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Process similarity measure plays an important role in business process management and is usually considered as a versatile solution to fulfill the effective utilization of process models. Although many studies have worked on different notions of process similarity, most of them are not precise enough, as they simply compare processes with respect to the model structure features or the model behavior features separately. To address the problem, in this paper, we propose to measure the business process similarity by considering both process models and process logs. The process models are pre-defined descriptions of business processes, and the process logs can be considered as an objective observation of the actual process execution behavior. The combination of both can help to better character business processes. More specifically, two effective frameworks together with four novel approaches are presented. The former first constructs a weighted business process graph (WBPG) from the process model and the process log, and then computes the similarity of two corresponding WBPGs by using the weighted graph edit distance measure and the weighted node adjacent relation similarity measure. The latter first measures the similarity of process logs and the similarity of process models separately, and then merges the results. Finally, by experimental evaluation, we demonstrate the effectiveness and the applicability of the proposed approaches by comparing them with the start of the art.

User similarity measure plays an important role in various location-based services including location prediction and recommendation. However, existing similarity computation methods fail to meet the distance metric axioms. In addition, existing works also suffer from some deficiency when identifying indoor stay regions and representing semantic information. To address these issues, this paper proposes a new method to evaluate user similarity by analyzing the global positioning system (GPS) trajectory data. Specifically, a more accurate algorithm for indoor stay region identification is proposed by taking velocity into account. Word embedding technique is used to compute the semantic distance between two stay regions. After that, stay regions are clustered and the user's GPS trajectories are represented as a multiset of semantic label sequences. Then a distance metric of these multisets satisfying the distance metric axioms is proposed on the basis of the balanced transportation problem. Finally, the effectiveness of the proposed method is evaluated by experiments using both synthetic and real-life datasets.