Process mining employs event logs to provide insights into the actual processes. Event logs are recorded by information systems and contain valuable information helping organizations to improve their processes. However, these data also include highly sensitive private information which is a major concern when applying process mining. Therefore, privacy preservation in process mining is growing in importance, and new techniques are being introduced. The effectiveness of the proposed privacy preservation techniques needs to be evaluated. It is important to measure both sensitive data protection and data utility preservation. In this paper, we propose an approach to quantify the effectiveness of privacy preservation techniques. We introduce two measures for quantifying disclosure risks to evaluate the sensitive data protection aspect. Moreover, a measure is proposed to quantify data utility preservation for the main process mining activities. The proposed measures have been tested using various real-life event logs.
An online community is a virtual community where people can express their opinions and their knowledge freely. There are a great deal of information in online communities, however there is no way to determine its authenticity. Thus the knowledge which has been shared in online communities is not reliable. By determining expertise level of users and finding experts in online communities the accuracy of posted comments can be evaluated. In this study, a hybrid method for expert finding in online communities is presented which is based on content analysis and social network analysis. The content analysis is based on concept map and the social network analysis is based on PageRank algorithm. To evaluate the proposed method java online community was selected and then correlation between our results and scores prepared by java online community was calculated. Based on obtained results Spearman correlation for 11 subcategories of java online community using this method is 0.904, which is highly an acceptable value.
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