User Interface, User Experience, Artificial Intelligence
The main goal of the presented paper is to propose a method for adaptation of the user interface of workflow software to increase its efficiency, reduce the number of errors, and improve its UX. The authors assumed that the system adaptation will be achieved by application to intelligent methods for modeling user as well as system. In order to do this, a special tool for data gathering has been designed and in the next steps of the research, this tool will be also implemented in a real environment. A unique value of the paper is that after many years of theoretical research, the first attempt to implement a practical solution for self-adapting and the personalized interface for workflow systems was done. Janusz Sobecki et al. / Procedia Computer Science 176 (2020) 3506–3513 3507 adaptation [3]. This AI approach for user interface adaptation may be also enhanced with the application of user interface ontologies [4]. 1.1. Related Works In user interface design we should start with proper user model [1] however, we should always remember that ever-increasing number of users, especially of web-based systems, also brings the increase of differences among their users and interaction styles [3]. The user differences may reflex their demographical, psychological as well as sociological user characteristics, which have an influence on the users' information needs and interaction habits. The consequence of these differences is causing difficulties in modeling these users in the standard way [1], so since many years more sophisticated solutions have been proposed, for example, one enhanced with the application of ontologies [5], which was further applied in SOA systems development [4] and [6]. The information systems design and development have been enhanced by ontologies on at least several different levels, such as database integration, business logic, or Graphical User Interfaces (GUI) [6]. The before mentioned work presents an approach for mapping formal ontologies to GUI. This supports device-independent GUI construction and semi-automatic GUI modeling. This issue was also been raised in other work [7], as well as [8]. User interfaces have been also adopted by means of application different recommender methods such as Demographic Filtering, Content-Based Filtering, Collaborative Filtering or Hybrid Approach [9, 15], wherein user grouping or classification different machine learning algorithms may be applied, or the recommender method hybridization may be based