Name
Abstract | ||
|---|---|---|
Every organization has business processes, however, there are numerous organizations in which execution logs of processes are not available. Consequently, these organizations do not have the opportunity to exploit the potential of execution logs for analyzing the performance of their processes. As a first step towards facilitating these organizations, in this paper, we argue that customer feedback is a valuable source of information that can provide important insights about process performance. However, a key challenge to this approach is that the feedback includes a significant amount of comments that are not related to process performance. Therefore, utilizing the complete feedback without omitting the irrelevant comments may generate misleading results. To that end, firstly, we have generated a customer feedback corpus of 3356 comments. Secondly, we have used two well-established BPM frameworks, Devil’s Quadrangle and Business Process Redesign Implementation framework, to manually classify the comments as relevant and irrelevant to process performance. Finally, we have used five supervised learning techniques to evaluate the effectiveness of the two frameworks for their ability to automatically identify performance relevant comments. The results show that Devil’s Quadrangle is more suitable framework than Business Process Redesign Implementation framework. |
Year | Venue | Field |
|---|---|---|
2018 | CAiSE Workshops | Data science,Business process,Systems engineering,Customer feedback,Computer science,Supervised learning,Exploit,Customer reviews,Business process reengineering |
DocType | Citations | PageRank |
Conference | 1 | 0.40 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanam Ahmad | 1 | 1 | 0.40 |
| Syed Irtaza Muzaffar | 2 | 1 | 0.40 |
| Khurram Shahzad | 3 | 165 | 25.77 |
| Kamran Malik | 4 | 1 | 1.75 |