3D Partition-Based Clustering for Supply Chain Data Management

A. Suhaibah, U. Uznir, François Anton, Darka Mioc, A. A. Rahman

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

Supply Chain Management (SCM) is the management of the products and goods flow from its origin point to point of consumption. During the process of SCM, information and dataset gathered for this application is massive and complex. This is due to its several processes such as procurement, product development and commercialization, physical distribution, outsourcing and partnerships. For a practical application, SCM datasets need to be managed and maintained to serve a better service to its three main categories; distributor, customer and supplier. To manage these datasets, a structure of data constellation is used to accommodate the data into the spatial database. However, the situation in geospatial database creates few problems, for example the performance of the database deteriorate especially during the query operation. We strongly believe that a more practical hierarchical tree structure is required for efficient process of SCM. Besides that, three-dimensional approach is required for the management of SCM datasets since it involve with the multi-level location such as shop lots and residential apartments. 3D R-Tree has been increasingly used for 3D geospatial database management due to its simplicity and extendibility. However, it suffers from serious overlaps between nodes. In this paper, we proposed a partition-based clustering for the construction of a hierarchical tree structure. Several datasets are tested using the proposed method and the percentage of the overlapping nodes and volume coverage are computed and compared with the original 3D R-Tree and other practical approaches. The experiments demonstrated in this paper substantiated that the hierarchical structure of the proposed partition-based clustering is capable of preserving minimal overlap and coverage. The query performance was tested using 300,000 points of a SCM dataset and the results are presented in this paper. This paper also discusses the outlook of the structure for future reference.
Original languageEnglish
JournalI S P R S Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume2
Pages (from-to)9-17
ISSN2194-9042
DOIs
Publication statusPublished - 2015
EventJoint International Geoinformation Conference 2015 - Kuala Lumpur, Malaysia
Duration: 28 Oct 201530 Oct 2015
http://www.geoinfo.utm.my/jointgeoinfo2015/index.html

Conference

ConferenceJoint International Geoinformation Conference 2015
CountryMalaysia
CityKuala Lumpur
Period28/10/201530/10/2015
Internet address

Bibliographical note

The Annals are open access publications, they are published under the Creative Common Attribution 3.0 License

Keywords

  • Supply Chain Management
  • 3D Spatial Data Clustering
  • 3D Spatial Database
  • 3D GIS
  • Data Management
  • Information Retrieval

Cite this

Suhaibah, A. ; Uznir, U. ; Anton, François ; Mioc, Darka ; Rahman, A. A. / 3D Partition-Based Clustering for Supply Chain Data Management. In: I S P R S Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2015 ; Vol. 2. pp. 9-17.
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abstract = "Supply Chain Management (SCM) is the management of the products and goods flow from its origin point to point of consumption. During the process of SCM, information and dataset gathered for this application is massive and complex. This is due to its several processes such as procurement, product development and commercialization, physical distribution, outsourcing and partnerships. For a practical application, SCM datasets need to be managed and maintained to serve a better service to its three main categories; distributor, customer and supplier. To manage these datasets, a structure of data constellation is used to accommodate the data into the spatial database. However, the situation in geospatial database creates few problems, for example the performance of the database deteriorate especially during the query operation. We strongly believe that a more practical hierarchical tree structure is required for efficient process of SCM. Besides that, three-dimensional approach is required for the management of SCM datasets since it involve with the multi-level location such as shop lots and residential apartments. 3D R-Tree has been increasingly used for 3D geospatial database management due to its simplicity and extendibility. However, it suffers from serious overlaps between nodes. In this paper, we proposed a partition-based clustering for the construction of a hierarchical tree structure. Several datasets are tested using the proposed method and the percentage of the overlapping nodes and volume coverage are computed and compared with the original 3D R-Tree and other practical approaches. The experiments demonstrated in this paper substantiated that the hierarchical structure of the proposed partition-based clustering is capable of preserving minimal overlap and coverage. The query performance was tested using 300,000 points of a SCM dataset and the results are presented in this paper. This paper also discusses the outlook of the structure for future reference.",
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3D Partition-Based Clustering for Supply Chain Data Management. / Suhaibah, A.; Uznir, U.; Anton, François; Mioc, Darka; Rahman, A. A.

In: I S P R S Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 2, 2015, p. 9-17.

Research output: Contribution to journalConference articleResearchpeer-review

TY - GEN

T1 - 3D Partition-Based Clustering for Supply Chain Data Management

AU - Suhaibah, A.

AU - Uznir, U.

AU - Anton, François

AU - Mioc, Darka

AU - Rahman, A. A.

N1 - The Annals are open access publications, they are published under the Creative Common Attribution 3.0 License

PY - 2015

Y1 - 2015

N2 - Supply Chain Management (SCM) is the management of the products and goods flow from its origin point to point of consumption. During the process of SCM, information and dataset gathered for this application is massive and complex. This is due to its several processes such as procurement, product development and commercialization, physical distribution, outsourcing and partnerships. For a practical application, SCM datasets need to be managed and maintained to serve a better service to its three main categories; distributor, customer and supplier. To manage these datasets, a structure of data constellation is used to accommodate the data into the spatial database. However, the situation in geospatial database creates few problems, for example the performance of the database deteriorate especially during the query operation. We strongly believe that a more practical hierarchical tree structure is required for efficient process of SCM. Besides that, three-dimensional approach is required for the management of SCM datasets since it involve with the multi-level location such as shop lots and residential apartments. 3D R-Tree has been increasingly used for 3D geospatial database management due to its simplicity and extendibility. However, it suffers from serious overlaps between nodes. In this paper, we proposed a partition-based clustering for the construction of a hierarchical tree structure. Several datasets are tested using the proposed method and the percentage of the overlapping nodes and volume coverage are computed and compared with the original 3D R-Tree and other practical approaches. The experiments demonstrated in this paper substantiated that the hierarchical structure of the proposed partition-based clustering is capable of preserving minimal overlap and coverage. The query performance was tested using 300,000 points of a SCM dataset and the results are presented in this paper. This paper also discusses the outlook of the structure for future reference.

AB - Supply Chain Management (SCM) is the management of the products and goods flow from its origin point to point of consumption. During the process of SCM, information and dataset gathered for this application is massive and complex. This is due to its several processes such as procurement, product development and commercialization, physical distribution, outsourcing and partnerships. For a practical application, SCM datasets need to be managed and maintained to serve a better service to its three main categories; distributor, customer and supplier. To manage these datasets, a structure of data constellation is used to accommodate the data into the spatial database. However, the situation in geospatial database creates few problems, for example the performance of the database deteriorate especially during the query operation. We strongly believe that a more practical hierarchical tree structure is required for efficient process of SCM. Besides that, three-dimensional approach is required for the management of SCM datasets since it involve with the multi-level location such as shop lots and residential apartments. 3D R-Tree has been increasingly used for 3D geospatial database management due to its simplicity and extendibility. However, it suffers from serious overlaps between nodes. In this paper, we proposed a partition-based clustering for the construction of a hierarchical tree structure. Several datasets are tested using the proposed method and the percentage of the overlapping nodes and volume coverage are computed and compared with the original 3D R-Tree and other practical approaches. The experiments demonstrated in this paper substantiated that the hierarchical structure of the proposed partition-based clustering is capable of preserving minimal overlap and coverage. The query performance was tested using 300,000 points of a SCM dataset and the results are presented in this paper. This paper also discusses the outlook of the structure for future reference.

KW - Supply Chain Management

KW - 3D Spatial Data Clustering

KW - 3D Spatial Database

KW - 3D GIS

KW - Data Management

KW - Information Retrieval

U2 - 10.5194/isprsannals-II-2-W2-9-2015

DO - 10.5194/isprsannals-II-2-W2-9-2015

M3 - Conference article

VL - 2

SP - 9

EP - 17

JO - I S P R S Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - I S P R S Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 2194-9042

ER -