Abstract
Nowadays, there are many real-time spatial applications like location-aware services and traffic monitoring and the need for real time spatial data processing becomes more and more important. As a result, there is a tremendous amount of real-time spatial data in real-time spatial data warehouse. The continuous growth in the amount of data seems to outspeed the advance of the traditional centralized real-time spatial data warehouse. As a solution, many organizations use distributed real-time spatial data warehouse (DRTSDW) as a powerful technique to achieve OLAP (On Line Analytical Processing) analysis and business intelligence (BI). Distributing data in real time data warehouse is divided into two steps: partitioning data and their allocation into sites. Several works have proposed many algorithms for partitioning and allocation data. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly, especially in overload situations. In order to deal with this volumetry and to increase query efficiency, we propose a novel approach for partitioning data in real-time spatial data warehouse to find the right number of clusters and to divides the RTSDW into partitions using the horizontal partitioning. Secondly, we suggest our allocation strategy to place the partitions on the sites where they are most used, to minimize data transfers between sites. We have evaluated those proposed approaches using the new TPC-DS (Transaction processing performance council, http://www.tpc.org, 2014) benchmark. The preliminary results show that the approach is quite interesting.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bai, L., Lin, Z., Xu, C.: Spatiotemporal operations on spatiotemporal XML data using XQuery. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 1278–1282. IEEE (August 2016)
Bernardino, J., Madeira, H.: Experimental evaluation of a new distributed partitioning technique for data warehouses. In: International Database Engineering and Applications Symposium, pp. 312–321 (2001)
Gorawski, M., Malczok, R.: Distributed spatial data warehouse indexed with virtual memory aggregation tree. In: STDBM, pp. 25–32 (2004)
Hadjieleftheriou, M., Kollios, G., Gunopulos, D., Tsotras, V.J.: On-line discovery of dense areas in spatio-temporal databases. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750, pp. 306–324. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45072-6_18
Hamdi, S., Bouazizi, E., Faiz, S.: A speculative concurrency control in real-time spatial big data using real-time nested spatial transactions and imprecise computation. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 534–540. IEEE (October 2017)
Huang, C.Y., Liang, S.H.: LOST-Tree: a spatio-temporal structure for efficient sensor data loading in a sensor web browser. Int. J. Geogr. Inf. Sci. 27(6), 1190–1209 (2013)
Inmon, W.H.: Building the Data Warehouse. Wiley, Hoboken (2005)
Iwerks, G.S., Samet, H., Smith, K.: Continuous k-nearest neighbor queries for continuously moving points with updates. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 512–523. VLDB Endowment (September 2003)
Lee, M.L., Hsu, W., Jensen, C.S., Cui, B., Teo, K.L.: Supporting frequent updates in R-trees: a bottom-up approach. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 608–619. VLDB Endowment (September 2003)
Mokbel, M.F., Xiong, X., Aref, W.G.: SINA: scalable incremental processing of continuous queries in spatio-temporal databases. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 623–634. ACM (June 2004)
Mokbel, M.F., Xiong, X., Aref, W.G., Hambrusch, S.E., Prabhakar, S., Hammad, M.A.: PALACE: a query processor for handling real-time spatio-temporal data streams. In: Proceedings of the 13th International Conference on Very Large Data Bases, vol. 30, pp. 1377–1380. VLDB Endowment (August 2004)
Noaman, A.Y., Barker, K.: A horizontal fragmentation algorithm for fact relation in a distributed data warehouse. In: Proceedings of the 8th International Conference on Information and Knowledge Management, CIKM 1999, pp. 154–161 (November 1999)
Noaman, A.Y., Barker, K.: Distributed data warehouse architectures. J. Data Warehouse. 2(2), 37–50 (1997)
Phansalkar, S., Ahirrao, S.: Survey of data partitioning algorithms for big data stores. In: 2016 4th International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 163–168. IEEE (December 2016)
Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. ACM SIGMOD Rec. 29(2), 331–342 (2000)
Tao, Y., Papadias, D., Shen, Q.: Continuous nearest neighbor search. In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 287–298. VLDB Endowment (August 2002)
White, C.: A technical architecture for data warehousing. InfoDB J. 9(1), 5–11 (1995)
Zhou, S., Zhou, A., Tao, X., Hu, Y.: Hierarchically distributed data warehouse. In: Proceedings of the 4th International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region, Beijing, China, pp. 848–53 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hamdi, W., Faiz, S. (2020). Distributing Data in Real Time Spatial Data Warehouse. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-60239-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60238-3
Online ISBN: 978-3-030-60239-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)