Today, businesses have to respond with flexibility and speed to ever-changing customer demand and market opportunities. Service-Oriented Architecture (SOA) is the best methodology for developing new services and integrating them with adaptability the ability to respond to changing and new requirements. In this paper, we propose a framework for ensuring data quality between composite services, which solves semantic data transformation problems during service composition and detects data errors during service execution at the same time. We also minimize the human intervention by learning data constraints as a basis of data transformation and error detection. We developed a data quality assurance service based on SOA, which makes it possible to improve the quality of services and to manage data effectively for a variety of SOA-based applications. As an empirical study, we applied the service to detect data errors between CRM and ERP services and showed that the data error rate could be reduced by more than 30%. We also showed automation rate for setting detection rule is over 41% by learning data constraints from multiple registered services in the field of business.
|Number of pages||31|
|Journal||International Journal of Software Engineering and Knowledge Engineering|
|State||Published - May 2009|
Bibliographical noteFunding Information:
This work is supported by Ajou University research fellowship of 20062910. This research is supported by the ubiquitous computing and networking project (UCN) Project, Knowledge and Economy Frontier R&D Program of the Ministry of Knowledge Economy (MKE) in Korea and a result of subproject UCN 09C1-T3-10M.
- Data constraints
- Data quality
- Decision tree
- Learning algorithms
- Quality of Service (QoS)
- Service-Oriented Architecture (SOA)