Semantics-Aware Advanced OLAP Visualization of Multidimensional Data Cubes

2008 ◽  
pp. 974-1003 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Domenico Sacca ◽  
Paolo Serafino
Keyword(s):  
Data Warehouse ◽  
Large Family ◽  
Research Topic ◽  
Biological Data ◽  
Multidimensional Data ◽  
Data Cubes ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Multidimensional Data Cubes ◽  
Flattening Process

Efficiently supporting advanced OLAP visualization of multidimensional data cubes is a novel and challenging research topic, which results to be of interest for a large family of data warehouse applications relying on the management of spatio-temporal (e.g., mobile) data, scientific and statistical data, sensor network data, biological data, etc. On the other hand, the issue of visualizing multidimensional data domains has been quite neglected from the research community, since it does not belong to the well-founded conceptual-logical-physical design hierarchy inherited from relational database methodologies. Inspired from these considerations, in this article we propose an innovative advanced OLAP visualization technique that meaningfully combines (i) the so-called OLAP dimension flattening process, which allows us to extract two-dimensional OLAP views from multidimensional data cubes, and (ii) very efficient data compression techniques for such views, which allow us to generate “semantics-aware” compressed representations where data are grouped along OLAP hierarchies.

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.

scholarly journals Navigation Rules for Exploring Large Multidimensional Data Cubes

2008 ◽  
pp. 1334-1354
Author(s):  
Navin Kumar ◽  
Aryya Gangopadhyay ◽  
George Karabatis ◽  
Sanjay Bapna ◽  
Zhiyuan Chen
Keyword(s):  
Knowledge Discovery ◽  
Multidimensional Data ◽  
Discovery Process ◽  
High Recall ◽  
Data Cubes ◽  
Driven System ◽  
On Line ◽  
Analytical Processing ◽  
Multidimensional Data Cubes ◽  
Drill Down

Navigating through multidimensional data cubes is a nontrivial task. Although On-Line Analytical Processing (OLAP) provides the capability to view multidimensional data through rollup, drill-down, and slicing-dicing, it offers minimal guidance to end users in the actual knowledge discovery process. In this article, we address this knowledge discovery problem by identifying novel and useful patterns concealed in multidimensional data that are used for effective exploration of data cubes. We present an algorithm for the DIscovery of Sk-NAvigation Rules (DISNAR), which discovers the hidden interesting patterns in the form of Sk-navigation rules using a test of skewness on the pairs of the current and its candidate drill-down lattice nodes. The rules then are used to enhance navigational capabilities, as illustrated by our rule-driven system. Extensive experimental analysis shows that the DISNAR algorithm discovers the interesting patterns with a high recall and precision with small execution time and low space overhead.

Dynamic Multidimensional Data Cubes

2003 ◽  
pp. 200-221 ◽  
Author(s):  
Mirek Riedewald ◽  
Divyakant Agrawal ◽  
Amr El Abbadi
Keyword(s):  
Large Data ◽  
Data Cube ◽  
Multidimensional Data ◽  
Future Trends ◽  
Data Cubes ◽  
Current Trends ◽  
Speed Up ◽  
Analytical Processing ◽  
Data Collections ◽  
Multidimensional Data Cubes

Data cubes are ubiquitous tools in data warehousing, online analytical processing, and decision support applications. Based on a selection of pre-computed and materialized aggregate values, they can dramatically speed up aggregation and summarization over large data collections. Traditionally, the emphasis has been on lowering query costs with little regard to maintenance, i.e., update cost issues. We argue that current trends require data cubes to be not only query-efficient, but also dynamic at the same time, and we also show how this can be achieved. Several array-based techniques with different tradeoffs between query and update cost are discussed in detail. We also survey selected approaches for sparse data and the popular data cube operator, CUBE. Moreover, this work includes an overview of future trends and their impact on data cubes.

An MDA Approach for the Evolution of Data Warehouses

2015 ◽  
Vol 7 (3) ◽  
pp. 65-89 ◽  
Author(s):  
Said Taktak ◽  
Saleh Alshomrani ◽  
Jamel Feki ◽  
Gilles Zurfluh
Keyword(s):  
Data Warehouse ◽  
Research Topic ◽  
Multidimensional Data ◽  
Model Driven ◽  
Transformation Rules ◽  
Challenging Research ◽  
Operational Information ◽  
Evolving Context ◽  
View Transformation ◽  
Evolution Approach

Modeling and data warehousing have been considered, for more than one decade, as a new challenging research topic for which different approaches have been proposed. Nevertheless these proposals have focused on static aspects only. In practice, the evolution of the operational information system can lead to changes in its dependent multidimensional data warehouse (i.e. that this system feeds with data), and therefore may require the evolution of the data warehouse model. In this evolving context, the authors propose a model-driven based approach in order to automate the propagation of the evolutions occurred in the source database towards the multidimensional data warehouse. This approach is based on two evolution models, along with a set of transformation rules formalized in Query/View/Transformation. This paper describes this evolution approach for which we are developing a software prototype called DWE© (Data Warehouse Evolution).

scholarly journals Automated active phased array control stand software

2019 ◽  
pp. 93-102
Author(s):  
A. M. Aminev ◽  
A. V. Gilev ◽  
D. Yu. Grishin ◽  
V. E. Zaytsev ◽  
V. N. Sergeev
Keyword(s):  
Phased Array ◽  
Phased Arrays ◽  
Measurement Process ◽  
Multidimensional Data ◽  
Software Platform ◽  
End User ◽  
Data Cubes ◽  
Large Aperture ◽  
Radar Systems ◽  
Multidimensional Data Cubes

The study suggests using a software platform for multidimensional data cubes in automated active phased array control stands. The application of the platform greatly facilitates and accelerates the display and analysis of very large volumes of data coming from large-aperture active phased arrays during the measurement process, so that the end user can make spontaneous data requests. The study shows the prospects of using this platform for radar systems as a whole.

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