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Erschienen in:
2014 | OriginalPaper | Buchkapitel
HCDLST: An Indexing Technique for Current and Recent-Past Sliding Window Spatio-Temporal Data
verfasst von : Kuleshwar Sahu, Sangharatna J. Godboley, S. K. Jain
Erschienen in: Intelligent Computing, Networking, and Informatics
Verlag: Springer India
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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.