Tool for Identifying Working Style Based on Event Logs
Name
Mikheil Kenchoshvili
Abstract
Working style is a relatively new topic in Business Informatics. It is strictly related to concepts existing in other areas of the research such as Organizational Mining, Visual analytics, Pattern recognition. While there are several tools to tackle the task of organizational mining, they are general purpose stand-alone desktop applications, and none of them concentrate solely and deeply on working style identification. To this end, this thesis introduces a domain-specific web-based tool that aids the user with working style
identification. The tool allows end users to import event logs. They can explore and query patterns to answer the questions related to the working style of the human actors involved in that event logs. The thesis presents, functional and technical requirements as well as the implementation details of the tool. Also, discusses the difficulties faced during the development and the gaps that have been identified in the current state of practice of the technologies that were used for implementation. Moreover, several possible extensions of the tool are suggested that could be addressed in the future works. Finally, the thesis
demonstrates the usage of the tool by importing the sample data and exploring different capabilities and aspects of the application.
identification. The tool allows end users to import event logs. They can explore and query patterns to answer the questions related to the working style of the human actors involved in that event logs. The thesis presents, functional and technical requirements as well as the implementation details of the tool. Also, discusses the difficulties faced during the development and the gaps that have been identified in the current state of practice of the technologies that were used for implementation. Moreover, several possible extensions of the tool are suggested that could be addressed in the future works. Finally, the thesis
demonstrates the usage of the tool by importing the sample data and exploring different capabilities and aspects of the application.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Marcelo Sarini, Marlon Dumas
Defence year
2018