Book ChapterDOI
Quality in Data Warehousing
Mokrane Bouzeghoub,Zoubida Kedad +1 more
- pp 163-198
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TLDR
This chapter provides a general framework for data warehouse design based on quality in order to aggregate it and customize it with respect to business and organizational criteria required by decision makers.Abstract:
Data warehousing is a new technology which provides a software infrastructure for decision support systems and OLAP applications. Data warehouse systems collect data from heterogeneous and distributed sources, transform and reconcile this data in order to aggregate it and customize it with respect to business and organizational criteria required by decision makers. High level aggregated data is organized by subjects and stored as a multidimensional structure into a data mart. Data quality is very important in database applications in general and very crucial in data warehousing in particular. Indeed, data warehouse systems provide aggregated data to decision makers whose actions and decisions should be very strategic to the enterprise. Providing dirty data, imprecise data or non coherent data may lead to the rejection of the decision support system or may result into non productive decisions. This chapter provides a general framework for data warehouse design based on quality.read more
Citations
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Journal ArticleDOI
A proposal for a set of attributes relevant for Web portal data quality
TL;DR: A set of 33 attributes which are relevant for portal data quality are proposed which have been obtained from a revision of literature and a validation process carried out by means of a survey and it is thought that it might be considered as a good starting point for constructing one.
Proceedings ArticleDOI
Experimental validation of multidimensional data models metrics
TL;DR: Two metrics are defined for multidimensional data models and an experiment developed in order to validate them as quality indicators and it seems that the number of fact tables can be considered as a solid quality indicator of a multiddimensional data model.
Data freshness and data accuracy :a state of the art
TL;DR: A taxonomy of existing works proposed for dealing with both quality dimensions in several kinds of DIS, their underlying metrics and the features of DIS that impact their evaluation are presented.
LNCS 4255 - Defining a Data Quality Model for Web Portals
TL;DR: In this article, a model for the data quality in Web portals (PDQM) is proposed, which is built upon the foundation of three key aspects: a set of Web data quality attributes identified in the literature in this area, data quality expectations of data consumers on the Internet, and the functionalities that a Web portal may offer to its users.
e-Health monitoring applications: What about Data Quality?
TL;DR: The necessity of the analysis of data quality on e-Health applications, especially concerning remote monitoring and assistance of patients with chronic diseases, is underline.
References
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The Goal Question Metric Approach
TL;DR: Measurement is a mechanism for creating a corporate memory and an aid in answering a variety of questions associated with the enactment of any software process.
Proceedings Article
Querying Heterogeneous Information Sources Using Source Descriptions
TL;DR: The Information Manifold is described, an implemented system that provides uniform access to a heterogeneous collection of more than 100 information sources, many of them on the WWW, and algorithms that use the source descriptions to prune effciently the set of information sources for a given query are described.
Journal ArticleDOI
A product perspective on total data quality management
TL;DR: The purpose of this TDQM methodology is to deliver highquality information products (IP) to information consumers and aims to facilitate the implementation of an organization’s overall data quality policy formally expressed by top management.
Book
Maintenance of materialized views: problems, techniques, and applications
TL;DR: This chapter contains sections titled: Introduction, The Idea Behind View Maintenance, Using Full Information, Using Partial Information, Open Problems, Acknowledgments.
Journal ArticleDOI
A framework for analysis of data quality research
TL;DR: Using an analogy between product manufacturing and data manufacturing, this paper develops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature.