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Indexing the past and current position of moving objects in large-scale dataset
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Mohamad Reza Abbasifard   , Hassan Naderi * |
| Iran University of Science and Technology |
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Abstract: (3402 Views) |
| By increasing intelligent transportation systems (ITS) and location based services (LBS) that take advantage of spatio-temporal data, these data have increased the necessity for new indexing techniques. Indexing methods index these data generally in the past, present or future. Creating an integrated index for indexing data and also answering to various queries which can reduce indices’ updating time, is one of the challenges. The current study introduces an integrated method called “PCPI” (Past and Current Position Indexing) to index and store spatio-temporal data of the past and present in a simultaneous manner in the disk and main memory respectively that has ability to answer various spatio-temporal queries. PCPI uses a same resources for processing and creating indices in two different times. In this method, two data structure is used integratedly: the first data structure indexes and stores current position of moving objects in the main memory, and the second data structure on disk for trajectory data of moving objects that have high volume and cannot be stored in main memory. In addition, PCPI uses map matching methods to remove noises – e.g. stationary state noises- in the data received from the moving objects; this feature adds to accuracy and reliability of the query results. Effects of data reduction techniques on accelerating indexing and query processing and reducing disk space consumption (in massive datasets) were examined. Results of the comparisons made based on the experiments showed higher efficiency of the indexing structure. |
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| Keywords: Moving Objects Databases, Trajectory Indexing, Spatio-Temporal Data, Query Processing |
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Full-Text [PDF 1301 kb]
(1094 Downloads)
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Type of Study: Research |
Subject:
GIS
Received: 2017/10/8 | Accepted: 2017/10/8 | Published: 2017/10/8
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