Data Quality Management with Semantic Technologies

✍ Scribed by Christian Fürber (auth.)

Publisher: Gabler Verlag
Year: 2016
Tongue: English
Leaves: 230
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Christian Fürber investigates the useful application of semantic technologies for the area of data quality management. Based on a literature analysis of typical data quality problems and typical activities of data quality management processes, he develops the Semantic Data Quality Management framework as the major contribution of this thesis. The SDQM framework consists of three components that are evaluated in two different use cases. Moreover, this thesis compares the framework to conventional data quality software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work.

✦ Table of Contents

Front Matter....Pages I-XXVII
Introduction....Pages 1-7
Research Design....Pages 8-19
Data Quality....Pages 20-55
Semantic Technologies....Pages 56-68
Data Quality in the Semantic Web....Pages 69-77
Specification of Initial Requirements....Pages 78-86
Architecture of the Semantic Data Quality Management Framework (SDQM)....Pages 87-109
Application Procedure of SDQM....Pages 110-121
Evaluation of the Semantic Data Quality Management Framework (SDQM)....Pages 122-152
Related Work....Pages 153-170
Synopsis and Future Work....Pages 171-176
Back Matter....Pages 177-205

✦ Subjects

Business Information Systems; Knowledge Management

📜 SIMILAR VOLUMES

Data Science with Semantic Technologies:

📁 Data Science with Semantic Technologies: Deployment and Exploration

✍ Archana Patel, Narayan C. Debnath 📂 Library 📅 2023 🏛 CRC Press 🌐 English

Gone are the days when data was interlinked with related data by humans and human interpretation was required. Data is no longer just data. It is now considered a Thing or Entity or Concept with meaning, so that a machine not only understands the concept but also extrapolates the way humans do. D

Data Science with Semantic Technologies:

📁 Data Science with Semantic Technologies: Theory, Practice and Application

✍ Archana Patel, Narayan C. Debnath, Bharat Bhushan 📂 Library 📅 2022 🏛 Wiley-Scrivener 🌐 English

DATA SCIENCE WITH SEMANTIC TECHNOLOGIESThis book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this fi

Data Science with Semantic Technologies:

📁 Data Science with Semantic Technologies: New Trends and Future Developments

✍ Archana Patel, Narayan C. Debnath 📂 Library 📅 2023 🏛 CRC Press 🌐 English

As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic techno

The Semantic Web for Knowledge and Data

📁 The Semantic Web for Knowledge and Data Management: Technologies and Practices

✍ Zongmin Ma, Huaiqing Wang, Zongmin Ma, Huaiqing Wang 📂 Library 📅 2008 🏛 IGI Global 🌐 English

While the current Web provides access to an enormous amount of information, it is currently only human-readable. In response to this problem, the Semantic Web allows for explicit representation of the Semantics of data so that it is machine interpretable. Semantic Web for Knowledge and Data Mana

Managing Data Quality

📁 Managing Data Quality

✍ Tim King;Julian Schwarzenbach; 📂 Library 📅 2020 🏛 Book Network Int'l Limited trading as NBN Internat 🌐 English

Data quality -- Part 150: Master data: Q

📁 Data quality -- Part 150: Master data: Quality management framework

📂 Scientific