Abstract
The cutting-edge cloud-based computing platforms are a typical solution for the tremendous volumes of the moving object trajectories and the vast trajectory-driven applications. However, many challenges have been raised by the adopted distributed platforms, the nature of the trajectories, the diversity in query types, the enormous options of computing resources, etc. We propose a Dynamic Moving Object Index that is able to adapt to the changes in a dynamic environment while maximizing the benefits out of the available resources without any fine-tuning. It balances the index structure between the spatial and object localities in order to control the parallelism capacity, the communication overhead, and the computation distribution. The proposed index has innovative global and local indexes that implement several optimization approaches in order to contain the impact of balancing the locality pivot in a dynamic environment. We also conduct extensive experiments on two datasets and various queries including space-based, time-based, and object-based query types. The experiment study shows a significant performance improvement compared to existing indexing schemes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
CPModel is a light-weight polynomial regression model that is already trained. For more details about CPModel and its features, please refer to our previous work [2].
- 2.
TOver is used to reveal the ratio of the MBRs of the trajectories with respect to the global scope.
- 3.
It is subject to a good hashing method. We use Java 1.8 HashMap.
References
Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., Saltz, J.: Hadoop GIS: a high performance spatial data warehousing system over MapReduce. Proc. VLDB Endow. 6(11), 1009–1020 (2013). https://doi.org/10.14778/2536222.2536227
Alqahtani, O., Altman, T.: A resilient large-scale trajectory index for cloud-based moving object applications. Appl. Sci. 10(20) (2020). https://doi.org/10.3390/app10207220
Apache Spark. https://spark.apache.org/. Accessed 31 Jan 2021
Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002). https://doi.org/10.1023/A:1015231126594
Eldawy, A., Mokbel, M.F.: SpatialHadoop: a mapreduce framework for spatial data. In: Proceedings of the 2015 IEEE 31st International Conference on Data Engineering, pp. 1352–1363 (2015). https://doi.org/10.1109/ICDE.2015.7113382
JTS Topology Suite. https://locationtech.github.io/jts/. Accessed 31 Jan 2021
Keng Liao, W., Liu, Y., Choudhary, A.: A grid-based clustering algorithm using adaptive mesh refinement. In: Proceedings of the 7th Workshop on Mining Scientific and Engineering Data Sets (2005)
Ma, Q., Yang, B., Qian, W., Zhou, A.: Query processing of massive trajectory data based on MapReduce. In: Proceedings of the First International Workshop on Cloud Data Management, CloudDB 2009, pp. 9–16. ACM (2009). https://doi.org/10.1145/1651263.1651266
Nishimura, S., Das, S., Agrawal, D., Abbadi, A.E.: MD-HBase: a scalable multi-dimensional data infrastructure for location aware services. In: Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management, pp. 7–16 (2011)
OpenStreetMap contributors: Planet dump. https://planet.osm.org. https://www.openstreetmap.org. Accessed 31 Jan 2021
OpenStreetMap contributors: Planet GPX. https://planet.openstreetmap.org/gps/. Accessed 31 Jan 2021
Peixoto, D.A., Hung, N.Q.V.: Scalable and fast top-k most similar trajectories search using MapReduce in-memory. In: Proceedings of the Databases Theory and Applications, pp. 228–241. Springer (2016)
Robinson, J.T.: The K-D-B-tree: a search structure for large multidimensional dynamic indexes. In: Proceedings of the 1981 ACM SIGMOD International Conference on Management of Data, SIGMOD 1981, pp. 10–18. ACM (1981). https://doi.org/10.1145/582318.582321
Schikuta, E.: Grid-clustering: an efficient hierarchical clustering method for very large data sets. In: Proceedings of 13th International Conference on Pattern Recognition, vol. 2, pp. 101–105 (1996). https://doi.org/10.1109/ICPR.1996.546732
Tang, M., Yu, Y., Malluhi, Q.M., Ouzzani, M., Aref, W.G.: LocationSpark: a distributed in-memory data management system for big spatial data. Proc. VLDB Endow. 9(13), 1565–1568 (2016). https://doi.org/10.14778/3007263.3007310
Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., Murthy, R.: Hive: a warehousing solution over a map-reduce framework. Proc. VLDB Endow. 2(2), 1626–1629 (2009). https://doi.org/10.14778/1687553.1687609
Wang, H., Belhassena, A.: Parallel trajectory search based on distributed index. Inf. Sci. 388–389, 62–83 (2017)
Wang, H., Zheng, K., Xu, J., Zheng, B., Zhou, X., Sadiq, S.: SharkDB: an in-memory column-oriented trajectory storage. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014, pp. 1409–1418. ACM (2014)
You, S., Zhang, J., Gruenwald, L.: Large-scale spatial join query processing in cloud. In: Proceedings of the 31st IEEE International Conference on Data Engineering Workshops (ICDEW), pp. 34–41 (2015). https://doi.org/10.1109/ICDEW.2015.7129541
Yu, J., Wu, J., Sarwat, M.: 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, SIGSPATIAL 2015, pp. 70:1–70:4. ACM (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Alqahtani, O., Altman, T. (2021). An Adaptive Large-Scale Trajectory Index for Cloud-Based Moving Object Applications. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-75075-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75074-9
Online ISBN: 978-3-030-75075-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)