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 anddataset gathered for this application is massive...
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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 anddataset 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 spatialdatabase. However, the situation in geospatialdatabase 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 geospatialdatabase management due to its simplicity and extendibility. However, it suffers from serious overlaps between nodes. In this paper, we proposed a partition-basedclustering 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-basedclustering 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.
In the next few years, 3ddata is expected to be an intrinsic part of geospatialdata. However, issues on 3dspatialdata management are still in the research stage. One of the issues is performance deterioration duri...
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ISBN:
(纸本)9783319121819;9783319121802
In the next few years, 3ddata is expected to be an intrinsic part of geospatialdata. However, issues on 3dspatialdata management are still in the research stage. One of the issues is performance deterioration during 3ddata retrieval. Thus, a practical 3d index structure is required for efficient data constellation. due to its reputation and simplicity, R-Tree has been received increasing attention for 3d geospatialdatabase management. However, the transition of its structure from 2d to 3d had caused a serious overlapping among nodes. Overlapping nodes also occur during splitting operation of the overflown node N of M + 1 entry. Splitting operation is the most critical process of 3d R-Tree. The produced tree should satisfy the condition of minimal overlap and minimal volume coverage in addition with preserving a minimal tree height. Based on these concerns, in this paper, we proposed a crisp clustering algorithm for the construction of a 3d R-Tree. Several datasets are tested using the proposed method and the percentage of the overlapping parallelepipeds and volume coverage are computed and compared with the original R-Tree and other practical approaches. The experiments demonstrated in this research substantiated that the proposed crisp clustering is capable to preserve minimal overlap, coverage and tree height, which is advantageous for 3d geospatialdata implementations. Another advantage of this approachis that the properties of this crisp clustering algorithm are analogous to the original R-Tree splitting procedure, which makes the implementation of this approach straightforward.
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