MCIS 2009 : Managing Data Quality in Collaborative Information Systems
Call For Papers
===== Call for Submissions =======
Workshop in Conjunction with DASFAA 2009
14th International Conference on Database Systems for Advanced Applications
21-23 April 2009 in Brisbane, Australia
=== Important Dates
15 Jan, 2009 Submission of paper
28 Feb, 2009 Notification of acceptance
15 Mar, 2009 Camera ready
20 April, 2009 Workshop
Poor data quality is known to compromise the credibility and efficiency of commercial
as well as public endeavours. Several developments from industry and 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 profiling, 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, bio-sequences etc.)
Submitted papers will be evaluated on the basis of significance, originality, technical quality,
and exposition. Papers should clearly establish the research contribution, and relation to previous
research. Position and survey papers are also welcome.
All papers accepted by MCIS 2009 will be published in a combined volume of Lecturer Notes in
Computer Science series published by Springer (Approved). MCIS 2009 will benefit from the registration
process of DASFAA 2009 (we will have a single registration for conferences, workshops and tutorials).