[서평]「The Unified Modeling Language User Guide」

In the course of the evolution of management support towards corporate wide Business Intelligence infrastructures, the integration of components for handling unstructured data comes into focus. In this paper, three types of approaches for tackling the respective challenges are distinguished. The approaches are mapped to a three layer BI framework and discussed regarding challenges and business potential. The application of the framework is exemplified for the domains of Competitive Intelligence and Customer Relationship Management.

Management Support with Structured and Unstructured Data-An Integrated Business Intelligence Framework

Multidimensional modeling requires specialized design techniques. Though a lot has been written about how a data warehouse should be designed, there is no consensus on a design method yet. This paper follows from a wide discussion that took place in Dagstuhl, during the Perspectives Workshop "Data Warehousing at the Crossroads", and is aimed at outlining some open issues in modeling and design of data warehouses. More precisely, issues regarding conceptual models, logical models, methods for design, interoperability, and design for new architectures and applications are considered.

http://www-db.deis.unibo.it/~srizzi/PDF/dolap06-design.pdf

Research in data warehouse modeling and design: dead or alive?

Extraction-transformation-loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, its cleansing, customization, reformatting, integration, and insertion into a data warehouse. Building the ETL process is potentially one of the biggest tasks of building a warehouse; it is complex, time consuming, and consumes most of data warehouse project's implementation efforts, costs, and resources. Building a data warehouse requires focusing closely on understanding three main areas: the source area, the destination area, and the mapping area (ETL processes). The source area has standard models such as entity relationship diagram, and the destination area has standard models such as star schema, but the mapping area has not a standard model till now. In spite of the importance of ETL processes, little research has been done in this area due to its complexity. There is a clear lack of a standard model that can be used to represent the ETL scenarios. In this paper we will try to navigate through the efforts done to conceptualize the ETL processes. Research in the field of modeling ETL processes can be categorized into three main approaches: Modeling based on mapping expressions and guidelines, modeling based on conceptual constructs, and modeling based on UML environment. These projects try to represent the main mapping activities at the conceptual level. Due to the variation and differences between the proposed solutions for the conceptual design of ETL processes and due to their limitations, this paper also will propose a model for conceptual design of ETL processes. The proposed model is built upon the enhancement of the models in the previous models to support some missing mapping features.

Original Article: A proposed model for data warehouse ETL processes

Guidelines for the effective use of entity-attribute-value modeling for biomedical databases.

Metadata has been identified as a key success factor in data warehouse projects. It captures all kinds of information necessary to analyse, design, build, use, and interpret the data warehouse contents. In order to spread the use of metadata, enable the interoperability between repositories, and tool integration within data warehousing architectures, a standard for metadata representation and exchange is needed. This paper considers two standards and compares them according to specific areas of interest within data warehousing. Despite their incontestable similarities, there are significant differences between the two standards which would make their unification difficult.

Metadata standards for data warehousing: open information model vs. common warehouse metadata

Metadata has been identified as a key success factor in data warehouse projects. It captures all kinds of information necessary to design, build, use and interpret the data warehouse contents. This paper gives an overview about the role metadata plays for data warehousing and reviews existing standards, commercial solutions and research actions relevant to metadata management. It turns out that an overall solution for managing all metadata in a central or federated repository is still missing regarding a global metadata schema as well as system aspects and interoperability among involved tools producing metadata. The divergence of proposed standards will probably prevent a breakthrough within the near future.

Metadata management for data warehousing: an overview

This report gives an overview of metadata management in general (Part I) and on the role of metadata for data warehousing (Part II) Because of the complexity and extensive applicability of metadata, a compact, precise definition of the notion may hardly be provided Therefore, we explain metadata by illustrating the use and the forms it may take within various application areas In the case of data warehousing, we present a classification of metadata along certain dimensions and we discuss significant aspects of metadata management that have to be considered for the construction of a data warehouse system Furthermore, this report provides a comprehensive survey and analysis of the state of the art of metadata management in industry and research The most important standards for representation and interchange of metadata, commercial products and research projects are presented and discussed (as far as the available information allows) for both, the general case and the particular case of data warehousing

Metadata Management and Data Warehousing

At Swiss Life, as in many other companies, office workers for customer support are no longer specialists dealing with certain kinds of office tasks only, but are becoming generalists who must deal with all kinds of tasks. There are many laws and company regulations which take influence on how a certain task is to be performed properly. As a consequence, the work of this new generation of office workers is quite demanding and urgently calls for a better support. For this purpose, the Information Systems Research Group of Swiss Life has developed a knowledge-based system, called EULE2, that aims at providing a user with a maximal guidance in performing office tasks he may not be familiar with. A research prototype is completed and we are now in the phase of developing the first official version which is intended to become deployed.In the following, Section 2 characterizes EULE2 as a user sees the system. Subsequently, the architecture of EULE2 is outlined (Section 3). EULE2 and issues of knowledge management it is related with are addressed in Section 4. Section 5 concludes the paper and gives an outlook on future work.

EULE2: a knowledge-based system for supporting office work

Metadata has been identified as a key success factor in data warehouse projects. It captures all kinds of information necessary to extract, transform and load data from source systems into the data warehouse, and afterwards to use and interpret the data warehouse contents. This paper gives an overview about the role metadata plays for data warehousing and reviews existing standards, commercial solutions and research actions relevant to metadata management. It turns out that an overall solution for managing all metadata in a central or federated repository is still missing regarding a global metadata schema as well as system aspects and interoperability among involved tools producing metadata. The divergence of proposed standards will probably prevent a breakthrough within the near future.

/pdf/the-role-of-metadata-for-data-warehousing-1ojn9rprvp.pdf

Thomas Vetterli

Papers

Metadata standards for data warehousing: open information model vs. common warehouse metadata

Metadata management for data warehousing: an overview

Metadata Management and Data Warehousing

EULE2: a knowledge-based system for supporting office work

The Role of Metadata for Data Warehousing