Spatial Keyword Queries: Top k-Spatial Keyword Search (TOPK-SK)

Abstract

With advances in geo-positioning technologies and geo-location services, there unit of measurement a
speedily growing amount of spatiotextual objects collected in many applications like location based services and social
networks, inside that an object is delineate by its spatial location and a set of keywords (terms). Consequently, the
study of spatial keyword search that explores every location and matter description of the objects has attracted nice
attention from the economic organizations and analysis communities. Inside the paper, we tend to tend to check two
basic problems inside the spatial keyword queries: high k spatial keyword search (TOPK-SK), and batch high k spatial
keyword search (BTOPK-SK). Given a set of spatio-textual objects, question an issue location and a set of question
keywords, the TOPK-SK retrieves the highest k objects each of that contains all keywords inside the question. BTOPKSK is that the process of sets of TOPK-SK queries[1]. Supported the inverted index and thus the linear quadtree, we
tend to tend to propose a totally distinctive index structure, called inverted linear quadtree (IL- Quadtree), that's
strictly designed to require advantage of every spatial and keyword based pruning techniques to effectively prune the
search area [2] [3]. A cost-effective formula is then developed to tackle high k spatial keyword search. To any enhance
the filtering capability of the signature of linear quadtree [4], we tend to tend to propose a partition based
methodology. in addition, to change BTOPK-SK, we tend to tend to vogue a greenhorn computing paradigm that
partition the queries into groups supported every spatial proximity and thus the matter connectedness between queries
[5] [6]. we tend to tend to point out that the IL-Quadtree technique might efficiently support BTOPK-SK.
Comprehensive experiments on real and artificial info clearly demonstrate the efficiency of our methods.