Towards Quality-Oriented Data Warehouse Usage and Evolution

Abstract

Abstract. As a decision support information system, a data warehouse must provide high level quality of data and quality of service. In the DWQ project we have proposed an architectural framework and a repository of metadata which describes all the data warehouse components in a set of metamodels to which is added a quality metamodel, defining for each data warehouse metaobject the corresponding relevant quality dimensions and quality factors. Apart from this static definition of quality, we also provide an operational complement, that is a methodology on how to use quality factors and to achieve user quality goals. This methodology is an extension of the Goal-Question-Metric (GQM) approach, which allows to capture (a) the inter-relationships between different quality factors and (b) to organize them in order to fulfil specific quality goals. After summarizing the DWQ quality model, this paper describes the methodology we propose to use this quality model, as well as its impact on the data warehouse evolution.

Eliciting and prioritizing quality requirements supported by ontologies: a case study using the ElicitO framework and tool

“…Related work in this area includes the research conducted by Rao and Osei‐Bryson () proposing a comprehensive taxonomy of quality dimensions to define, organize and measure quality in knowledge management systems; the research conducted by Vassiliadis et al . () proposing a methodology for mapping high‐level user‐defined quality goals for a data warehouse into specific quality factors and dimensions stored in the data warehouse metadata, facilitating the development of warehouse maintenance and evolution processes taking into account quality concerns. The paper by Jarke et al .…”

“…Another important research dimension of information, knowledge management and decision support systems quality stems from developing quality criteria, metrics and mechanisms to assess, maintain and improve the quality contained in the data, information and knowledge-based system architectures. Related work in this area includes the research conducted by Rao and Osei-Bryson (2007) proposing a comprehensive taxonomy of quality dimensions to define, organize and measure quality in knowledge management systems; the research conducted by Vassiliadis et al (2000) proposing a methodology for mapping high-level user-defined quality goals for a data warehouse into specific quality factors and dimensions stored in the data warehouse metadata, facilitating the development of warehouse maintenance and evolution processes taking into account quality concerns. The paper by Jarke et al (1999) also discusses how to extend the data warehouse architecture model to support quality models, also providing mathematical techniques for measuring and optimizing data warehouse quality factors.…”

The Impact of Using Data Warehouse on Manpower Employment Decision Support System

“…DW architecture exhibits various layers of data in which data from one layer are derived from data of the lower layer." [17]. Here, we will illustrate the layers that constitute a data warehouse and as depicted in figure-2..…”

A Model for Data Warehouse Operational Processes

“…The linkage of the architecture model to quality parameters (quality model) and its implementation in ConceptBase have been formally described in [13]. [27] presents a methodology for the actual exploitation of the information found in the metadata repository and the quality-oriented evolution of a data warehouse based on the architecture and quality model. In this paper, we complement these approaches with the metamodeling for the dynamic part of the data warehouse environment: the processes.…”