Efficient Geo-Textual Hybrid Indexing Techniques for Moving Objects and Queries
Sulbha Kishor Powar1, Ganesh Magar2
1Sulbha Kishor Powar, Research Scholar, Department of Computer Science, Women’s University, Santacruz (West), Mumbai, Maharashtra, India.
2Dr. Ganesh M. Magar, Associate Professor, Department of Computer Science, Women’s University, Santacruz (West), Mumbai, Maharashtra, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4419-4428 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9142038620/2020©BEIESP | DOI: 10.35940/ijrte.F9142.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Advancements of various Geographic Information Technologies have resulted in huge growth in Geo-Textual data. Many Indexing and searching algorithms are developed to handle this Geo-Textual data which contains spatial, textual and temporal information. In past, Indexing and searching algorithms are developed for the applications in which the object trajectory or velocity vector is known in advance and hence we can predict the future position of the objects. There are real time applications like emergency management systems, traffic monitoring, where the objects movements are unpredictable and hence future position of the objects cannot be predicted. Techniques are required to answer the geo-textual kNN query where the velocity vectors or trajectories of moving and moving queries are not known. In case of moving objects, capturing current position of the object and maintaining spatial index optimally is very much essential. The hybrid indexing techniques used earlier are based on R-tree spatial index. The nodes of the R-tree index structure are split or merged to maintain the locations of continuously moving objects, increasing the maintenance cost as compared to the grid index. In this paper a solution is proposed for creating and maintaining hybrid index for moving objects and queries based on grid and inverted list hybrid indexing techniques. The method is also proposed for finding Geo-Textual nearest neighbours for static and moving queries using hybrid index and conceptual partitioning of the grid. The overall gain reported by the experimental work using hybrid index over the non- hybrid index is 30 to 40 percent depending on the grid size chosen for mapping the data space and on the parameters of queries.
Keywords: Conceptual Partitioning, Grid, Hybrid Index, Inverted List, Nearest Neighbour
Scope of the Article: Artificial Intelligent Methods, Models, Techniques.