KDB/sub KD/-tree: a compact KDB-tree structure for indexing multidimensional data

2004 ◽  
Author(s):  
Byunggu Yu ◽  
T. Bailey ◽  
R. Orlandic ◽  
J. Somavaram
Keyword(s):  
Tree Structure ◽  
Multidimensional Data

An efficient peer-to-peer indexing tree structure for multidimensional data

2009 ◽  
Vol 25 (1) ◽  
pp. 77-88 ◽  
Author(s):  
Rong Zhang ◽  
Weining Qian ◽  
Aoying Zhou ◽  
Minqi Zhou
Keyword(s):  
Peer To Peer ◽  
Tree Structure ◽  
Multidimensional Data

Man-Computer Derivations of Tree Structure from Multivariate Data

1968 ◽  
Author(s):  
Gerald H. Shure ◽  
Laurence I. Press ◽  
Miles S. Rogers
Keyword(s):  
Multivariate Data ◽  
Tree Structure

Building a tree structure

1976 ◽  
Author(s):  
Patricia Marks Greenfield ◽  
Leslie Schneider
Keyword(s):  
Tree Structure

Recommendations for deploying multidimensio

2017 ◽  
Vol 11 (1) ◽  
pp. 68-80
Author(s):  
E. E. Akimkina
Keyword(s):  
Comparative Analysis ◽  
Management System ◽  
Analytical Data ◽  
Enterprise Management ◽  
Multidimensional Data ◽  
Dimensional Approach ◽  
Process Data ◽  
Modeling Systems ◽  
Enterprise Management System ◽  
Practical Recommendations

A comparative analysis of different approaches to analytical data and shows that the most ample opportunities has a multi-dimensional approach, implemented with the help of OLAP technology. Presented multidimensional OLAP-cube model with the measurements for the analysis and processing of process data. Practical recommendations for the deployment of a multidimensional data modeling systems with regard to their integration into existing enterprise management system.

Structuring and visualization of indicators in multidimensional data cubes

2018 ◽  
pp. 79-87
Author(s):  
E. E. Akimkina
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Management ◽  
Multidimensional Data ◽  
End User ◽  
Processing Technologies ◽  
Data Cubes ◽  
Multidimensional Visualization ◽  
Multidimensional Data Cubes ◽  
Practical Recommendations

The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.

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