A Pattern-Based Framework for Addressing Data Representational Inconsistency
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- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：9877
- 期：1
- 页码：395-406
- 全文大小：610 KB
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15.Yi, B., Hua, W., Sadiq, S.: Technical report: Pattern-based framework for addressing data representational inconsistency (2016). https://drive.google.com/folderview?id=0B7vhn9TkNVEVYjN4WWhIclpLdTA&usp=sharing - 作者单位：Bingyu Yi (16)
Wen Hua (16)
Shazia Sadiq (16)
16. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia - 丛书名：Databases Theory and Applications
- ISBN：978-3-319-46922-5
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
- 卷排序：9877
文摘
Data representational inconsistency, where data has diverse formats or structures, is a crucial data quality problem. Existing fixing approaches either target on a specific domain or require massive information from users. In this work, we propose a user-friendly pattern-based framework for addressing data representational inconsistency. Our framework consists of three modules: pattern design, pattern detection, and pattern unification. We identify several challenges in all the three tasks in order to handle an inconsistent dataset both accurately and efficiently. We propose various techniques to tackle these issues, and our experimental results on real-life datasets demonstrate better performance of our proposals compared with existing methods.