The VLDB Journal

, Volume 25, Issue 4, pp 473–493 | Cite as

Elite: an elastic infrastructure for big spatiotemporal trajectories

  • Xike Xie
  • Benjin Mei
  • Jinchuan Chen
  • Xiaoyong Du
  • Christian S. Jensen
Regular Paper


As the volumes of spatiotemporal trajectory data continue to grow at a rapid pace; a new generation of data management techniques is needed in order to be able to utilize these data to provide a range of data-driven services, including geographic-type services. Key challenges posed by spatiotemporal data include the massive data volumes, the high velocity with which the data are captured, the need for interactive response times, and the inherent inaccuracy of the data. We propose an infrastructure, Elite, that leverages peer-to-peer and parallel computing techniques to address these challenges. The infrastructure offers efficient, parallel update and query processing by organizing the data into a layered index structure that is logically centralized, but physically distributed among computing nodes. The infrastructure is elastic with respect to storage, meaning that it adapts to fluctuations in the storage volume, and with respect to computation, meaning that the degree of parallelism can be adapted to best match the computational requirements. Further, the infrastructure offers advanced functionality, including probabilistic simulations, for contending with the inaccuracy of the underlying data in query processing. Extensive empirical studies offer insight into properties of the infrastructure and indicate that it meets its design goals, thus enabling the effective management of big spatiotemporal data.


Elasticity Spatiotemporal data Trajectories 



This work was supported by the 973 program with No 2012CB316205, a grant from the Obel Family Foundation, and National Science Foundation of China under Grant No. 61432006. The work was done in part when some of the authors visited SA Center for Big Data Research at Renmin University of China. The center is partially funded by the Chinese National “111” Project “Attracting International Talents in Data Engineering and Knowledge Engineering Research”.


  1. 1.
    Ceikute, V., Jensen, C.S.: Vehicle routing with user-generated trajectory data. In: MDM (2015)Google Scholar
  2. 2.
    Yang, B., Guo, C., Ma, Y., Jensen, C.S.: Toward personalized, context-aware routing. VLDB J. 24(2), 297–318 (2015)CrossRefGoogle Scholar
  3. 3.
    Dai, J., Yang, B., Guo, C., Jensen, C.S.: Efficient and accurate path cost estimation using trajectory data. In: CoRR abs/1510.02886 (2015)Google Scholar
  4. 4.
    Stougiannis, A., Pavlovic, M., Tauheed, F., et al.: Data-driven neuroscience: enabling breakthroughs via innovative data management. In: SIGMOD (2013)Google Scholar
  5. 5.
    Manyika, J., Chui, M.: Big data: the next frontier for innovation, competition, and productivity. In: McKinsey Global Institute (2011)Google Scholar
  6. 6.
    Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Evaluating probabilistic queries over imprecise data. In: SIGMOD (2003)Google Scholar
  7. 7.
    Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Querying imprecise data in moving object environments. TKDE 16(9), 1112–1127 (2004)Google Scholar
  8. 8.
    Trajcevski, G., Tamassia, R., Ding, H., et al.: Continuous probabilistic nearest-neighbor queries for uncertain trajectories. In: EDBT (2009)Google Scholar
  9. 9.
    Civilis, A., Jensen, C.S., Pakalnis, S.: Techniques for efficient road-network-based tracking of moving objects. TKDE 17(5), 698–712 (2005)Google Scholar
  10. 10.
    Jensen, C.S., Pakalnis, S.: Trax - real-world tracking of moving objects. In: VLDB (2007)Google Scholar
  11. 11.
    Eldawy, A., Li, Y., Mokbel, M.F., Janardan, R.: \(\text{ CG }\_\text{ Hadoop }\): computational geometry in mapreduce. In: GIS (2013)Google Scholar
  12. 12.
    Eldawy, A., Mokbel, M.F.: A demonstration of SpatialHadoop: an efficient MapReduce framework for spatial data. In: VLDB (2013)Google Scholar
  13. 13.
    Aji, A., Wang, F., Vo, H., et al.: Hadoop-GIS: a high performance spatial data warehousing system over mapreduce. In: VLDB (2013)Google Scholar
  14. 14.
    Nishimura, S., Das, S., Agrawal, D., El Abbadi, A.: MD-HBase: a scalable multi-dimensional data infrastructure for location aware services. In: MDM (2011)Google Scholar
  15. 15.
    Wang, J., Wu, S., Gao, H., et al.: Indexing multi-dimensional data in a cloud system. In: SIGMOD (2010)Google Scholar
  16. 16.
    Tsatsanifos, G., Sacharidis, D., Sellis, T.: Index-based query processing on distributed multidimensional data. GeoInformatica 17(3), 489–519 (2013)CrossRefGoogle Scholar
  17. 17.
    Ratnasamy, S., Francis, P., Handley, M., et al.: A scalable content-addressable network. In: SIGCOMM (2001)Google Scholar
  18. 18.
    Wei, L.Y., Zheng, Y., Peng, W.C.: Constructing popular routes from uncertain trajectories. In: KDD (2012)Google Scholar
  19. 19.
    Pei, T., Zhou, C., Zhu, A.-X, et al.: Windowed nearest neighbour method for mining spatio-temporal clusters in the presence of noise. In: International Journal of Geographical Information Science (2010)Google Scholar
  20. 20.
    Dalvi, N.N., Suciu, D.: Efficient query evaluation on probabilistic databases. In: VLDB (2004)Google Scholar
  21. 21.
    Pfoser, D., Jensen, C.S.: Capturing the uncertainty of moving-objects representations. In: SSDBM (1999)Google Scholar
  22. 22.
    Lian, X, Chen, L.: Monochromatic and bichromatic reverse skyline search over uncertain databases. In: SIGMOD (2008)Google Scholar
  23. 23.
    Kriegel, H.P., Kunath, P., Renz, M.: Probabilistic nearest-neighbor query on uncertain objects. In: DASFAA (2007)Google Scholar
  24. 24.
    Pugh, W.: Concurrent maintenance of lists. Technical report, Dept. of Computer Science, University of Maryland (1990)Google Scholar
  25. 25.
    Gargantini, I.: An effective way to represent octrees. Commun. ACM 25(12), 905–910 (1982)CrossRefMATHGoogle Scholar
  26. 26.
    Sidlauskas, D., Saltenis, S., Christiansen, C.W., et al.: Trees or grids?: indexing moving objects in main memory. In: GIS (2009)Google Scholar
  27. 27.
    Sidlauskas, D., Saltenis, S., Jensen, C.S.: Processing of extreme moving-object update and query workloads in main memory. VLDB J. 23(5), 817–841 (2014)CrossRefGoogle Scholar
  28. 28.
    Cheng, R., Chen, J., Mokbel, M., Chow, C.Y.: Probabilistic verifiers: evaluating constrained nearest-neighbor queries over uncertain data. In: ICDE (2008)Google Scholar
  29. 29.
    You, S., Zhang, J., Gruenwald, L.: Large-scale spatial join query processing in cloud. In: ICDE Workshops (2015)Google Scholar
  30. 30.
    Trajcevski, G., Wolfson, O., Zhang, F., Chamberlain, S.: The geometry of uncertainty in moving object databases. In: EDBT (2002)Google Scholar
  31. 31.
    Zheng, K., Trajcevski, G., Zhou, X., Scheuermann, P.: Probabilistic range queries for uncertain trajectories on road networks. In: EDBT (2011)Google Scholar
  32. 32.
    Zheng, K., Fung, G.P.C., Zhou, X.: K-nearest neighbor search for fuzzy objects. In: SIGMOD (2010)Google Scholar
  33. 33.
    Xie, X., Yiu, M.L., Cheng, R., Lu, H.: Scalable evaluation of trajectory queries over imprecise location data. TKDE 26(8), 2029–2044 (2014)Google Scholar
  34. 34.
    Tao, Y., Papadias, D.: MV3R-Tree: a spatio-temporal access method for timestamp and interval queries. In: VLDB (2001)Google Scholar
  35. 35.
    Pfoster, D., Jensen, C.S., Theodoridis, Y.: Novel approaches to the indexing of moving object trajectories. In: VLDB (2000)Google Scholar
  36. 36.
    Chakka, V.P., Everspaugh, A.C., Patel, J.M., et al.: Indexing large trajectory data sets with SETI. In: The first biennial conference on innovative data systems research (CIDR) (2003).
  37. 37.
    Tsatsanifos, G., Sacharidis, D., Sellis, T.: RIPPLE: a scalable framework for distributed processing of rank queries. In: EDBT (2014)Google Scholar
  38. 38.
    The apache cassandra project.
  39. 39.
    Cormode, G., Li, F., Yi, K.: Semantics of ranking queries for probabilistic data and expected ranks. In: ICDE (2009)Google Scholar
  40. 40.
    Born, M.: On the stability of crystal lattices. IX. Covariant theory of lattice deformations and the stability of some hexagonal lattices. In: Proceedings of the Cambridge Philosophical Society 38 (1942)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Xike Xie
    • 1
  • Benjin Mei
    • 2
  • Jinchuan Chen
    • 3
  • Xiaoyong Du
    • 3
  • Christian S. Jensen
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.School of InformationRenmin University of ChinaBeijingChina
  3. 3.Key Lab of Data Engineering and Knowledge EngineeringRenmin University of ChinaBeijingChina

Personalised recommendations