Approaching the Limits of Data Quality Management
ARC Discovery Grant 2007 - 2009
Regardless of the intelligence and sophistication dedicated to functionality of new software solutions, data quality remains a major factor in the successful deployment of IT systems. Importance of managing data quality has increased manifold in today's global information sharing environments. This project will delve into the fundamental questions behind data consistency, integrity and constraints satisfaction by providing deep insights into the computational boundaries of data quality management. Consequently, innovative solutions are expected to be developed offering full scope of automatic data cleansing and unification, targeting generic data quality management requirements that span business as well as scientific data control.
Chief Investigators (ITEE/UQ)
Prof Maria
Orlowska
Dr. Shazia Sadiq
Associated Staff
Associated Students
Naiem Khodabandehloo (PhD)
Related Publications
Shazia Sadiq, Xiaofang Zhou, Maria Orlowska (2007)
Data Quality – The Key Success Factor for Data Driven
Engineering. In Frontiers of Data Driven Engineering (FDDE2007) at IFIP
International Conference on Network and Parallel Computing. Sep 18-21, 2007.
Dalian, China.
Abstract: As the scale and diversity of data grows in the digital arena, the
complexities of data driven engineering grow multifold with it. The last several
years have brought forth several new technologies to service this need -
semantic web, grid systems, web service composition to mention a few. However, a
fundamental underpinning of the success of these technologies resides in the
quality of data that they can provide. Often the failure of a technology is
attributed to its functionality when the real problem lies in the quality of
data it uses and subsequently produces. In this paper, we highlight a need to
embrace data quality considerations in all aspects of data driven engineering.
Shazia Sadiq, Maria Orlowska, Wasim Sadiq (2007)
Induction of Data Quality Protocols into Business Process Management. 9th
International Conference on Enterprise Information Systems. 12-16, June 2007. Funchal,
Madeira - Portugal.
Abstract: Data quality plays a fundamental role in the
success of IT solutions deployment. Success of large projects may be compromised
due to lack of governance and control of data quality. The criticality of this
problem has increased manifold in the current business environment heavily
dependent on external data, where such data may pollute enterprise databases. At
the same time, it is well recognized that an organization’s business processes
provide the backbone for business operations through constituent enterprise
applications and services. As such business process management systems are often
the first point of contact for dirty data. It is on the basis of this role that
we propose that BPM technologies can and should be viewed as a vehicle for data
quality enforcement. In this paper, we target a specific data quality problem,
namely data mismatch. We propose to address this problem by explicitly inducting
requisite data quality protocols in to the business process management system.
In addition to presenting the details of the proposed approach, we will also
present in this paper, a detailed analysis of process data properties and
typical errors.