Name
Abstract | ||
|---|---|---|
Ticketing system is an example of a Service System (SS) which is responsible for handling huge volumes of tickets generated by large enterprise IT (Information Technology) infrastructure components, and ensuring smooth operation. An issue is captured as summary on the ticket and once a ticket is resolved, the solution is also noted down on the ticket as resolution. Further the system maintains the provision of recording the time when a ticket is opened, acknowledged to user, resolved and/or closed, from which different QoS parameters could be obtained. For example, Resolution Time can be computed as the difference of resolution date and opening date of the ticket. QoS parameters are used to measure the performance of different aspects of a service. In case of impreciseness of observations of these parameters fuzzy sets seems to be an optimal tool to model them. To ensure better operation for services based on these QoS values we propose a two-stage analysis framework for QoS prediction of incoming tickets which includes fuzzy clustering of incident tickets based on QoS values and building a fuzzy regression model using this categorization and the textual contents of tickets. Further we carry out a fuzzy correlation analysis of different categories (clusters) of QoS parameters. Lastly we report on our experimental results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICWS.2017.55 | 2017 IEEE International Conference on Web Services (ICWS) |
Keywords | Field | DocType |
Quality of servies (QoS),service system,tickets,fuzzy set,Response Time,Resolution Time,fuzzy clustering,Fuzzy C-means algorithm (FCM),non-negative least squares (NNLS),fuzzy correlation | Data mining,Fuzzy clustering,Information technology,Computer science,Service system,Fuzzy logic,Quality of service,Ticket,Fuzzy set,Maintenance engineering | Conference |
ISBN | Citations | PageRank |
978-1-5386-0753-4 | 0 | 0.34 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suman Roy | 1 | 21 | 7.25 |
| Durga Prasad Muni | 2 | 2 | 0.72 |
| Adrija Bhattacharya | 3 | 1 | 3.39 |
| Dipanjan Dutta | 4 | 0 | 0.34 |
| Navin Budhiraja | 5 | 60 | 8.18 |