What Shall I Do Next? Intention Mining for Flexible Process Enactment

International audienceBesides the benefits of flexible processes, practical implementations of process aware information systems have also revealed difficulties encountered by process participants during enactment. Several support and guidance solutions based on process mining have been proposed, but they lack a suitable semantics for human reasoning and decisions making as they mainly rely on low level activities. Applying design science, we created FlexPAISSeer, an intention mining oriented approach, with its component artifacts: 1) IntentMiner which discovers the intentional model of the executable process in an unsupervised manner; 2) In-tentRecommender which generates recommendations as intentions and confidence factors, based on the mined intentional process model and probabilistic calculus. The artifacts were evaluated in a case study with a Netherlands software company, using a Childcare system that allows flexible data-driven process enactment

Providing awareness, explanation and control of personalized filtering in a social networking site

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user’s usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the “filter bubble” problem and increase the users’ trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users’ trust in the system