Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects

By Gao Cong, Christian S. Jensen, Dingming Wu. Proceedings of the VLDB Endowment, 2009

675 citations recorded by the source corpus.

The conventional Internet is acquiring a geo-spatial dimension. Web documents are being geo-tagged, and geo-referenced objects such as points of interest are being associated with descriptive text documents. The resulting fusion of geo-location and documents enables a new kind of top-k query that takes into account both location proximity and text relevancy. To our knowledge, only naive techniques exist that are capable of computing a general web information retrieval query while also taking location into account. This paper proposes a new indexing framework for location-aware top-k text retrieval. The framework leverages the inverted file for text retrieval and the R-tree for spatial proximity querying. Several indexing approaches are explored within the framework. The framework encompasses algorithms that utilize the proposed indexes for computing the top-k query, thus taking into account both text relevancy and location proximity to prune the search space. Results of empirical studies with an implementation of the framework demonstrate that the paper's proposal offers scalability and is capable of excellent performance.

Subfields: Computer Networks and Communications, Geography, Planning and Development, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6f556cfb6da2e07de1bce8960a698d26164ebbb2

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Citations in the corpus

Collective spatial keyword querying (2011, methodology): “The IR-tree [8] is essentially an R-tree [12] extended with inverted files [16].”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “Linear combinations of two distinct similarity measures is a common solution for spatial-textual data [41, 195].”
Mining significant semantic locations from GPS data (2010, background): “Another example is that the outcome can be combined with the so-called location-aware keyword query [6].”
Spatial Keyword Query Processing: An Experimental Evaluation (2013, methodology): “To distinguish it from the IR-tree in references [7,20], we refer to it as the IRLi-tree.”