Group spatiotemporal pattern queries

Mahmoud Sakr1,2, Ralf Hartmut Güting1
1Database Systems for New Applications, Fern Universität in Hagen, Hagen, Germany
2Computer and Information Sciences, University of Ain Shams, Cairo, Egypt

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Tài liệu tham khảo

Geopkdd website geographic privacy-aware knowledge discovery and delivery. http://www.geopkdd.eu

Secondo web site. http://dna.fernuni-hagen.de/secondo.html/

Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843. doi: 10.1145/182.358434

Andrienko G, Andrienko N, Wrobel S (2007) Visual analytics tools for analysis of movement data. SIGKDD Explor Newsl 9:38–46. doi: 10.1145/1345448.1345455

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

Benkert M, Gudmundsson J, Hübner F, Wolle T (2008) Reporting flock patterns. Comput Geom Theory Appl 41(3):111–125. doi: 10.1016/j.comgeo.2007.10.003

Bui-Xuan BM, Ferreira A, Jarry A (2003) Computing shortest, fastest, and foremost journeys in dynamic networks. Int J Found Comput Sci 14(2):267–285. doi: 10.1142/S0129054103001728 . http://www-apr.lip6.fr/~buixuan/files/BFJ03.pdf

Cotelo Lema JA, Forlizzi L, Güting RH, Nardelli E, Schneider M (2003) Algorithms for moving objects databases. Comput J 46(6):680–712

Dodge S, Weibel R, Lautenschütz AK (2008) Towards a taxonomy of movement patterns. Inf Vis 7(3):240–252. doi: 10.1057/palgrave.ivs.9500182

Düntgen C, Behr T, Güting RH (2009) Berlinmod: a benchmark for moving object databases. VLDB J 18(6):1335–1368. doi: 10.1007/s00778-009-0142-5

Eppstein D, Galil Z, Italiano GF (1999) Dynamic graph algorithms. In: Atallah MJ (ed) Algorithms and theory of computation handbook, chap 8. CRC Press. http://www.info.uniroma2.it/italiano/Papers/dyn-survey.ps.Z

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

Güting RH (1993) Second-order signature: a tool for specifying data models, query processing, and optimization. SIGMOD Rec 22(2):277–286. doi: 10.1145/170036.170079

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, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: KDD’07, pp 330–339

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

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, 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, 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

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

Kalnis P, Mamoulis N, Bakiras S (2005) On discovering moving clusters in spatio-temporal data. In: SSTD, pp 364–381

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

Laube P, Imfeld S, Weibel R (2005) Discovering relative motion patterns in groups of moving point objects. Int J Geogr Inf Sci 19(6):639–668

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

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

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

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

Pelekis N, Theodoridis Y, Vosinakis S, Panayiotopoulos T (2006) HERMES–a framework for location-based data management. In: Proceedings of EDBT 2006

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

Ren C, Lo E, Kao B, Zhu X, Cheng R (2011) On querying historical evolving graph sequences. PVLDB 4(11):726–737

Sakr M (2012) Spatiotemporal pattern queries. Ph.D. thesis, Fern Universität Hagen. http://deposit.fernuni-hagen.de/2814/

Sakr M, Güting RH (2011) Spatiotemporal pattern queries. GeoInformatica 15:497–540. doi: 10.1007/s10707-010-0114-3

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

Trasarti R (2010) Mastering the spatio-temporal knowledge discovery process. Ph.D. thesis, University of Pisa Department of Computer Science, Italy

Trasarti R, Giannotti F, Nanni M, Pedreschi D, Renso C (2011) A query language for mobility data mining. IJDWM 7(1):24–45

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

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

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 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, Zhou X (eds) (2011) Computing with Spatial Trajectories. Springer

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