Username   Password       Forgot your password?  Forgot your username? 


Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018
No.7 July 2018
No.7 July 2018
No.8 August 2018
No.8 August 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006


A Survey on Trajectory Big Data Processing

Volume 14, Number 2, February 2018, pp. 320-333
DOI: 10.23940/ijpe.18.02.p13.320333

Amina Belhassena, Hongzhi Wang

Harbin Institute of Technology, Harbin, 15007, China


Rapid advancements of location-based information provided by publicly available GPS-enabled mobiles devices boost the generation of massive trajectory data. Recently, numerous researchers have addressed many problems regarding trajectory data, which is based on storage and queries processing. Further, a wide spectrum of application domains can benefit from trajectory data mining including trajectory organization as well as queries. Therefore, large-scale trajectory data has received increasing attention in research fields as well as in industry. As the massive trajectory data processing exceeds the power of centralized approaches used previously, in this paper, we survey various existing tools used to process large-scale trajectory data in a distributed way, e.g. MapReduce, Hadoop, and Spark. Furthermore, this paper reviews an extensive collection of existing applications of movement objects, including trajectory data mining and frequent trajectory. We also propose an open interesting research direction, which is challenging and has not been explored up until now, to improve the quality of trajectory query.


References: 49

    1. R. Agrawal and R. Srikant, “Mining Sequential Patterns,” in Proceedings of the 11th International Conference Data Engineering (ICDE), pp. 3-14, 1995.
    2. E. Ahmed and M.F Mohamed, “A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data,” in Proceedings of VLDB Endrow, pp. 1230-1233, August 2013.
    3. A. Aji and F. Wang, “High Performance Spatial Query Processing for Large Scale Scientific Data,” in Proceedings of the SIGMOD/PODS PhD Symposium, pp. 9-14. New York, NY, USA, 2012.
    4. A. Aji, F. Wang, H. Vo, R. Lee, R., Q. Liu., X. Zhang, and J. Saltz, “Hadoop GIS: A High Performance Spatial Data Warehousing System over Mapreduce,” in Proceedings of the VLDB Endow, pp. 1009-1020, August 2013.
    5. M. Anna, P. Fabio., T. Roberto, and G. Fosca “WhereNext: A Location Predictor on Trajectory Pattern Mining,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637-646, New York, NY, USA, 2009.
    6. T. Ashish, S. Joydeep, J. Namit, S. Shao, C. Prasad, A. Suresh, L. Hao, W. Pete, and M. Raghotham “Hive: A Warehousing Solution over a Map-reduce Framework,” in Proceedings of the. VLDB Endow, pp. 1626-1629, August 2009.
    7. L. Avinash and M.  Prashant “Cassandra: A Decentralized Structured Storage System” in SIGOPS Oper. Syst. Rev. (ACM), vol. 44 pp. 35-40, 2010.
    8. A. Azza, B-P. Kamil, A. Daniel, A. Silberschatz, and R. Alexander. “HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads,” in Proceedings of VLDB Endowment), pp. 922-933, 2009.
    9. A. Azza, B-P. Kamil, A. Daniel, A. Silberschatz, and R. Alexander. “HadoopDB in Action: Building Real World Applications,” in Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 1111-1114, New York, NY, USA, 2010.
    10. Y. Bin, M. Qiang, Q. Weining, and Z. Aoying, “Truster: Trajectory Data Processing on Clusters” (demo paper), In DASFAA, pp. 768-771. 2009.
    11. O. Christopher, R. Benjamin., S. Utkarsh., K. Ravi, and T. Andrew, “Pig Latin: A Not-So-Foreign Language for Data Processing,” in Proceedings of International Conference on Management of Data the ACM SIGMOD. pp. 1099-1110, New York, NY, USA, 2008.
    12. H. Chunchun, K. Xionghua, L. Nianxue, and Z Qiansheng, “Parallel Clustering of Big Data of Spatio-Temporal Trajectory,” in Proceedings of the 11th International Conference on Natural Computation (ICNC), pp. 769-774, 2015.
    13. X. Dawen, W. Binfeng., L. Yantao, R. Zhuobo, and Z. Zhang, “An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division,” Discrete Dynamics in Nature and Society, vol. 18, 2015.
    14. Z. Deng and Y. Bai, “Floating Car Data Processing Model based on Hadoop-GIS Tools,” in Proceedings of the Fifth International Conference on Agro-Geoinformatics, pp. 1-4, July 2016.
    15. C. Fay, D. Jeffrey., G. Sanjay, H.C. Wilson, W.A. Deborah, B. Mike, C. Tushar, F. Andrew, and G.E. Robert, “BigTable: A Distributed Storage System for Structured Data,” ACM Trans. Computer. System., vol. 26, pp. 1-26, Jun 2008.
    16. Q. Hongjiang, G. Rong, Y. Chunfeng., and H. Yihua, “YAFIM: A Parallel Frequent Itemset Mining Algorithm with Spark,” IEEE International Parallel Distributed Processing Symposium Workshops, pp. 1664-1671, May 2014.
    17. A. H. Htoo, G. Long, and T. Kian-Lee, “Mining Sub-trajectory Cliques to Find Frequent Routes,” in Proceedings of the 13th International Symposium on Advances in Spatial and Temporal Databases, Vol.8098, New York, NY, USA, 92-109, 2013.
    18. D. Jeffrey, G. Sanjay “MapReduce: Simplified Data Processing on Large Clusters,” Commun ACM, vol. 51, pp. 107-113, January 2008.
    19. Z. Jerry and P. Jelena, “MapReduce: The programming Model and Practice,” SIGMETRICS Tutorial, 2009.
    20. Y. Jia, W. Jinxuan, and S. Mohamed, “GeoSpark: A Cluster Computing Framework for Processing Large-scale Spatial Data,” in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA, 2015.
    21. L. Jiamin Liu and R.H. Güting, “Parallel Secondo: Boosting Database Engines with Hadoop,” in Proceedings of the IEEE 18th International Conference on Parallel and Distributed Systems, pp. 738-743, December 2012.
    22. L. Jiamin Liu and R.H. Güting,” Parallel Secondo: A Practical System for Large-Scale Processing of Moving Objects,” in Proceedings of the IEEE 30th International Conference on Data Engineering, pp. 1190-1193, Marsh, 2014.
    23. G. Jian and R. Yong-gong, “Research on Improved A Priori Algorithm Based on Coding and MapReduce,” in Proceedings of the 10th Web Information System and Application Conference, pp. 294-299, 2013.
    24. H. Jian,  L. Chunwei, and Q. Jinhui . “Developing Map Matching Algorithm for Transportation Data Center,” in Proceedings of the Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 167-170, 2014.
    25. Z. Jianting, “Towards Personal High-Performance Geospatial Computing (HPC-G): Perspectives and a Case Study,” in Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems, pp. 3-10, New York, NY, USA, 2010.
    26. E. Masciari, S. Gao, and Z. Carlo, “Sequential Pattern Mining from Trajectory Data,” in Proceedings of the 17th International Database Engineering Applications Symposium, pp. 162-167, New York, NY, USA, 2013.
    27. Z. Matei and C. Mosharaf, “Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing,” in Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, pp. 2-2, Berkeley, CA, USENIX Association, 2012.
    28. I. Michael, B. Mihai, Y. Yuan, B. Andrew, and F. Dennis, “Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks,” EuroSys. 2007.
    29. M. Mikolaj, “Prediction of Moving Object Location Based on Frequent Trajectories,” in Proceedings of the 21st International Conference on Computer and Information Sciences, pp. 583-592, Berlin, 2006.
    30. M. Mikolaj, “Mining Frequent Trajectories of Moving Objects for Location Prediction,” in Proceedings of the Fifth International Conference on Machine Learning and Data Mining in Pattern Recognition, Vol. 4571 of lecture notes in Computer science, pp. 667-680. 2007.
    31. Z. J. Mohammed, P. Srinivasan, O. Mitsunori, and L. Wei, “New Algorithms for Fast Discovery of Association Rules,” in Proceedings of the third International Conference on Knowledge Discovery and Data Mining, pp283-286, Newport, Beach, 1997.
    32. S. Natalijia and S. Dragan, “Processing and Analysis of Big Trajectory Data using MapReduce,” Facta Universitatis, Series: Automatic Control and Robotics, vol. 14, pp. 19-27, 2015.
    33. L. Ning,, H. Qing, and S. ZhongZhi, “Parallel Implementation of Apriori Algorithm Based on MapReduce,” in Proceedings of the 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel Distributed Computing, pp. 236-241, 2012.
    34. H. Patrick, K. Mahadev, J.P Flavio, and R. Benjamin, “ZooKeeper: Wait-Free Coordination for Internet-Scale Systems,” in Proceedings of the USENIX Annual Technical Conference, pp. 11-11, Berkeley, CA: USENIX Association, 2010.
    35. M. Qiang., Y. Bin, Q. Weining, and Z. Aoying, “Query Processing of Massive Trajectory Data Based on Mapreduce,” in Proceedings of the First International Workshop on Cloud Data Management. pp. 9-16, New York, NY, USA,2009.
    36. G. H. Ralf, B. Thomas, and D. Christian “SECONDO: A Platform for Moving Objects Database Research and for Publishing and Integrating Research Implementations,” IEEE Data Eng. Bull., Vol. 33, pp. 56-63, 2010.
    37. G. Sanjay, G. Howard, and L. Shun-Tak, “The Google File System,” in Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles. pp. 29-43, New York, NY, USA, 2003.
    38. R. Sanjay., K. Manohar, and K. Arti, “R-Apriori: An Efficient Apriori Based Algorithm on Spark,” in Proceedings of the 8th Workshop on Ph.D. Workshop in Information and Knowledge Management. pp. 27-34, New York, NY, USA, 2015.
    39. Z. Shubin, H. Jizhong L. Zhiyong, W. Kai, and X. Zhiyong, “SJMR: Parallelizing Spatial Join with MapReduce on Clusters,” in Proceedings of the IEEE International Conference on Cluster Computing and Workshops, pp. 1-8, 2009.
    40. T. Suryakanthi and V. S. J. Pallapolu. “A Comparative Study on Performance of Hadoop File System with MapR File System to Process Big Data Records,” in IJCSI International Journal of Computer Science Issues, vol. 13, pp. 129-140, 2016.
    41. K. Wang, J. Han, B. Tu, J. Dai, W. Zhou, and X. Song, “Accelerating Spatial Data Processing with MapReduce,” in Proceedings of the IEEE 16th International Conference on Parallel and Distributed Systems, pp. 229-236, December 2010.
    42. X. Wang., X. Liu, B. Liu, E. N. de Souza, and S. Matwin, “Vessel Route Anomaly Detection with Hadoop MapReduce,” in Proceedings of the. IEEE International Conference on Big Data, pp. 25-30, October 2014.
    43. E. Wiam, W. Ali, and A.M. Adel, “An Investigation of Parallel Road Map Inference from Big GPS Traces Data,” Procedia Computer Science, vol. 53, pp. 131-140, 2015.
    44. Y. Xian, C. Xu, and Y. Liu. “Implementing Trajectory Data Stream Analysis in Parallel,” in Proceedings of the IEEE International Conference on Big Data, pp. 2431-2436, 2016.
    45. D. Xie, L. Feifei, Y. Bin, L. Gefei, Z. Liang, and G. Minyi. “Simba: Efficient In-Memory Spatial Analytics,” in Proceedings of the International Conference on Management of Data. New York, NY, USA, 2016.
    46. H. Yang, D. Ali, H. Ruey-Lung, and P. D. Stott, “Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters,” in Proceedings of the International Conference on Management of Data ACM SIGMOD. New York, NY, USA, 2007.
    47. L. Yanhua, C. Chi-Yin, D. Ke, Y. Mingxuan, Z. Jia, Z. Jia-Dong, Y. Qiang, and Z. Zhi-Li, “Sampling Big Trajectory Data,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 941-950, New York, NY, USA, ACM.
    48. K. Yongchul, N. Dylan, G.P, Jeffrey, B. Magdalena, H. Bill Howe, and L. Sarah. “Scalable Clustering Algorithm for N-Body Simulations in a Shared-Nothing Cluster,” in Proceedings of the 22nd SSDBM Conference, 2010.
    49. Z. Yuanchun, Z. Yang, G. Yong, X. Zhenghua, F. Yanjie, G. Danhuai, S. Jing, Z. Tiangang, W. Xuezhi, and Li. Jianhui., “An Efficient Data Processing Framework for Mining the Massive Trajectory of Moving Objects,” Computers Environment and Urban Systems, Vol. 61, pp. 129-140, 2017.


      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      Download this file (IJPE-2018-02-13.pdf)IJPE-2018-02-13.pdf[A Survey on Trajectory Big Data Processing]599 Kb


      Prev Next

      Program Disturb Research and Error Avoidance Algorithm Design of 3D-TLC NAND Flash Memory

      Xiaoshan Yang, Ligu Zhu, Qicong Zhang, Meng Zhang, Fei Wu, and Wei Zhang

      Read more

      Data Complexity Analysis for Software Defect Detection

      Ying Ma, Yichang Li, Junwen Lu, Peng Sun, Yu Sun, and Xiatian Zhu

      Read more

      Fuzzy AHP-based Comprehensive Evaluation for Smart Grid in Energy Internet Systems

      Ying Ma, Yichang Li, Shunzhi Zhu, Nan Qin, Guang Zhao, and Chao Huang

      Read more

      User Group-based Method for Cold-Start Recommendation

      Jing He, Shuo Yuan, Yi Xiang, and Wei Zhou

      Read more

      Object Tracking Method based on 3D Cartoon Animation in Broadcast Soccer Videos

      Chunlong Xie, Zhiqian Zhang, Chunsheng Wang, and Zhengqing Liu

      Read more

      Image Encryption Method based on Hill Matrix and Dynamic DNA Encoding

      Xuncai Zhang, Zheng Zhou, Yishan Liu, Guangzhao Cui, Ying Niu, and Yanfeng Wang

      Read more

      Video Indexing and Retrieval based on Key Frame Extraction

      Wenshi Wang, Zhangqin Huang, Weidong Wang, Shuo Zhang, and Rui Tian

      Read more

      Modeling Approach Combining Performance and Reliability for Mobile Cloud System

      Han Xu, Haiqing Wang, Liang Luo, Xiwei Qiu, Sa Meng, and Xun Guo

      Read more

      Understanding the Similarity of Log Revision Behaviors in Open Source Software

      Xu Niu, Shanshan Li, Zhouyang Jia, Shulin Zhou, Wang Li, and Xiangke Liao

      Read more

      Learning to Predict Price based on E-commerce Online Auction Machine

      Xiaohui Li, Hongbin Dong, Xiaowei Wang, and Shuang Han

      Read more

      Rate Control Algorithm for Multiview Video Coding based on Human Visual Characteristics

      Tao Yan, In-Ho Ra, Qiuwen Zhang, Hui Wen, Hang Xu, and Shuqing Chen

      Read more
      This site uses encryption for transmitting your passwords.