## 5 Case Study
### Case Perspective Diagnosis  
The stage of case perspective diagnosis contains two aspects (the statistical analysis of case attributes and case content diagnosis): In terms of the statistical analysis of case attributes, the case attributes of complaint grades, complaint causes, and complaint problems are involved. In terms of case content diagnosis, the technique of word frequency statistics based on Chinese word segmentation [37] is employed to explore the case attributes of complaint content record and complaint handling result record. As shown in Figures 3-5, in terms of complaint grades, the number of general complaint cases is much more than major complaint cases; in terms of complaint causes, \"not in time\" and \"other cause\" are the most frequent causes of complaints; and, in terms of complaint problems, \"service complaint\" is the most frequent complaint problem. For raw process data in 2016, the record of complaint content describes the detailed complaint contents of customers and the record of complaint handling result describes the complaint handling service process in detail. Therefore, natural texts that may contain useful information for CHS process improvement and the ICTCLAS (Institute of Computing Technology, Chinese Lexical Analysis System) of Chinese word segmentation technique [38] are employed to explore the natural texts. Then the Chinese word statistics is based on part of speech (such as, verb, adjective, numeral). The results of all statistics are sent to two CHS experts. Based on the business understanding of business experts, the key statistical results are fed back (Figures 6-8). Ultimately, for the statistical results of each part of speech, the top 20 words are retained. Figure 3: Number of cases with different complaint grades. Figure 4: Number of cases with different complaint causes. Figure 5: Number of cases with different complaint problems. Figure 6: Statistical results of noun frequency in the natural texts of complaint contents. Figure 5 presents the statistical results of noun frequency in the natural texts of complaint contents. In Figure 5, the words of \"sir\" and \"madam\" almost share the same frequency. We can conclude that the frequency of male and female customers is similar in the company's CHS process. Besides, the words of \"product 1\" and \"product 2\" share the maximum frequency compared with the company's other products in the company's CHS process. Figure 6 presents the statistical results of institutional suffix frequency in the natural texts of complaint contents. In Figure 6, we can find that \"franchised shop\", \"market\", \"flagship store\", and \"shopping mall\" are the main shopping channels to buy the company's products for customers. In the meantime, the main complaint channels outside the company for customers are \"administration of industry and commerce\", and \"customers' association\". Figure 7 presents the statistical results of job noun frequency in the natural texts of complaint handling results. Although there is no resource attribute in each CHS case in the original data of our study, we can find most of the categories of business staff of the company involved in CHS process from Figure 7, such as technician, manager, master worker, head of department, commissioner, group leader, and so on. Therefore, we need to filter the noise data before implementing PM in the next phase. In this study, our strategy is that we will filter the complete CHS case once an undesirable timestamp is found in any case. In the meantime, based on the suggestions of business experts, the case duration is limited to 30 days. Then an algorithm is designed to filter the cases with low-quality timestamps. The steps of the algorithm are as follows. (1) Saving the timestamp of each event into an array. (2) Screening out the events with exceptional timestamp and marking the events. (3) Travelling through all events and marking the entire case if there is one exception event.