Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9814
Title: Spatial aggregation: Data model and implementation
Authors: Gomez, Leticia
HAESEVOETS, Sofie 
KUIJPERS, Bart 
VAISMAN, Alejandro 
Issue Date: 2009
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Source: INFORMATION SYSTEMS, 34(6). p. 551-576
Abstract: Data aggregation in Geographic Information Systems (GIS) is a desirable feature, only marginally present in commercial systems nowadays, mostly through ad hoc solutions. We address this problem introducing a formal model that integrates, in a natural way, geographic data and non-spatial information contained in a data warehouse external to the GIS. This approach allows both aggregation of geometric components and aggregation of measures associated to those components, defined in GIS fact tables. We define the notion of geometric aggregation, a general framework for aggregate queries in a GIS setting. Although general enough to express a wide range of (aggregate) queries, some of these queries can be hard to compute in a real-world GIS environment because they involve computing an integral over a certain area. Thus, we identify the class of summable queries, which can be efficiently evaluated replacing this integral with a sum of functions of geometric objects. Integration of GIS and OLAP (On Line Analytical Processing) is supported also through a language, GISOLAP-QL. We present an implementation, denoted Met, which supports four kinds of queries: standard GIS, standard OLAP. geometric aggregation (like "total population in states with more than three airports"), and integrated GIS-OLAP queries ("total sales by product in cities crossed by a river", also allowing navigation of the results). Further, Piet implements a novel query processing technique: first, a process called subpolygonization decomposes each thematic layer in a GIS, into open convex polygons: then, another process (the overlay precomputation) computes and stores in a database the overlay of those layers for later use by a query processor. Experimental evaluation showed that for a wide class of geometric queries, overlay precomputation outperforms R-tree-based techniques, suggesting that it can be an alternative for GIS query processing. (C) 2008 Elsevier B.V.. All rights reserved.
Notes: [Kuijpers, Bart; Vaisman, Alejandro A.] Hassell Univ, Diepenbeek, Belgium. [Gomez, Leticia] Inst Tecnol Buenos Aires, Buenos Aires, DF, Argentina. [Vaisman, Alejandro A.] Univ Buenos Aires, RA-1053 Buenos Aires, DF, Argentina. [Kuijpers, Bart; Vaisman, Alejandro A.] Transnatl Univ Limburg, Limburg, Belgium.
Keywords: Data warehousing; OLAP; GIS; Aggregation
Document URI: http://hdl.handle.net/1942/9814
ISSN: 0306-4379
e-ISSN: 1873-6076
DOI: 10.1016/j.is.2009.03.002
ISI #: 000267270500005
Category: A1
Type: Journal Contribution
Validations: ecoom 2010
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
31.pdfPublished version1.02 MBAdobe PDFView/Open
Show full item record

SCOPUSTM   
Citations

22
checked on Sep 5, 2020

WEB OF SCIENCETM
Citations

21
checked on Mar 29, 2024

Page view(s)

56
checked on Sep 7, 2022

Download(s)

220
checked on Sep 7, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.