
17.
Zurück zum Zitat Güting RH, Almeida V, Ansorge D, Behr T, Ding Z, Höse T, Hoffmann F, Spiekermann M, Telle U (2005) secondo: an extensible DBMS platform for research prototyping and teaching. In: ICDE ’05: proceedings of the 21st international conference on data engineering. IEEE Computer Society, Washington, DC, pp 1115–1116 Güting RH, Almeida V, Ansorge D, Behr T, Ding Z, Höse T, Hoffmann F, Spiekermann M, Telle U (2005) secondo: an extensible DBMS platform for research prototyping and teaching. In: ICDE ’05: proceedings of the 21st international conference on data engineering. IEEE Computer Society, Washington, DC, pp 1115–1116
18.
Zurück zum Zitat Güting RH, Behr T, Almeida V, Ding Z, Hoffmann F, Spiekermann M (2004) secondo: an extensible DBMS architecture and prototype. Tech Rep Informatik-Report 313 FernUniversität Hagen Güting RH, Behr T, Almeida V, Ding Z, Hoffmann F, Spiekermann M (2004) secondo: an extensible DBMS architecture and prototype. Tech Rep Informatik-Report 313 FernUniversität Hagen
19.
Zurück zum Zitat Güting RH, Böhlen MH, Erwig M, Jensen CS, Lorentzos NA, Schneider M, Vazirgiannis M (2000) A foundation for representing and querying moving objects. ACM Trans Database Syst 25(1):1–42. doi:10.1145/352958.352963 CrossRef Güting RH, Böhlen MH, Erwig M, Jensen CS, Lorentzos NA, Schneider M, Vazirgiannis M (2000) A foundation for representing and querying moving objects. ACM Trans Database Syst 25(1):1–42. doi:10.​1145/​352958.​352963 CrossRef
20.
Zurück zum Zitat Jeung H, Shen HT, Zhou X (2008) Convoy queries in spatio-temporal databases. In: ICDE ’08: proceedings of the 2008 IEEE 24th international conference on data engineering. IEEE Computer Society, Washington, DC, pp 1457–1459. doi:10.1109/ICDE.2008.4497588 Jeung H, Shen HT, Zhou X (2008) Convoy queries in spatio-temporal databases. In: ICDE ’08: proceedings of the 2008 IEEE 24th international conference on data engineering. IEEE Computer Society, Washington, DC, pp 1457–1459. doi:10.​1109/​ICDE.​2008.​4497588
21.
Zurück zum Zitat Kalnis P, Mamoulis N, Bakiras S (2005) On discovering moving clusters in spatio-temporal data. In: SSTD, pp 364–381 Kalnis P, Mamoulis N, Bakiras S (2005) On discovering moving clusters in spatio-temporal data. In: SSTD, pp 364–381
22.
Zurück zum Zitat Kamath KY, Caverlee J (2011) Transient crowd discovery on the real-time social web. In: Proceedings of the fourth ACM international conference on Web search and data mining, WSDM ’11. ACM, New York, pp 585–594. doi:10.1145/935826.1935909 Kamath KY, Caverlee J (2011) Transient crowd discovery on the real-time social web. In: Proceedings of the fourth ACM international conference on Web search and data mining, WSDM ’11. ACM, New York, pp 585–594. doi:10.​1145/​935826.​1935909
23.
Zurück zum Zitat Laube P, Imfeld S, Weibel R (2005) Discovering relative motion patterns in groups of moving point objects. Int J Geogr Inf Sci 19(6):639–668CrossRef Laube P, Imfeld S, Weibel R (2005) Discovering relative motion patterns in groups of moving point objects. Int J Geogr Inf Sci 19(6):639–668CrossRef
24.
Zurück zum Zitat Laube P, Kreveld M, Imfeld S (2004) Finding REMO—detecting relative motion patterns in geospatial lifelines. In: Developments in spatial data handling: proceedings of the 11th international symposium on spatial data handling. Springer, Berlin Heidelberg, pp 201–215. doi:10.1007/b138045 Laube P, Kreveld M, Imfeld S (2004) Finding REMO—detecting relative motion patterns in geospatial lifelines. In: Developments in spatial data handling: proceedings of the 11th international symposium on spatial data handling. Springer, Berlin Heidelberg, pp 201–215. doi:10.​1007/​b138045
25.
Zurück zum Zitat Li Z, Han J, Ji M, Tang LA, Yu Y, Ding B, Lee JG, Kays R (2011) Movemine: mining moving object data for discovery of animal movement patterns. ACM Trans Intell Syst Technol 2(4):37:1–37:32. doi:10.1145/989734.1989741 CrossRef Li Z, Han J, Ji M, Tang LA, Yu Y, Ding B, Lee JG, Kays R (2011) Movemine: mining moving object data for discovery of animal movement patterns. ACM Trans Intell Syst Technol 2(4):37:1–37:32. doi:10.​1145/​989734.​1989741 CrossRef
26.
Zurück zum Zitat Li Z, Ji M, Lee JG, Tang LA, Yu Y, Han J, Kays R (2010) MoveMine: mining moving object databases. In: SIGMOD ’10: proceedings of the 2010 international conference on management of data. ACM, New York, pp 1203–1206. doi:10.1145/807167.1807319 Li Z, Ji M, Lee JG, Tang LA, Yu Y, Han J, Kays R (2010) MoveMine: mining moving object databases. In: SIGMOD ’10: proceedings of the 2010 international conference on management of data. ACM, New York, pp 1203–1206. doi:10.​1145/​807167.​1807319
27.
Zurück zum Zitat Ortale R, Ritacco E, Pelekis N, Trasarti R, Costa G, Giannotti F, Manco G, Renso C, Theodoridis Y (2008) The daedalus framework: progressive querying and mining of movement data. In: GIS, p 52 Ortale R, Ritacco E, Pelekis N, Trasarti R, Costa G, Giannotti F, Manco G, Renso C, Theodoridis Y (2008) The daedalus framework: progressive querying and mining of movement data. In: GIS, p 52
28.
Zurück zum Zitat Pelekis N, Theodoridis Y, Vosinakis S, Panayiotopoulos T (2006) HERMES–a framework for location-based data management. In: Proceedings of EDBT 2006 Pelekis N, Theodoridis Y, Vosinakis S, Panayiotopoulos T (2006) HERMES–a framework for location-based data management. In: Proceedings of EDBT 2006
29.
Zurück zum Zitat Ramanathan A, Agarwal PK, Kurnikova M, Langmead CJ (2009) An online approach for mining collective behaviors from molecular dynamics simulations. In: Proceedings of the 13th annual international conference on research in computational molecular biology, RECOMB 2’09. Springer-Verlag, Berlin, Heidelberg, pp 138–154. doi:10.1007/978-3-642-02008-7_10 Ramanathan A, Agarwal PK, Kurnikova M, Langmead CJ (2009) An online approach for mining collective behaviors from molecular dynamics simulations. In: Proceedings of the 13th annual international conference on research in computational molecular biology, RECOMB 2’09. Springer-Verlag, Berlin, Heidelberg, pp 138–154. doi:10.​1007/​978-3-642-02008-7_​10
30.
Zurück zum Zitat Ren C, Lo E, Kao B, Zhu X, Cheng R (2011) On querying historical evolving graph sequences. PVLDB 4(11):726–737 Ren C, Lo E, Kao B, Zhu X, Cheng R (2011) On querying historical evolving graph sequences. PVLDB 4(11):726–737
33.
Zurück zum Zitat Tang LA, Zheng Y, Yuan J, Han J, Leung A, Hung CC, Peng WC (2012) On discovery of traveling companions from streaming trajectories. In: IEEE 28th international conference on data engineering (ICDE) 2012, pp. 186–197. doi:10.1109/ICDE.2012.33 Tang LA, Zheng Y, Yuan J, Han J, Leung A, Hung CC, Peng WC (2012) On discovery of traveling companions from streaming trajectories. In: IEEE 28th international conference on data engineering (ICDE) 2012, pp. 186–197. doi:10.​1109/​ICDE.​2012.​33
34.
Zurück zum Zitat Trasarti R (2010) Mastering the spatio-temporal knowledge discovery process. Ph.D. thesis, University of Pisa Department of Computer Science, Italy Trasarti R (2010) Mastering the spatio-temporal knowledge discovery process. Ph.D. thesis, University of Pisa Department of Computer Science, Italy
35.
Zurück zum Zitat Trasarti R, Giannotti F, Nanni M, Pedreschi D, Renso C (2011) A query language for mobility data mining. IJDWM 7(1):24–45 Trasarti R, Giannotti F, Nanni M, Pedreschi D, Renso C (2011) A query language for mobility data mining. IJDWM 7(1):24–45
36.
Zurück zum Zitat Wolfson O, Xu B, Chamberlain S, Jiang L (1998) Moving objects databases: Issues and solutions. In: SSDBM’98: 10th international conference on scientific and statistical database management, pp 111–122 Wolfson O, Xu B, Chamberlain S, Jiang L (1998) Moving objects databases: Issues and solutions. In: SSDBM’98: 10th international conference on scientific and statistical database management, pp 111–122
37.
Zurück zum Zitat Xiao D, Eltabakh M (2013) Stepq: Spatio-temporal engine for complex pattern queries. In: Nascimento M, Sellis T, Cheng R, Sander J, Zheng Y, Kriegel HP, Renz M, Sengstock C (eds) Advances in spatial and temporal databases, lecture notes in computer science, vol 8098, pp 386–390. Springer Berlin Heidelberg Xiao D, Eltabakh M (2013) Stepq: Spatio-temporal engine for complex pattern queries. In: Nascimento M, Sellis T, Cheng R, Sander J, Zheng Y, Kriegel HP, Renz M, Sengstock C (eds) Advances in spatial and temporal databases, lecture notes in computer science, vol 8098, pp 386–390. Springer Berlin Heidelberg
38.
Zurück zum Zitat Zheng K, Zheng Y, Yuan NJ, Shang S, Zhou X (2013) Online discovery of gathering patterns over trajectories. IEEE Trans Knowl Data Eng 99(PrePrints): 1. doi:10.1109/TKDE.2013.160 Zheng K, Zheng Y, Yuan NJ, Shang S, Zhou X (2013) Online discovery of gathering patterns over trajectories. IEEE Trans Knowl Data Eng 99(PrePrints): 1. doi:10.​1109/​TKDE.​2013.​160
39.
Zurück zum Zitat Zheng Y, Yuan NJ, Zheng K, Shang S (2013) On discovery of gathering patterns from trajectories. In: Proceedings of the 2013 IEEE international conference on data engineering (ICDE 2013), ICDE ’13. IEEE Computer Society, Washington, DC, pp 242–253. doi:10.1109/ICDE.2013.6544829 Zheng Y, Yuan NJ, Zheng K, Shang S (2013) On discovery of gathering patterns from trajectories. In: Proceedings of the 2013 IEEE international conference on data engineering (ICDE 2013), ICDE ’13. IEEE Computer Society, Washington, DC, pp 242–253. doi:10.​1109/​ICDE.​2013.​6544829
40.
Zurück zum Zitat Zheng Y, Zhou X (eds) (2011) Computing with Spatial Trajectories. Springer Zheng Y, Zhou X (eds) (2011) Computing with Spatial Trajectories. Springer
41.
Zurück zum Zitat Zhou S, Chen D, Cai W, Luo L, Low MYH, Tian F, Tay VSH, Ong DWS, Hamilton BD (2010) Crowd modeling and simulation technologies. ACM Trans Model Comput Simul 20(4):20:1–20:35. doi:10.1145/842722.1842725 CrossRef Zhou S, Chen D, Cai W, Luo L, Low MYH, Tian F, Tay VSH, Ong DWS, Hamilton BD (2010) Crowd modeling and simulation technologies. ACM Trans Model Comput Simul 20(4):20:1–20:35. doi:10.​1145/​842722.​1842725 CrossRef
Metadaten
Titel
Group spatiotemporal pattern queries
verfasst von
Mahmoud Attia Sakr
Ralf Hartmut Güting
Publikationsdatum
01.10.2014
Verlag
Springer US
Erschienen in
GeoInformatica / Ausgabe 4/2014
Print ISSN: 1384-6175
Elektronische ISSN: 1573-7624
DOI
https://doi.org/10.1007/s10707-013-0198-7

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