Data and Information Quality (JDIQ)


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Journal of Data and Information Quality (JDIQ), Volume 5 Issue 1-2, August 2014

Felix Naumann
Article No.: 1
DOI: 10.1145/2648781

Section: Challenge Papers

Information quality research challenge: Predicting and quantifying the impact of social issues on information quality programs
John Talburt, Therese L. Williams, Thomas C. Redman, David Becker
Article No.: 2
DOI: 10.1145/2629603

Discovering product counterfeits in online shops: A big data integration challenge
Erhard Rahm
Article No.: 3
DOI: 10.1145/2629605

Challenges for privacy preservation in data integration
Peter Christen, Dinusha Vatsalan, Vassilios S. Verykios
Article No.: 4
DOI: 10.1145/2629604

Techniques for integrating data from diverse sources have attracted significant interest in recent years. Much of today’s data collected by businesses and governments are about people, and integrating such data across organizations can raise...

Section: Regular Papers

Reach for gold: An annealing standard to evaluate duplicate detection results
Tobias Vogel, Arvid Heise, Uwe Draisbach, Dustin Lange, Felix Naumann
Article No.: 5
DOI: 10.1145/2629687

Duplicates in a database are one of the prime causes of poor data quality and are at the same time among the most difficult data quality problems to alleviate. To detect and remove such duplicates, many commercial and academic products and methods...

Conflict resolution with data currency and consistency
Wenfei Fan, Floris Geerts, Nan Tang, Wenyuan Yu
Article No.: 6
DOI: 10.1145/2631923

This article introduces a new approach for conflict resolution: given a set of tuples pertaining to the same entity, it identifies a single tuple in which each attribute has the latest and consistent value in the set. This problem is important in...

Process-driven data quality management: A critical review on the application of process modeling languages
Paul Glowalla, Ali Sunyaev
Article No.: 7
DOI: 10.1145/2629568

Data quality is critical to organizational success. In order to improve and sustain data quality in the long term, process-driven data quality management (PDDQM) seeks to redesign processes that create or modify data. Consequently, process...