Paper Info
Title
Cataloger: Catalog Recommendation Service for IT Change Requests.
Abstract
Service automation improves the efficiency of IT service management processes. Traditionally, IT change management relies on humans to submit a change request ticket or navigate a cumbersome catalog. Today, new systems are created to execute changes based on a service catalog that is linked to back-end application programming interfaces (APIs). Consequently, a user would need to identify the right API among thousands or more items, and fill in all the required parameters. This interaction is fully self-served with little assistance. We present Cataloger a novel recommendation system that enables humans to specify their change requests in natural language sentences and recommends the most appropriate APIs. Cataloger incorporates multi-step process where IT change requests are first classified into categories, tasks and actions (APIs), and then parameters are extracted from the requests. We evaluate a well-known set of machine learning techniques for classification and parameters extraction for Cataloger, and propose a novel feedback method for improved accuracy. We evaluate Cataloger on real-world data from four different clients of IBM. Our evaluation shows that the feedback approach significantly improves the accuracy of identifying categories, tasks, and actions for change requests, thereby, improving the API recommendation to users.
Year
Venue
Field
2017
ICSOC
Recommender system,Data mining,IBM,World Wide Web,Computer science,Change request,Ticket,Service catalog,Change management (ITSM),Application programming interface,IT service management
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
15
5
Name
Order
Citations
PageRank
Anup K. Kalia162.21
Jin Xiao214019.55
Muhammed Fatih Bulut3606.81
Maja Vukovic437841.62
Nikos Anerousis524034.23