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.

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.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

Basic Notions

2003 ◽  
pp. 1-45
Author(s):  
Maurizio Rafanelli
Keyword(s):  
Graph Model ◽  
Aggregate Data ◽  
Multidimensional Data ◽  
Formal Definition ◽  
Aggregation Process ◽  
Data Cubes ◽  
Multidimensional Databases ◽  
On Line ◽  
Analytic Processing ◽  
Definition Of

This chapter presents the basic notions regarding multidimensional (aggregate) databases by referring to different definitions given for them in the literature. It illustrates the important concepts of micro, macro, and metadata; presents a formal definition of the aggregation process, discussing the concepts of dimension and dimension hierarchies; describes the multidimensional aggregate data structure, distinguishing between simple, complex, and composite structure; illustrates the different types of null values; and discusses differences and similarities which exist between multidimensional aggregate data (generally called statistical data because they are used mainly by statisticians) and the On-Line-Analytic Processing (OLAP) of multidimensional data represented by different data cubes, also discussing the different (symmetric and non-symmetric) treatment of dimensions and measures required by OLAP and aggregate multidimensional databases. Finally it discusses a graph model and a tabular model for this kind of data, and gives a set of definitions regarding the OLAP terminology.

Schema Evolution Models and Languages for Multidimensional Data Warehouses

2009 ◽  
pp. 119-128
Author(s):  
Edgard Benítez-Guerrero ◽  
Ericka-Janet Rechy-Ramírez
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Historical Data ◽  
Multidimensional Systems ◽  
Multidimensional Data ◽  
Schema Evolution ◽  
Time Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Factual Data

A Data Warehouse (DW) is a collection of historical data, built by gathering and integrating data from several sources, which supports decisionmaking processes (Inmon, 1992). On-Line Analytical Processing (OLAP) applications provide users with a multidimensional view of the DW and the tools to manipulate it (Codd, 1993). In this view, a DW is seen as a set of dimensions and cubes (Torlone, 2003). A dimension represents a business perspective under which data analysis is performed and organized in a hierarchy of levels that correspond to different ways to group its elements (e.g., the Time dimension is organized as a hierarchy involving days at the lower level and months and years at higher levels). A cube represents factual data on which the analysis is focused and associates measures (e.g., in a store chain, a measure is the quantity of products sold) with coordinates defined over a set of dimension levels (e.g., product, store, and day of sale). Interrogation is then aimed at aggregating measures at various levels. DWs are often implemented using multidimensional or relational DBMSs. Multidimensional systems directly support the multidimensional data model, while a relational implementation typically employs star schemas(or variations thereof), where a fact table containing the measures references a set of dimension tables.

scholarly journals Data Warehouse Schemas using Multidimensional Data Model for Retail

2017 ◽  
Vol 2 (1) ◽  
pp. 84
Author(s):  
Kheri Arionadi Shobirin ◽  
Adi Panca Saputra Iskandar ◽  
Ida Bagus Alit Swamardika
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Data Warehouse ◽  
Data Model ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Complex Query ◽  
On Line ◽  
Analytical Processing ◽  
Operational Data

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

Explanation in OLAP Data Cubes

2014 ◽  
Vol 7 (4) ◽  
pp. 63-78 ◽  
Author(s):  
Rahhal Errattahi ◽  
Mohammed Fakir ◽  
Fatima Zahra Salmam
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Structures ◽  
Relevant Information ◽  
Multidimensional Data ◽  
New Approach ◽  
Data Cubes ◽  
Multidimensional Data Structures ◽  
Analytical Processing ◽  
Interactive Data

OLAP is an important technology that offers a fast and interactive data navigation, it also provides tools to explore data cubes in order to extract interesting information from a multidimensional data structures. However, the OLAP exploration is done manually, without tools that could automatically extract relevant information from the cube. In addition OLAP is not capable of explaining relationships that could exist within data. This paper presents a new approach to coupling between data mining and online analytical processing. Its approach provides the explanation in OLAP data cubes by using the association rules between the inter-dimensional predicates. The mining process could be done by one of the two algorithms, Apriori and Fp-Growth, in which aggregate measures to calculate support and confidence are exploited. It also evaluates the interestingness of mined association rules according to the Lift criteria.

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.

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