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MCIS 2010 : Third International Workshop on Managing Data Quality in Collaborative Information Systems

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Link: http://www.itee.uq.edu.au/~dke/mcis2010
 
When Apr 4, 2010 - Apr 4, 2010
Where Tsukuba, Japan
Submission Deadline Dec 11, 2009
Notification Due Feb 12, 2010
Final Version Due Apr 26, 2010
Categories    databases   HCI
 

Call For Papers

MCIS-2010 CALL FOR PAPPERS Third International Workshop on Managing Data Quality in Collaborative Information Systems
April 4, 2010 Tsukuba, Japan

http://www.itee.uq.edu.au/~dke/mcis2010

In conjunction with the 15th International Conference on Database Systems for Advanced Applications (DASFAA2010)

== Organizers

Shazia Sadiq, Xiaofang Zhou, Ke Deng
University of Queensland, Australia

Xiaochun Yang
Northeastern University, China

== Program Committee

Adam Jatowt, Kyoto Uni., JP
Lei Chen, Hong Kong Uni. of Sci. and Tech., HK
Jiuyong Li, University of South, AU
Qing Liu, CSIRO, AU
Marek Kowalkiewicz, SAP, AU
Marta Indulska, Uni. of Queensland, AU
Mohamed Medhat Gaber, Monash Uni. AU
Wanita Sherchan, CSIRO, AU.
Yanfeng Shu, CSIRO AU.
Bin Wang, Northeastern Uni, China
Cheqing Jin, East China Normal Uni, China
Jun Gao, Peking Uni, China

== Important Dates

Dec. 11, 2009 Paper submission deadline
Feb. 12, 2010 Acceptance notification to authors
Feb. 26, 2010 On-site paper deadline
Apr. 26, 2010 Final camera-ready copy

== Introduction ==

Workshop
Poor data quality is known to compromise the credibility and efficiency of commercial as well as public endeavours. Several developments from industry as well as academia have contributed significantly towards addressing the problem. These typically include analysts and practitioners who have contributed to the design of strategies and methodologies for data governance; solution architects including software vendors who have contributed towards appropriate system architectures that promote data integration and; and data experts who have contributed to data quality problems such as duplicate detection, identification of outliers, consistency checking and many more through the use of computational techniques. The attainment of true data quality lies at the convergence of the three aspects, namely organizational, architectural and computational.
At the same time, importance of managing data quality has increased manifold in today's global information sharing environments, as the diversity of sources, formats and volume of data grows. In this workshop we target data quality in the light of collaborative information systems where data creation and ownership is increasingly difficult to establish. Collaborative settings are evident in enterprise systems, where partner/customer data may pollute enterprise data bases raising the need for data source attribution, as well as in scientific applications, where data lineage across long running collaborative scientific processes needs to be established. Collaborative settings thus warrant a pipeline of data quality methods and techniques that commence with (source) data assessment, data cleansing, methods for sustained quality, integration and linkage, and eventually ability for audit and attribution.
The workshop will provide a forum to bring together diverse researchers and make a consolidated contribution to new and extended methods to address the challenges of data quality in collaborative settings. Topics covered by the workshop include at least the following:

* Data integration, linkage and fusion
* Entity resolution, duplicate detection, and consistency checking
* Data profiling and measurement
* Use of data mining for data quality assessment
* Methods for data transformation, reconciliation, consolidation
* Algorithms for data cleansing
* Data quality and cleansing in information extraction
* Dealing with uncertain or noisy data (e.g., sensor data)
* Data lineage and provenance
* Models, frameworks, methodologies and metrics for data quality
* Application specific data quality, case studies, experience reports
* User/social perceptive on data quality and cleansing
* Data quality and cleansing for complex data (e.g. documents, semi-structured data, XMLs, multimedia data, graphs, biosequences, etc.)

== Publication

Authors should submit papers reporting original works that are currently not under review or published elsewhere. The workshop proceedings are to be published as part of Springer's Lecture Notes in Computer Science series. After the workshop, contact will be made with an International Journal to publish a selection of best papers.

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