7,558 research outputs found
Using Reference Models for Data Warehouse Metadata Management
Based on experience from applying the Common Warehouse Metamodel to metadata management at a large Swiss bank, the applicability of metadata reference models in complex application domains is analyzed. In order to explain the context of the case, the state-of-the-art of the Common Warehouse Metamodel and its application for data warehouse management are summarized. Based on the project experience documented in this paper, benefits of the reference model approach are described and recommendations for future developments of the Common Warehouse Metamodel are proposed
Knowledge and Metadata Integration for Warehousing Complex Data
With the ever-growing availability of so-called complex data, especially on
the Web, decision-support systems such as data warehouses must store and
process data that are not only numerical or symbolic. Warehousing and analyzing
such data requires the joint exploitation of metadata and domain-related
knowledge, which must thereby be integrated. In this paper, we survey the types
of knowledge and metadata that are needed for managing complex data, discuss
the issue of knowledge and metadata integration, and propose a CWM-compliant
integration solution that we incorporate into an XML complex data warehousing
framework we previously designed.Comment: 6th International Conference on Information Systems Technology and
its Applications (ISTA 07), Kharkiv : Ukraine (2007
Extracting, Transforming and Archiving Scientific Data
It is becoming common to archive research datasets that are not only large
but also numerous. In addition, their corresponding metadata and the software
required to analyse or display them need to be archived. Yet the manual
curation of research data can be difficult and expensive, particularly in very
large digital repositories, hence the importance of models and tools for
automating digital curation tasks. The automation of these tasks faces three
major challenges: (1) research data and data sources are highly heterogeneous,
(2) future research needs are difficult to anticipate, (3) data is hard to
index. To address these problems, we propose the Extract, Transform and Archive
(ETA) model for managing and mechanizing the curation of research data.
Specifically, we propose a scalable strategy for addressing the research-data
problem, ranging from the extraction of legacy data to its long-term storage.
We review some existing solutions and propose novel avenues of research.Comment: 8 pages, Fourth Workshop on Very Large Digital Libraries, 201
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
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