DocumentCode
1213842
Title
Deriving Protocol Models from Imperfect Service Conversation Logs
Author
Motahari-Nezhad, Hamid R. ; Saint-Paul, Régis ; Benatallah, Boualem ; Casati, Fabio
Author_Institution
Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW
Volume
20
Issue
12
fYear
2008
Firstpage
1683
Lastpage
1698
Abstract
Understanding the business (interaction) protocol supported by a service is very important for both clients and service providers: it allows developers to know how to write clients that interact with a service, and it allows development tools and runtime middleware to deliver functionality that simplifies the service development lifecycle. It also greatly facilitates the monitoring, visualization, and aggregation of interaction data. This paper presents an approach for discovering protocol definitions from real-world service interaction logs. It first describes the challenges in protocol discovery in such a context. Then, it presents a novel discovery algorithm, which is widely applicable, robust to different kinds of imperfections often present in realworld service logs, and able to derive protocols of small sizes, also thanks to heuristics. As finding the most precise and the smallest model is algorithmically not feasible from imperfect service logs, finally, the paper presents an approach to refine the discovered protocol via user interaction, to compensate for possible imprecision introduced in the discovered model. The approach has been implemented and experimental results show its viability on both synthetic and real-world datasets.
Keywords
Web services; data visualisation; middleware; protocols; Web service; business interaction protocol specification; imperfect service conversation log; interaction data aggregation; interaction data monitoring; interaction data visualization; protocol definition discovery; runtime middleware; service development tool; user interaction; Mining methods and algorithms; Systems and Software; Web-based services; Workflow management;
fLanguage
English
Journal_Title
Knowledge and Data Engineering, IEEE Transactions on
Publisher
ieee
ISSN
1041-4347
Type
jour
DOI
10.1109/TKDE.2008.87
Filename
4515865
Link To Document