Data Driven vs. Metric Driven Data Warehouse Design

2011 ◽  
pp. 223-227 ◽  
Author(s):  
John M. Artz
Keyword(s):  
Data Warehouse ◽  
Computer Technology ◽  
Data Warehousing ◽  
Data Driven ◽  
Paradigmatic Shift ◽  
The Real ◽  
Warehouse Design ◽  
Future Demands

Although data warehousing theory and technology have been around for well over a decade, they may well be the next hot technologies. How can it be that a technology sleeps for so long and then begins to move rapidly to the foreground? This question can have several answers. Perhaps the technology had not yet caught up to the theory or that computer technology 10 years ago did not have the capacity to delivery what the theory promised. Perhaps the ideas and the products were just ahead of their time. All these answers are true to some extent. But the real answer, I believe, is that data warehousing is in the process of undergoing a radical theoretical and paradigmatic shift, and that shift will reposition data warehousing to meet future demands.

Data Driven vs. Metric Driven Data Warehouse Design

2011 ◽  
pp. 382-387 ◽  
Author(s):  
John M. Artz
Keyword(s):  
Data Warehouse ◽  
Computer Technology ◽  
Data Warehousing ◽  
Data Driven ◽  
Paradigmatic Shift ◽  
The Real ◽  
Warehouse Design ◽  
Future Demands

Although data warehousing theory and technology have been around for well over a decade, they may well be the next hot technologies. How can it be that a technology sleeps for so long and then begins to move rapidly to the foreground? This question can have several answers. Perhaps the technology had not yet caught up to the theory or that computer technology 10 years ago did not have the capacity to delivery what the theory promised. Perhaps the ideas and the products were just ahead of their time. All these answers are true to some extent. But the real answer, I believe, is that data warehousing is in the process of undergoing a radical theoretical and paradigmatic shift, and that shift will reposition data warehousing to meet future demands.

Multidimensional Design Methods for Data Warehousing

2011 ◽  
pp. 78-105 ◽  
Author(s):  
Oscar Romero ◽  
Alberto Abelló
Keyword(s):  
Data Warehouse ◽  
Design Process ◽  
Data Warehousing ◽  
Design Methods ◽  
Data Sources ◽  
Data Driven ◽  
Main Research ◽  
Multidimensional Modeling ◽  
Warehouse Design ◽  
Warehousing Systems

In the last years, data warehousing systems have gained relevance to support decision making within organizations. The core component of these systems is the data warehouse and nowadays it is widely assumed that the data warehouse design must follow the multidimensional paradigm. Thus, many methods have been presented to support the multidimensional design of the data warehouse.The first methods introduced were requirement-driven but the semantics of the data warehouse (since the data warehouse is the result of homogenizing and integrating relevant data of the organization in a single, detailed view of the organization business) require to also consider the data sources during the design process. Considering the data sources gave rise to several data-driven methods that automate the data warehouse design process, mainly, from relational data sources. Currently, research on multidimensional modeling is still a hot topic and we have two main research lines. On the one hand, new hybrid automatic methods have been introduced proposing to combine data-driven and requirement-driven approaches. These methods focus on automating the whole process and improving the feedback retrieved by each approach to produce better results. On the other hand, some new approaches focus on considering alternative scenarios than relational sources. These methods also consider (semi)-structured data sources, such as ontologies or XML, that have gained relevance in the last years. Thus, they introduce innovative solutions for overcoming the heterogeneity of the data sources. All in all, we discuss the current scenario of multidimensional modeling by carrying out a survey of multidimensional design methods. We present the most relevant methods introduced in the literature and a detailed comparison showing the main features of each approach.

Core Methodologies in Data Warehouse Design and Development

2013 ◽  
Vol 1 (1) ◽  
pp. 57-66
Author(s):  
James Yao ◽  
John Wang ◽  
Qiyang Chen ◽  
Ruben Xing
Keyword(s):  
Data Warehouse ◽  
System Development ◽  
Hybrid Approach ◽  
Heterogeneous Data ◽  
Data Driven ◽  
Decision Making Process ◽  
Process Data ◽  
Design And Development ◽  
Heterogeneous Data Sources ◽  
Warehouse Design

Data warehouse is a system which can integrate heterogeneous data sources to support the decision making process. Data warehouse design is a lengthy, time-consuming, and costly process. There has been a high failure in data warehouse development projects. Thus how to design and develop a data warehouse have become important issues for information systems designers and developers. This paper reviews and discusses some of the core data warehouse design and development methodologies in information system development. The paper presents in particular the most recent and much heated hybrid approach which is a combination of data-driven and requirement-driven approaches.

scholarly journals X-ETL: A Data-Driven Approach for Designing Star Schemas

2019 ◽  
Vol 7 (1) ◽  
pp. 4
Author(s):  
Nawfal El Moukhi ◽  
Ikram El Azami ◽  
Abdelaaziz Mouloudi ◽  
Abdelali Elmounadi
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Data Driven ◽  
Critical Stage ◽  
Model Driven ◽  
New Methods ◽  
Star Models ◽  
Data Driven Approach ◽  
Data Source ◽  
Warehouse Design

The data warehouse design is currently recognized as the most important and complicated phase in any project of decision support system implementation. Its complexity is primarily due to the proliferation of data source types and the lack of a standardized and well-structured method, hence the increasing interest from researchers who have tried to develop new methods for the automation and standardization of this critical stage of the project. In this paper, the authors present the set of developed methods that follows the data-driven paradigm, and they propose a new data-driven method called X-ETL. This method aims to automating the data warehouse design by generating star models from relational data. This method is mainly based on a set of rules derived from the related works, the Model-Driven Architecture (MDA) and the XML language.

Universal Data Warehousing Based on a Meta-Data Modeling Approach

2003 ◽  
Vol 12 (03) ◽  
pp. 325-363 ◽  
Author(s):  
Joseph Fong ◽  
Qing Li ◽  
Shi-Ming Huang
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Object Oriented ◽  
Data Models ◽  
Heterogeneous Databases ◽  
Materialized Views ◽  
Prototype System ◽  
Relational View ◽  
Metadata Model ◽  
Star Schema

Data warehouse contains vast amount of data to support complex queries of various Decision Support Systems (DSSs). It needs to store materialized views of data, which must be available consistently and instantaneously. Using a frame metadata model, this paper presents an architecture of a universal data warehousing with different data models. The frame metadata model represents the metadata of a data warehouse, which structures an application domain into classes, and integrates schemas of heterogeneous databases by capturing their semantics. A star schema is derived from user requirements based on the integrated schema, catalogued in the metadata, which stores the schema of relational database (RDB) and object-oriented database (OODB). Data materialization between RDB and OODB is achieved by unloading source database into sequential file and reloading into target database, through which an object relational view can be defined so as to allow the users to obtain the same warehouse view in different data models simultaneously. We describe our procedures of building the relational view of star schema by multidimensional SQL query, and the object oriented view of the data warehouse by Online Analytical Processing (OLAP) through method call, derived from the integrated schema. To validate our work, an application prototype system has been developed in a product sales data warehousing domain based on this approach.

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