Geo-Social Top-k and Skyline Keyword Queries on Road Networks

Abstract

The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

“…The proposed UBA clearly outperforms sequential algorithms when the query points exhibit a highly skewed distribution. In future, we plan to apply this unified batch solution to extremely large spatial networks for distributed batch processing of sophisticated spatial queries, e.g., spatial join queries [ 54 ] and spatial keyword queries [ 2 , 50 ].…”

“…(Spatial network [ 3 , 9 , 11 , 25 , 26 , 41 , 50 , 51 ]) . A dynamic spatial network can be described as a dynamic weighted graph , where V, E, and W indicate the vertex set, edge set, and edge distance matrix, respectively.…”

Optimized Algorithm for Skyline Community Discovery in Multi-Valued Networks

“…According to the concepts of k-core [17], [18], [21] and skyline [19], [25], the skyline community is defined in the multi-valued graph. Therefore, a skyline community in the multi-valued graph G should be cohesive and not be dominated by other communities.…”

Efficient Processing of Top-k Frequent Spatial Keyword Queries

“…Some efforts 14,16,22,23,[24][25][26] used the pivot based hierarchical method to explore the relationship between textual keywords of spatial objects to answer semantic frequent TkSKQ. Other efforts made so far focused to maintain users social relationships and respond to social-aware TkSKQ and some typical queries configured for such purposes include: the geo-social skyline keyword query (GSSK) 15 , social TkSKQ 27 , social-aware top-k spatial keyword (SkSK) query 12 , socio-spatial skyline query (SSSQ) 28 , and top-k frequent spatiotemporal terms (kFST) query 13 . In these studies, some invert table-based index structures are employed to organize textual keywords of spatial objects, and the generalized knowledge, i.e.…”

“…They employ, R-tree 1,2,4,6,10 , spatial grid 5,7 , quadtree 3,8,11 , TMIS 9 , etc. to organize the spatial information of spatial objects and employ table-based structure [12][13][14][15][16] to maintain the textual keyword information to achieve top-k frequent spatial keyword queries. Since, in STDB, the textual keywords of spatial object are diverse and complex, the number of frequent features of them is often more than data itself, and the retrieval of frequent features by above table-based index structure still needs a high cost.…”