Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings

In the past few years, the growth of urban area has been increasing and has resulted immense number of urban datasets. This situation contributes to the difficulties in handling and managing issues related to urban area. Huge and massive datasets can degrade the performance of data retrieval and inf...

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Main Author:	Azri, Nor Suhaibah
Format:	Thesis
Language:	English
Published:	2017
Subjects:	G70.212-70.215 Geographic information system
Online Access:	http://eprints.utm.my/id/eprint/78844/1/NorSuhaibahAzriPFGHT2017.pdf
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spelling	my-utm-ep.788442018-09-17T04:21:10Z Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings 2017-01 Azri, Nor Suhaibah G70.212-70.215 Geographic information system In the past few years, the growth of urban area has been increasing and has resulted immense number of urban datasets. This situation contributes to the difficulties in handling and managing issues related to urban area. Huge and massive datasets can degrade the performance of data retrieval and information analysis. In addition, urban environments are very difficult to manage because they involved with various types of data, such as multiple types of zoning themes in urban mixeduse development. Thus, a special technique for efficient data handling and management is necessary. In this study, a new three-dimensional (3D) spatial access method, the Voronoi Classified and Clustered Data Constellation (VOR-CCDC) is introduced. The VOR-CCDC data structure operates on the basis of two filters, classification and clustering. To boost up the performance of data retrieval, VORCCDC offers a minimal percentage of overlap among nodes and a minimal coverage area in order to avoid repetitive data entry and multi-path queries. Besides that, VOR-CCDC data structure is supplemented with an extra element of nearest neighbour information. Encoded neighbouring information in the Voronoi diagram allows VOR-CCDC to optimally explore the data. There are three types of nearest neighbour queries that are presented in this study to verify the VOR-CCDC’s ability in finding the nearest neighbour information. The queries are Single Search Nearest Neighbour query, k Nearest Neighbour (kNN) query and Reverse k Nearest Neighbour (RkNN) query. Each query is tested with two types of 3D datasets; single layer and multi-layer. The test demonstrated that VOR-CCDC performs the least amount of input/output than their best competitor, the 3D R-Tree. Besides that, VOR-CCDC is also tested for performance evaluation. The results indicate that VOR-CCDC outperforms its competitor by responding 60 to 80 percent faster to the query operation. In the future, VOR-CCDC structure is expected to be expanded for temporal and dynamic objects. Besides that, VOR-CCDC structure can also be used in other applications such as brain cell database for analysing the spatial arrangement of neurons or analysing the protein chain reaction in bioinformatics applications. 2017-01 Thesis http://eprints.utm.my/id/eprint/78844/ http://eprints.utm.my/id/eprint/78844/1/NorSuhaibahAzriPFGHT2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:105971 phd doctoral Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformation and Real Estate
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	G70.212-70.215 Geographic information system
spellingShingle	G70.212-70.215 Geographic information system Azri, Nor Suhaibah Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings
description	In the past few years, the growth of urban area has been increasing and has resulted immense number of urban datasets. This situation contributes to the difficulties in handling and managing issues related to urban area. Huge and massive datasets can degrade the performance of data retrieval and information analysis. In addition, urban environments are very difficult to manage because they involved with various types of data, such as multiple types of zoning themes in urban mixeduse development. Thus, a special technique for efficient data handling and management is necessary. In this study, a new three-dimensional (3D) spatial access method, the Voronoi Classified and Clustered Data Constellation (VOR-CCDC) is introduced. The VOR-CCDC data structure operates on the basis of two filters, classification and clustering. To boost up the performance of data retrieval, VORCCDC offers a minimal percentage of overlap among nodes and a minimal coverage area in order to avoid repetitive data entry and multi-path queries. Besides that, VOR-CCDC data structure is supplemented with an extra element of nearest neighbour information. Encoded neighbouring information in the Voronoi diagram allows VOR-CCDC to optimally explore the data. There are three types of nearest neighbour queries that are presented in this study to verify the VOR-CCDC’s ability in finding the nearest neighbour information. The queries are Single Search Nearest Neighbour query, k Nearest Neighbour (kNN) query and Reverse k Nearest Neighbour (RkNN) query. Each query is tested with two types of 3D datasets; single layer and multi-layer. The test demonstrated that VOR-CCDC performs the least amount of input/output than their best competitor, the 3D R-Tree. Besides that, VOR-CCDC is also tested for performance evaluation. The results indicate that VOR-CCDC outperforms its competitor by responding 60 to 80 percent faster to the query operation. In the future, VOR-CCDC structure is expected to be expanded for temporal and dynamic objects. Besides that, VOR-CCDC structure can also be used in other applications such as brain cell database for analysing the spatial arrangement of neurons or analysing the protein chain reaction in bioinformatics applications.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Azri, Nor Suhaibah
author_facet	Azri, Nor Suhaibah
author_sort	Azri, Nor Suhaibah
title	Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings
title_short	Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings
title_full	Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings
title_fullStr	Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings
title_full_unstemmed	Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings
title_sort	voronoi classfied and clustered constellation data structure for three-dimensional urban buildings
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformation and Real Estate
publishDate	2017
url	http://eprints.utm.my/id/eprint/78844/1/NorSuhaibahAzriPFGHT2017.pdf
_version_	1747818085184700416

Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings

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