Gaining Insight from Operational Data for Service Optimization
Service optimization is key in the drive to further automate and increase the effi-ciency of the services provided to clients. A given client may be ahead, in one or more areas, versus where other clients are. Learning from these best-of-breed cli-ents benefits the other clients in optimizing their services. Each of the clients normally generates a large number of data points for all the elements comprising the services they consume. Important data points are generally contained within incident tickets and change tickets. These tickets contain not only information about the nature of the issue, but also contain resolution information.
This work was done through collaboration of IBM Research and Technical services, and authors are grateful to Sinem Guven, Karin Murthy, Jin Xiao, Anup Kalia and Sander Plug for their contribution.
- 2.Gartner IT summit, best practices for continuous application availability, 2005.Google Scholar
- 3.Druebert J (2010) Changes, incidents & unintended consequences. In: Insight on IT service managementGoogle Scholar
- 4.Tang L, Li T, Shwartz L, Grabarnik G (2013) Recommending resolutions for problems identified by monitoring. In: IFIP/IEEE IM, GhentGoogle Scholar
- 5.Bogojeska J, Lanyi D, Giurgiu I, Stark G, Wiesmann D (2013) Classifying server behavior and predicting impact of modernization actions. In: IFIP/IEEE CNSM, ZurichGoogle Scholar
- 6.Agarwal S, Sindhgatta R, Sengupta B (2012) SmartDispatch: enabling efficient ticket dispatch in an IT service environment. In: ACM KDD, BeijingGoogle Scholar
- 7.Güven S, Barbu C, Husemann D, Wiesmann D (2012) Change risk expert. In: IFIP/IEEE NOMS, Maui, HIGoogle Scholar
- 8.Hagen S, Seibold M, Kemper A (2012) Efficient verification of IT change. In: IFIP/IEEE NOMS, Maui, HIGoogle Scholar
- 9.Lipitakis A-D, Kotsiantis S (2014) A hybrid Machine Learning methodology for imbalanced datasets. In: IEEE IISA, CreteGoogle Scholar
- 10.Bulut F (2016) Performance evaluations of supervised learners on imbalanced datasets. In: Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT)Google Scholar
- 11.Güven S, Murthy K (2016) Understanding the role of change in incident prevention. In: IEEE/IFIP CNSM, MontrealGoogle Scholar
- 13.Güven S, Jasionowski P, Murthy K, Tunga K, Stark G (2017) COACH: COgnitive Analytics for CHange. IEEE/IFIP IMS, PortugalGoogle Scholar