OLAP Analysis Operators for Multi-State Data Warehouses

2016 ◽  
Vol 12 (4) ◽  
pp. 20-53 ◽  
Author(s):  
Franck Ravat ◽  
Jiefu Song ◽  
Olivier Teste
Keyword(s):  
Data Reduction ◽  
Decision Makers ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
State Data ◽  
Olap Analysis ◽  
Reduced Data

Data reduction in Multidimensional Data Warehouses (MDWs) allows increasing the efficiency of analysis and facilitating decision-makers' tasks. In this paper, the authors model a MDW containing reduced data through a set of states. Each state is valid for a certain period of time; it contains only useful information according to decision-makers' needs. In order to carry out analyses in a MDW composed of multiple states, an extension of traditional OLAP analysis operators is required. In this paper, the authors define a set of OLAP operators compatible with reduced MDWs. For each operator, they propose a user-oriented definition along with an algorithmic translation. To show the feasibility and the efficiency of the proposed concepts, they implement the analysis operators in an R-OLAP framework.

Mining Association Rules in Data Warehouses

2008 ◽  
pp. 303-335
Author(s):  
Haorianto Cokrowijoyo Tjioe ◽  
David Taniar
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strategic Decision ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Multidimensional Databases ◽  
Efficient Data ◽  
Decision Making Processes ◽  
Mining Association Rules ◽  
Transactional Data

Data mining applications have enormously altered the strategic decision-making processes of organizations. The application of association rules algorithms is one of the well-known data mining techniques that have been developed to cope with multidimensional databases. However, most of these algorithms focus on multidimensional data models for transactional data. As data warehouses can be presented using a multidimensional model, in this paper we provide another perspective to mine association rules in data warehouses by focusing on a measurement of summarized data. We propose four algorithms — VAvg, HAvg, WMAvg, and ModusFilter — to provide efficient data initialization for mining association rules in data warehouses by concentrating on the measurement of aggregate data. Then we apply those algorithms both on a non-repeatable predicate, which is known as mining normal association rules, using GenNLI, and a repeatable predicate using ComDims and GenHLI, which is known as mining hybrid association rules.

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

2013 ◽  
Vol 9 (2) ◽  
pp. 89-109 ◽  
Author(s):  
Marie-Aude Aufaure ◽  
Alfredo Cuzzocrea ◽  
Cécile Favre ◽  
Patrick Marcel ◽  
Rokia Missaoui
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Query Processing ◽  
Data Warehouse ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Cooperative Query Answering ◽  
User Centric ◽  
Use Of Knowledge

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

Employing Multidimensional Data Visualization Tools to Assess the Impact of Constraint Uncertainties on Complex Design Problems

2017 ◽  
Author(s):  
Gary M. Stump ◽  
Simon W. Miller ◽  
Michael A. Yukish ◽  
Christopher M. Farrell
Keyword(s):  
Data Visualization ◽  
Decision Makers ◽  
Multidimensional Data ◽  
Design Problems ◽  
Design Constraints ◽  
Performance Space ◽  
Multidimensional Data Visualization ◽  
Complex Design ◽  
Visualization Tools ◽  
The Impact

A potential source of uncertainty within multi-objective design problems can be the exact value of the underlying design constraints. This uncertainty will affect the resulting performance of the selected system commensurate with the level of risk that decision-makers are willing to accept. This research focuses on developing visualization tools that allow decision-makers to specify uncertainty distributions on design constraints and to visualize their effects in the performance space using multidimensional data visualization methods to solve problems with high orders of computational complexity. These visual tools will be demonstrated using an example portfolio design scenario in which the goal of the design problem is to maximize the performance of a portfolio with an uncertain budget constraint.

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

2016 ◽  
pp. 1859-1880
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet ◽  
Yvan Bédard
Keyword(s):  
Spatial Data ◽  
End Users ◽  
Environmental Data ◽  
Decision Makers ◽  
Multidimensional Analysis ◽  
Multidimensional Data ◽  
New Approach ◽  
New Models ◽  
Different Types ◽  
Spatial Vagueness

Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

Design features of decision support systems with adaptation elements

2020 ◽  
pp. 58-68
Author(s):  
Э.Э. Акимкина
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
System Performance ◽  
Support Systems ◽  
Multidimensional Data ◽  
Internal Environment ◽  
Data Warehouses ◽  
Design Features

Рассмотрены вопросы повышения эффективности систем поддержки принятия решений на основе многомерных хранилищ данных, имеющие существенное значение для выполнения требований по увеличению быстродействия систем. Показано, что эффективность функционирования системы поддержки принятия решений возрастает при введении в нее элементов обслуживания, которые позволяют учитывать изменение условий внешней и внутренней среды. Разработана методика проектирования системы поддержки принятия решений, учитывающая особенности ее адаптации к изменяющимся условиям с помощью элементов обслуживания. Issues of increasing the effectiveness of decision support systems based on multidimensional data warehouses are considered, which are essential for fulfilling the requirements to increase system performance. It is shown that the effectiveness of the functioning of the decision support system increases with the introduction of service elements in it, which allow taking into account changes in the conditions of the external and internal environment. A methodology has been developed for designing a decision support system that takes into account the peculiarities of its adaptation to changing conditions using service elements.

scholarly journals A Unified Conceptual Model for Data Warehouses

2021 ◽  
Vol 5 (5) ◽  
pp. 162-169
Author(s):  
Shreya Banerjee ◽  
Sourabh Bhaskar ◽  
Anirban Sarkar ◽  
Narayan C. Debnath
Keyword(s):  
Conceptual Model ◽  
Design Methodology ◽  
Data Modeling ◽  
Major Choice ◽  
Decision Makers ◽  
Systematic Design ◽  
Data Warehouses ◽  
Nosql Databases

These days, NoSQL (Not only SQL) databases are being used as a deployment tool for Data Warehouses (DW) due to its support for dynamic and scalable data modeling capabilities. Yet, decision-makers have faced several challenges to accept it as a major choice for implementation of their DW. The most significant one among those challenges is a lack of common conceptual model and a systematic design methodology for different NoSQL databases. The objective of this paper is to resolve these challenges by proposing an ontology based formal conceptual model for NoSQL based DWs. These proposed concepts are capable of realizing the cube concepts for visualization of multi-dimensional data in NoSQL based DW solutions. In this context, two strategies are specified, implemented and illustrated using a case study for devising of the proposed conceptual model.

scholarly journals A Prototyped NL-Based Approach for the Design of Multidimensional Data Warehouse

2020 ◽  
Vol 9 (5) ◽  
Author(s):  
Abeer Alzahrani ◽  
Mohamed Alqarni ◽  
Jamel Feki
Keyword(s):  
Natural Language ◽  
Data Warehouse ◽  
Decision Makers ◽  
Design Approach ◽  
Multidimensional Data ◽  
Design Quality ◽  
Decision Making Processes ◽  
Scientific Arguments ◽  
Research Domain

Organizations are more and more interested in the Data Warehouse (DW) technology and data analytics to base their decision-making processes on scientific arguments instead of intuition. Despite the efforts invested, the DW design issue remains a great challenging research domain. The design quality of the DW depends on several aspects, as the requirement gathering. In this context, we propose a Natural Language (NL) based design approach, which is twofold, first, it facilitates the involvement of the decision-makers in the DW design process; indeed, NL can encourage the decision-makers to express their requirements as English-like sentences conform to NL-templates. Secondly, our approach aims to generate semi-automatically a DW schema from a set of requirements gathered as analytical queries compliant to the NL-templates. This design approach relies on (i) two easy-to-use NL-templates to specifying the analysis components, and (ii) a set of five heuristic rules for extracting the multidimensional concepts from the requirements. We demonstrate the feasibility of our approach by developing the prototype Natural Language Decisional Requirements to DW Schema (NLDR2DWS).

scholarly journals Innovative Approaches for Efficiently Warehousing Complex Data from the Web

Data Mining ◽  
2013 ◽  
pp. 1422-1448
Author(s):  
Fadila Bentayeb ◽  
Nora Maïz ◽  
Hadj Mahboubi ◽  
Cécile Favre ◽  
Sabine Loudcher ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Warehouse ◽  
Design Management ◽  
Complex Data ◽  
Data Warehouses ◽  
Process Data ◽  
Access Methods ◽  
Olap Analysis ◽  
The Web

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of Information Systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling, and user driven-OLAP analysis.

Dynamic Multidimensional Data Cubes for Interactive Analysis of Massive Datasets

2005 ◽  
pp. 924-929 ◽  
Author(s):  
Mirek Riedewald ◽  
Divyakant Agrawal
Keyword(s):  
Complex Analysis ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Massive Datasets ◽  
Interactive Analysis ◽  
Business Units ◽  
External Data ◽  
Main Challenge ◽  
Multidimensional Data Cubes ◽  
Networking Technology

Rapidly improving computing and networking technology enables enterprises to collect data from virtually all its business units. The main challenge today is to extract useful information from an overwhelmingly large amount of raw data. To support complex analysis queries, data warehouses were introduced. They manage data, which is extracted from the different operational databases and from external data sources, and they are optimized for fast query processing. For modern data warehouses, it is common to manage Terabytes of data. According to a recent survey by the Winter Corporation (2003), for instance, the decision support database of SBC reached a size of almost 25 Terabytes, up from 10.5 Terabytes in 2001 (Winter Corporation, 2001).

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