Anne Gutschmidt

Multi-touch Table or Plastic Wall? Design of a Study for the Comparison of Media in Modeling

An important aspect of participatory enterprise modeling is the work in a group. However, does the collaboration of the group members change depending on which medium is used to generate the models? Are there for example differences in the group’s behavior when working with a plastic wall, similar to a whiteboard, or with a multi-touch table? Based on the state of research and theoretical foundations of group work as well as previous research, relevant research issues are raised and an experimental design will be described in order to examine possible differences in the group work depending on the medium. Relevant aspects will be forms of cooperation, i.e. verbal and nonverbal contributions of the participants, but also territorial behavior and group performance.

Moving Type Detection without Time Information

Today location technologies are integrated into many devices enabling location-based services. Movement data recorded with these devices can be uploaded to web sites and shared with others. Movement data can be organized using keywords and semantic tags, e.g. walking and running. Our main goal is to automatically classify movement data as walking, cycling or driving. In contrast to other work we use real world GPS data without time information. Users delete time information to protect their privacy. Over a period of two months we collected movement data from a popular track sharing platform and classified the GPS tracks using Decision Tree, Naive Bayes, Support Vector Machine and Naive Bayes Tree. Our results show that users can expect reasonable results from tag suggestion services detecting moving type even without time information. A second result of our work indicates that angle-based features are insignificant for classification using real world GPS tracks. Distance was the only significant feature in our study.

An approach to situational market segmentation on on-line newspapers based on current tasks

We discuss how a task-based situational market segmentation may be applied to on-line newspapers, distinguishing between fact finding, information gathering and browsing. During a period of four weeks we had 41 users keep a diary and recorded their surfing behavior on different on-line newspapers. The results of a Naive Bayes classification with feature selection indicate that content-related attributes such as the number of news categories browsed are indispensable for task recognition.