Context-sensitive Assistance Systems for Smart Self-Management
As information technology increasingly permeates everyday life, it provides great potential and at the same time leads to new challenges. In the current working world two challenges can be observed in particular, a work intensification and blurring boundaries between personal and work life. Many employees are often pressed for time. Frequent interruptions and multitasking constitute additional difficulties for the planning and completion of tasks, both in business and personal life. The resulting stress combined with a lack of opportunities for recreation are hazards to cause ill health. Therefore, self-management is becoming increasingly important, not only with regard to productivity, but especially in terms of maintaining individual motivation, wellbeing, and health. Developing an individually fitting and frictionless workflow may entail additional effort, but can be a great benefit, especially in the long-term. Existing tools, such as digital calendars or to-do lists, offer only rudimentary support as most of them are static and require continuous manual adjustment. Moreover, developments in the area of sensor technology and smart devices are rarely taken into consideration so far. These technologies enable collecting data on users and their environment. The information obtained, e.g. the location of a user, his motion, or biological parameters, as well as conclusions, e.g. about the current situation, can be integrated into approaches to support self-management.
The aim of this research project is to develop and test a concept of an innovative assistance system for context-sensitive self-management. In particular, the potential of sensors and smart devices shall be examined to collect necessary data and to implement a ubiquitously usable recommendation feature. The results shall be used to develop a personalised, situation-aware, and stress-sensitive assistance system for self-management.
A Method for Reference Enterprise Architecture Development
Aiming at successful organizational transformation and Business-IT alignment, Enterprise Architectures (EA) provide a holistic picture of an organization. Therefore, EA models capture different perspectives of the organization at hand, i.e. strategy, organizational structures, business processes and its responsibilities, data models, application landscapes and IT infrastructures, and relates these perspectives with each other. Research in Enterprise Architecture Management (EAM) offers a plethora of frameworks, methods, modeling languages and tools to develop, deploy and analyze EA models. Using these, each organization holds its own individual EA. Still, they can be grouped together using characteristics (such as which industry they operate in) and, hence, share commonalities regarding their structure. Further, different organizations share the same environmental dynamics. Changes in their environment (e.g. of regulatory nature or technologic developments) might have similar consequences for different organizations. Here, reference models help to identify common structures and derive universally valid solutions using EA models. Organizations then can apply these reference EA models to their organizational specifics in order to use them for efficient and effective organizational transformation. Although there exist several reference models that refer to EA structures, research lacks a concrete methodical approach how to develop reference enterprise architectures.
The objective of this PhD project is to provide a method for the development of reference enterprise architectures. In this context, the it focuses on reference models for groups of organizations, that operate in a dynamic business environment. The method will be developed using a research design, which follows Design Science Research principles. The method will be validated by applying it to various case studies. Consequently, explicit reference EA models will be produced during the course of the project, which can be seen as artefacts of this endeavor themselves.
In this regard, several approaches of the reference modeling research domain will be applied. Relevant research issues are among others: At what point a reference EA can be understood as a reference? How to elicit the relevant data to develop a reference EA? How can approaches of reference modeling be applied to the structure of EA models? How can deductive and inductive approaches be integrated? Furthermore, the following issues from the EAM domain are addressed: What structure a reference EA model should follow? How can EA models be compared with each other? What approaches regarding EA analysis can be used for the project?