Efficient Top K Temporal Spatial Keyword Search

Abstract

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper,we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques .

Top-K Collective Spatial Keyword Queries

“…Chen et al [16] studied the direction-aware why-not spatial keyword top-k query problem, which aims to find top-k objects that best match the spatial-textual similarity of the query parameters in the given query direction. Zhang et al [17] proposed SSG-Trees index technique which used to organize spatio-temporal objects efficiently to solve top-k temporal spatial keyword queries. Qian et al [18] considered the semantic consistency of spatial web objects and query keywords.…”