|
Title:
|
FAME.Q - A FORMAL APPROACH TO MASTER QUALITY IN ENTERPRISE LINKED DATA |
|
Author(s):
|
André Langer, Martin Gaedke |
|
ISBN:
|
978-989-8533-57-9 |
|
Editors:
|
Pedro Isaías |
|
Year:
|
2016 |
|
Edition:
|
Single |
|
Keywords:
|
Data Quality; Formal Model; Semantic Web; Linked Data; Enterprise Data Management |
|
Type:
|
Full Paper |
|
First Page:
|
51 |
|
Last Page:
|
58 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Whereas in the past, research and industry mainly focused on technological aspects related to the exposure of data and its processing from remote sources, the concern in Semantic Web Data Management started to shift over to other non-technical challenges when dealing with so called Linked Enterprise Data. Information Quality is such an aspect that plays an important role in the process of selecting the best available data source in the Web, consolidating it with already existing information and thereby improving the business value of the own data stock. Throughout the last decades, research has already comprehensively dealt with the question of what quality is and how it can be interpreted through different approaches. Surprisingly, it is all the more astounding that there are only vague and no concise and formal definitions of the quality concept so far, especially in the Semantic Web context. A formalization would help to make quality calculations more comparable and implementable. The paper addresses this challenge and raises the question on how to compute the quality of a particular data source by combining different aspects from existing approaches resulting in a more concise model. As a finding, data quality is expressed as the percentage to which degree a particular data source fulfills a set of specified requirements in a certain context. The formalized definition of data quality helps to discuss specific assessment aspects, and is exemplarily applied to a scenario from the CRM application domain. |
|
|
|
|
|
|
