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STQP: Spatio-Temporal Indexing and Query Processing

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Nikhat Fatima, Ayesha Ameen, Syed Raziuddin

Nikhat Fatima, Ayesha Ameen and Syed Raziuddin. STQP: Spatio-Temporal Indexing and Query Processing. International Journal of Computer Applications 150(10):5-9, September 2016. BibTeX

	author = {Nikhat Fatima and Ayesha Ameen and Syed Raziuddin},
	title = {STQP: Spatio-Temporal Indexing and Query Processing},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2016},
	volume = {150},
	number = {10},
	month = {Sep},
	year = {2016},
	issn = {0975-8887},
	pages = {5-9},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2016911514},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this ongoing work, the location-aware ranking query (LRQ) are considered, an important category of location-aware query. Types of location-aware ranking query are the k-nearest neighbour (NN) query and location-aware keyword query(LKQ). NN LKQs and inquiries have vast applications in many domains. However, there are a great number of location-aware datasets that demand better and flexible location aware rank queries. They are a lot more complex than spatio-textual objects. These things are termed as location-aware things. For location-aware things, simple NN LKQs and queries may well not be expressive enough to find the objects of interests. In this particular proposed work the generic location-aware rank query is formulated, which retrieves the objects satisfying a query predicate, ranks and returns the full total results predicated on spatial proximity, textual relevance's and measures extracted from attribute values. We create a construction called location aware indexing and query processing(LINQ), for useful indexing and querying of GLRQs. LINQ evolves the synopses tree to work with synopses of non-spatial features, and combines the synopses tree with other indexes to query and index the GLRQ. The global buckets can be used to provide efficiency and faster computation time by using Bin sort algorithm this proposed method is recognized as STQP. The increased proposed system will provide better results with respect to faster and output for spatial query results.


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Location Aware, Query, Synopsys Tree