Abstract
There are several applications such as wireless communication and geographical information system that use both spatial and temporal data. Since last decade, researchers are working on the current and limited past data for query processing that leads to the development of data stream management system (DSMS). Mostly, DSMSs are application specific. Earlier spatial and temporal data were managed by historical data management systems and did not have support for real-time data processing. In this paper, we propose an idea for indexing sliding window spatio-temporal data, which efficiently updates evolution of the objects’ positions and maintains the current and recent-past data for query processing. It also efficiently deletes the obsolete data that have no further use. We also propose a hash table and a doubly circular linked list-based technique which efficiently manages the index updates and time range queries for real-time data management.
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
References
Mokbel, M.F., Xiong, X., Hammad, M.A., Aref, W.G.: Continuous Query Processing of Spatio-Temporal Data Streams in Place. Kluwer Academic Publishers (2004)
Carney, D., Cetinternel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., Tatbul, N., Zdonik, S.: Monitoring streams: a new class of data management applications. In: Proceedings of the International Conference on Very Large Data Bases, pp. 215–226 (2002)
Golab, L., Ozsu, M.T.: Processing sliding window multi-joins in continuous queries over data streams. In: Proceedings of the 29th VLDB Conference (2003)
Tao, Y., Papadias, D.: MV3R-Tree: a spatio-temporal access method for time slice and interval queries. In: Proceeding of VLDB Conference (2001)
Botea, V., Mallett, D., Nascimento, M.A., Sander, J.: PIST: an efficient and practical indexing technique for historical spatio-temporal point data. GeoInformatica 12, 143–168 (2008)
Singh, M., Zhu, Q., Jagadish, H.V.: SWST: a disk based index for sliding window spatio-temporal data. In: IEEE Conference (2012)
Meškovi, E., Gali, Z., Baranovi, M.: Managing moving objects in spatio-temporal data streams. In: 12th IEEE International Conference on Mobile Data Management (2011)
Theodoridis, Y., Sellis, T., Papadopoulos, A.N., Manolopoulos, Y.: Specifications for efficient indexing in spatiotemporal databases. In: IEEE SSDBM’98 (1998)
Prasad Chakka, V., Everspaugh, A.C., Patel, J.M.: Indexing large trajectory data sets with SETI. In: Proceedings of the CIDR Conference (2003)
Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J. 15(2), 121–142 (2006)
Gutting, R.H., Schineider, M.: Moving Object Database. Elsevier Inc. (2005)
Golab, L., Ozsu, M.: Issues in data stream management. Sigmod Rec. 32(2), 5–14 (2003)
Tamer Özsu, M., Valduriez, P.: Principles of Distributed Database Systems. Pearson Education, Inc (2011)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART ACM, (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Sahu, K., Godboley, S.J., Jain, S.K. (2014). HCDLST: An Indexing Technique for Current and Recent-Past Sliding Window Spatio-Temporal Data. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_96
Download citation
DOI: https://doi.org/10.1007/978-81-322-1665-0_96
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1664-3
Online ISBN: 978-81-322-1665-0
eBook Packages: EngineeringEngineering (R0)