skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Analyses of Indexing Techniques on Uncertain Data With High Dimensionality

Access, IEEE, 2020, Vol.8, p.74101-74117

2013 IEEE ;DOI: 10.1109/ACCESS.2020.2988487

Full text available

Citations Cited by
  • Title:
    Analyses of Indexing Techniques on Uncertain Data With High Dimensionality
  • Author: Lawal, Ma'aruf Mohammed ; Ibrahim, Hamidah ; Sani, Nor Fazlida Mohd ; Yaakob, Razali
  • Subjects: Computer science ; Data partitioning ; Heuristic algorithms ; high-dimensional data ; Indexing ; indexing techniques ; Information technology ; MBR ; Spatial databases ; uncertain data
  • Is Part Of: Access, IEEE, 2020, Vol.8, p.74101-74117
  • Description: Deploying a solution for handling critical decision-based problem efficiently requires the processing of high-dimensional data. Over the years, due to modern technological advancement, unprecedented volume of uncertain data is been captured and this has necessitated the need to organize such data for better data access performance. To this effect, the use of indexing technique for supporting, organizing, and storing of uncertain data with high dimensionality has become pertinent. However, the choice of an indexing technique to improve search performance is highly influenced by the properties of the underlying data set, data construction methods employed by the indexing structure, and the query types it supports. This paper is motivated to conduct an extensive performance analysis among existing indexing techniques, namely: $R$ -tree, $R$ *-tree and $X$ -tree, in order to realize the most efficient indexing structure for organizing, storing and ultimately improving search performance over uncertain data with high dimensionality. The results of the analyses with regard to CPU processing time and number of nodes visited clearly show the superiority of $X$ -tree over $R$ -tree and $R$ *-tree, as its superiority holds for different data set sizes, data distributions, number of dimensions and even with varying selectivity ratio.
  • Publisher: IEEE
  • Language: English
  • Identifier: DOI: 10.1109/ACCESS.2020.2988487
  • Source: IEEE Xplore Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait