Collective spatial keyword queries

Abstract

With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries are ubiquitous in real life. One example of spatial-keyword query is the so-called collective spatial keyword query (CoSKQ) which is to find for a given query consisting a query location and several query keywords a set of objects which covers the query keywords collectively and has the smallest cost wrt the query location. In the literature, many different functions were proposed for defining the cost and correspondingly, many different approaches were developed for the CoSKQ problem. In this paper, we study the CoSKQ problem systematically by proposing a unified cost function and a unified approach for the CoSKQ problem (with the unified cost function). The unified cost function includes all existing cost functions as special cases and the unified approach solves the CoSKQ problem with the unified cost function in a unified way. Experiments were conducted on both real and synthetic datasets which verified our proposed approach.

Fraction-Score: A Generalized Support Measure for Weighted and Maximal Co-Location Pattern Mining

“…Proof. We prove by reduction from the existing Collective Spatial Keyword Query with the Diameter cost function (Dia-CoSKQ) problem [24], [7] which is NP-hard.…”

“…It could be verified that if the diameter of S ∪ {q} is at most d, the answer of the OIRI is "yes"; otherwise, the answer is "no". Based upon this, we can utilize the exact algorithm proposed in [24] for OIRI. Note that we could do slightly better by adopting an early-stopping strategy that whenever a set S with the diameter of S ∪ {q} at most d is found, it returns "yes" immediately.…”

“…)) [24], where n 1 (n 1 << |O|) is the number of objects that carry a label t ∈ C − {o.t}, k 3 (k 3 << |O|) is the number of objects shared by results of range queries. The complexity of Combinatorial-Search is O(…”