![]() In: Proceedings of ICNN 1995 - International Conference on Neural Networks. Kennedy, J., Eberhart, R.: Particle swarm optimization. Joglekar, M., Garcia-Molina, H., Parameswaran, A.: Interactive data exploration with smart drill-down. Grahne, G., Zhu, J.: Fast algorithms for frequent itemset mining using FP-trees. Van Eck, M.L., Sidorova, N., van der Aalst, W.M.P.: Guided interaction exploration and performance analysis in artifact-centric process models. VLDB 10, 1937–1940 (2017)ĭijkman, R., Wilbik, A.: Linguistic summarization of event logs – a practical approach. thesis, MIT (1985)ĭemiralp, Ç., Haas, P.J., Parthasarathy, S., Pedapati, T.: Foresight: recommending visual insights. ĭavis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results. In: Mayr, H.C., Guizzardi, G., Ma, H., Pastor, O. 74, 53–66 (2018)Ĭhatain, T., Carmona, J., van Dongen, B.: Alignment-based trace clustering. Parallel Databases 34(3), 379–423 (2015)īolt, A., de Leoni, M., van der Aalst, W.M.P.: Process variant comparison: using event logs to detect differences in behavior and business rules. īeheshti, S.M.R., Benatallah, B., Motahari-Nezhad, H.R.: Scalable graph-based OLAP analytics over process execution data. īallambettu, N.P., Suresh, M.A., Bose, R.P.J.C.: Analyzing process variants to understand differences in key performance indices. Van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Our results show that ProcessExplorer can be successfully applied to analyze and explore real-life data sets efficiently. We implemented our approach into an interactive visual exploration system, which we use as part of a user study conducted to evaluate our approach. ProcessExplorer takes an event log as input to automatically suggest subsets of similar process behavior, evaluate each subset, generate interesting insights, and suggest the subsets with the most interesting characteristics. ![]() In this paper, we propose ProcessExplorer, an interactive process mining approach to enable fast data analysis and exploration. Due to the increase of process complexity in flexible environments, visual exploration is increasingly becoming more challenging. Process mining enables the extraction of valuable knowledge from event logs, such as deviations, bottlenecks, and anomalies. Due to the highly competitive pressure in the market, organizations are particularly interested in optimizing their processes. Large amount of data is collected in event logs from information systems, reflecting the actual execution of business processes.
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