Keyword Search on Spatial Databases

By Ian De Felipe, Vagelis Hristidis, N. Rishe. IEEE International Conference on Data Engineering, 2008

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Collective spatial keyword querying (2011, background): “Several recently proposed hybrid indexes [5, 8, 10, 14, 15] that tightly integrate spatial indexing (e.”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “ean Range and Top-k Query.Hariharan et al. [84] the use of Boolean range queries to find all objects containing query keywords, all which must be located within a bounded range. Boolean Top-k queries [55] find thek nearest objects using conjunctive Boolean constraints on the text, i.e., it conducts ”
Spatial Keyword Query Processing: An Experimental Evaluation (2013, methodology): “The IR 2 -tree [9] augments each node of the R-tree with a signature ﬁle (bitmap) to capture the text information of the objects in the n-ode’s subtree.”
Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects (2009, methodology): “This approach exhibits the same problems as do signature-file based indexing [7].”
Keyword Search in Spatial Databases: Towards Searching by Document (2009, methodology): “[10] proposed another similar query combining k-NN query and keyword search, and uses a hybrid index of R-tree and signature file, called the IR(2).”
IR-Tree: An Efficient Index for Geographic Document Search (2011, methodology): “Along the same line, IR(2)-tree [8] builds an R-tree and uses signature files (rather than a set of words) to record the document words associated with nodes in the index.”