Architecture and quality in data warehouses: An extended repository approach

Abstract

Architecture and quality in data warehouses -An extended repository approach Jarke, M.; Jeusfeld, M.A.; Quix, C.; Vassiliadis, P. Published in: Information SystemsPublication date: 1999 Link to publicationCitation for published version (APA): Jarke, M., Jeusfeld, M. A., Quix, C., & Vassiliadis, P. (1999). Architecture and quality in data warehouses -An extended repository approach. Information Systems, 24(3), 229-253. General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. (1) Informatik V, RWTH Aachen, 52056 Aachen, Germany (2) Infolab, KUB University, Postbus 90153, 5000 LE Tilburg, The Netherlands (3) Computer Science Division, NTUA Athens, Zographou 15773 Athens, Greece $EVWUDFW -Most database researchers have studied data warehouses (DW) in their role as buffers of materialized views, mediating between update-intensive OLTP systems and query-intensive decision support. This neglects the organizational role of data warehousing as a means of centralized information flow control. As a consequence, a large number of quality aspects relevant for data warehousing cannot be expressed with the current DW meta models. This paper makes two contributions towards solving these problems. Firstly, we enrich the meta data about DW architectures by explicit enterprise models. Secondly, many very different mathematical techniques for measuring or optimizing certain aspects of DW quality are being developed. We adapt the Goal-Question-Metric approach from software quality management to a meta data management environment in order to link these special techniques to a generic conceptual framework of DW quality. The approach has been implemented in full on top of the ConceptBase repository system and has undergone some validation by applying it to the support of specific quality-oriented methods, tools, and application projects in data warehousing.

Data fusion in data federation using modified discriminative Markov logic networks

“…The quality of data was improved through query processing. The quality improvement was communicated to interested data sources (Jarke et al, 1999). The explicit enterprise model was proposed to enrich the data warehouse metadata to improve the quality of the data warehouse.…”

Data warehouse process management

“…Quality Usage Methodology In the three phases of the data warehouse lifecycle, the interested stakeholders need information on various aspects of the examined processes: what are they supposed to do, how are they implemented, why are they necessary and how they affect other processes in the data warehouse [68,29]. Like the data warehouse architecture and quality metamodels, the process metamodel assumes the clustering of their entities in logical, physical and conceptual perspectives, each assigned with the task of answering one of the aforementioned stakeholder questions.…”

“…In [29] a basic metamodel for data warehouse architecture and quality has been presented as in Fig. 2.…”

“…The metadata repository together with this quality goal definition methodology constitutes a decision support system which helps data warehouse designers and administrators to take relevant decisions, in order to achieve a reasonable quality level which fits the best user requirements. [29] Throughout our models, we encourage the use of templates for process, quality and architecture objects. This is especially apparent in the metadata layer where an abstract specification of architecture, process or quality objects, originally coming from the designers of the data warehouse, can be properly specialized by the data warehouse administrator at runtime.…”

“…In [29], we presented a metadata modeling approach which enables the capturing of the static parts of the architecture of a data warehouse. The linkage of the architecture model to quality parameters (in the form of a quality model) and its implementation in the metadata repository ConceptBase have been formally described in [32].…”