C-HLRD: a category-based hierarchical localisation technique for resource discovery in grid environments

Mollamotalebi, Mahdi; Ismail, Abdul Samad; Ahmed, Aboamama Atahar

doi:10.1504/ijwgs.2013.055723

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Clustered Objects Region Queries1

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2015

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“…These clusters can be regarded as "super objects" -an object which consists of a cluster of the desired objects of interest (e.g. clusters of Chinese restaurants in a shopping centre) [27,15,18]. Also note from this example, a cluster known as Chadstone Shopping Complex on the northwest of the query point is only partially covered by the query radius.…”

Section: Clustered Objects Region Queriesmentioning

confidence: 99%

A taxonomy for region queries in spatial databases

Taniar

Rahayu

2015

Journal of Computer and System Sciences

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In spatial databases, there are two basic types of queries, namely nearest neighbour queries (kNN) and range queries. Spatial range queries are not only finding objects of interest within a certain range or radius, but feature a wide spectrum: from finding objects of interest to forming the range (or region). Therefore, in this paper, we coin a term "Region Queries" to indicate a broad category of spatial range queries. It is imperative to understand the full capabilities of region queries, before starting to work on processing and optimising such queries. The aim of this paper is to show a complete picture of region queries. In this study, we present taxonomy of region queries, comprising of three categories: (i) finding objects of interest, (ii) forming regions, and (iii) determining centroids. These three query types form a comprehensive view of what region queries are about.

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Section: Clustered Objects Region Queriesmentioning

confidence: 99%