Baumann, Sebastian

Fettke, Peter

Rehse, Jana-Rebecca

Schuhmann, Steffen

MAnnheim DOCument Server

Interactive Process Clustering with t-SNESteffen Schuhmann1,2, Jana-Rebecca Rehse1,3, Sebastian Baumann1,2, andPeter Fettke1,21 German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany{firstname.lastname}@dfki.de2 Saarland University, Saarbrücken, Germany3 University of Mannheim, Mannheim, Germanyrehse@uni-mannheim.deAbstract. Process trace clustering is a well-studied and powerful tech-nique to support the discovery of high-quality process models. It splitsan event log into more cohesive sublogs, such that the discovered processmodels are easier to read and to understand. However, existing clusteringapproaches typically optimize measures like fitness or precision instead offocusing on the model understandability and utility, as assessed by a pro-cess analyst. In addition, they offer no opportunity to influence or adaptthe clustering result according to the analyst’s use case or preferences.In this paper, we propose an interactive tool to trace clustering based onthe t-SNE algorithm. Traces are represented in a two-dimensional graph,where they can be selected interactively for process discovery. We alsooffer the user some guidance with a predefined selection of possible clus-ters. Using this system, a process analyst is able to find a representativeset of process models for each event log without any knowledge in pro-gramming and a basic understanding of the used discovery techniques.Keywords: Trace Clustering · Process Discovery · Process Analytics ·Interactive Data Analytics1 IntroductionThe main goal of process discovery is to visualize a real-life business process, asrecorded in an event log, in a human-readable way [7]. In reality, however, dis-covery approaches often produce spaghetti models, i.e., highly complex modelsthat are difficult to read and to understand [2]. Spaghetti models originate inoverly complex process logs. For example, if the process contains multiple vari-ants for handling different types of business objects, all of those variants needto be included in the discovered model [7]. In this case, it makes sense to splitthe event log into multiple logs and discover a separate model for each of them[1]. The challenge lies in determining the best way to split the log, such that weend up with a minimal number of maximally useful models. For this purpose,process trace clustering is a well-known and effective technique, which has beenextensively studied and applied in many contributions, e.g., [2, 3, 6].Copyright © 2020 for this paper by its authors. Use permitted under Creative CommonsLicense Attribution 4.0 International (CC BY 4.0).2 S. Schuhmann et al.However, those existing clustering approaches typically optimize measureslike fitness or precision, whereby model understandability and utility are consid-ered after generating the process models of the clusters. In addition, they offerthe analyst no opportunity to influence or adapt the clustering result accordingto the concrete use case or preferences and they are often not integrated withprocess discovery.Therefore, we designed a novel interactive process clustering (IPC) tool basedon the t-SNE algorithm [8]. This algorithm is well suited for embedding high-dimensional data, such as a trace similarity matrix, into a lower dimensionalspace. The embedding of such a similarity matrix can then be visualized in atwo-dimensional graph, where traces with a high similarity are placed closertogether.We integrated this clustering technique into an interactive web-based tool,where the analyst can influence the clustering parameters, select clusters oftraces, discover models for those clusters, and compare their similarity. Moreover,we included process discovery algorithms to compute a set of process models thatappropriately represent the event log. Compared to existing clustering tools, IPCis both interactive and visual, giving the process analyst a useful guidance toolwith a high degree of freedom. The visual representation of the two-dimensionalembedding leads to a better understandability of the coherence in the event log.Also, the free selection provides the user with the ability to select groups basedon the utility of the concrete use case.2 Main Characteristics and InnovationThe objective of the IPC tool is to provide process analysts with an easily un-derstandable and interactive visualization of trace similarities, to find an appro-priate set of process models to represent the log at hand. As outlined in Fig. 1,the underlying approach consists of four major steps, which are either backendcomputations or frontend interactions between the tool and the user. In the firststep, we compute the pairwise similarity between all traces in the log. The result-ing similarity matrix is used as the basis for the t-SNE embedding in the secondstep. This embedding is then visualized in a two-dimensional graph, which theprocess analyst can use to gain insights into the log and either manually selectclusters for which to discover a process model or have the tool suggest clustersautomatically. The set of discovered models is evaluated by comparing their sim-ilarity, following the idea that the less similar two models are, the more sense itmakes to keep them as separate models.To be easily accessible without a complex installation process, the IPC pro-totype4 is implemented using web technologies and therefore usable with anymodern internet browser. The user interface was designed to support processanalysts by providing them the options needed for the cluster analysis withoutcluttering the UI with too many features or complex options. It is split into two4 http://ipc.sschuhmann.de/Interactive Process Clustering with t-SNE 3Process LogTrace Similarity Matrixt-SNE ResultsProcess Model CollectionProcess Model EvaluationCompareClusterSelect & MineCompare0.80.910.6110.60.80.9Sect. 3.2 Sect. 3.3 Sect. 3.4 Sect. 3.5Fig. 1. Main Idea for our Interactive Process Clustering (IPC) Approachmain screens. Users first see the process log upload prompt. It contains a filepicker to upload an event log in the XES5 format. The second screen, shownin Fig. 2, contains all elements used for the clustering process and can be di-vided into five main groups. On the left, the parametrization section contains allbuttons for parametrizing and starting the t-SNE visualization, the clusteringguidance, and the process discovery for a selected cluster. Next to it, there isa two-dimensional scatterplot, where the t-SNE results are displayed. It givesthe user the option to select a subset of processes as a cluster by dragging abounding box around it. After at least one process model was discovered for aselected cluster, the similarity matrix based on percentage of common activi-ties is displayed on the right side. It shows the similarity between all generatedprocess models in a color-coded matrix, where green elements highlight a lowsimilarity and red elements indicate a high similarity between the process mod-els. This color scheme originates in the goal to find process models that are asdistinct as possible. Below the plot and the similarity matrix, there are threeboxes containing descriptive data about the selected process instances, namelynumber of instances, average case duration, and average case length. Initially,these boxes display information about all contained traces.The lowest section of the user interface shows the process model table. Thistable contains the generated models along with their metadata. These includethe name, which was used to generate the process model, an image of the plothighlighting the selected cluster used to generate the process model, the simi-larity metric used to generate the embedding, and a timestamp indicating thetime of the model generation. The table also contains two interaction buttons,“Show Model” and “Delete”. This former displays the generated process modelin an overlay, the latter removes the process model from the table.IPC is an easy-to-use tool for discovering process models from event logclusters by interactively selecting the clusters on a two-dimensional projection.This projection changes according to the chosen similarity metric and thereforerepresents different aspects of the event log, such as the similarity of the traces’structural composition. There are few other contributions in the process miningfield that emphasize the visualization of trace clustering results, using, e.g., t-SNE. Schirmer et al. use it as a tool for event log pre-processing [5]. Theirapproach is similar to ours in terms of visualization and similarity measures, but5 http://www.xes-standard.org/4 S. Schuhmann et al.Fig. 2. The IPC user interface in the clustering processit is not integrated with process discovery, uses pre-labeled data, and focusesmore on finding outliers as a preprocessing step than on interactive processdiscovery. Different from our approach, it also does not implement a cachingstrategy, which may lead to computing times of several hours to days, dependingon the size of the process log.3 Tool MaturityThe tool was implemented as a demonstration unit to evaluate the usabilityand effectiveness of the proposed clustering approach in a user experience study.To ensure the quality of the software and allow the participants to focus onutility and usability, the implementation is build upon well-known frameworkslike Flask6, scikit-learn7 and PM4Py8 in the backend and D3.js9 and jQuery10 inthe frontend. A video providing a brief overview of the work with the evaluationdataset can be found online11.The goal of our evaluation was to assess the utility of the IPC approach andthe usability of the IPC tool. For this purpose, the 16 participants were givena short introduction to the tool and provided with a publicly available real-lifeevent log. Then, they were asked to find a number of clusters, which they foundappropriate for the given log, i.e., which adequately represented the log withoutproducing too complex process models. Afterwards, they were asked to assess thetool using the User Experience Questionnaire [4]. Users in general appreciatedthe tool, ranking it with a score around 1.8 in attractiveness, efficiency, andstimulation and only a slightly lower score (1.66) for novelty. However, evaluationscores were lower (around 1.2) for perspicuity and dependability.6 https://flask.palletsprojects.com/en/1.1.x/7 https://scikit-learn.org/stable/index.html8 https://pm4py.fit.fraunhofer.de/9 https://d3js.org/10 https://jquery.com/11 https://cloud.dfki.de/owncloud/index.php/s/wb234DfbLAsKmBGInteractive Process Clustering with t-SNE 5Since some of the operations, like calculating the similarity matrix and thet-SNE algorithm, are computationally complex, we implemented caching systemto reduce the number of these operations. This enables the users to run mul-tiple analysis on the same data in a reasonable time. Therefore, we store thecalculation results for the similarity matrix and t-SNE calculation on the server.These cached results are accessed by using a salted hash of the original eventlog. Since the current implementation does not feature a user management, thecached results are accessible for all users with access to the particular dataset.This way, all users benefit from the cached results after the initial calculation.4 Conclusions and future workThis paper presents our tool for Interactive Process Clustering using t-SNEand manual as well as automatic cluster selection. The tool was developed tovalidate the usability of t-SNE in business process analysis and its relevanceto trace clustering. The currently implemented similarity metrics focus on thestructural composition of the process instances. In future work, we will extendthose metrics to enable the user to focus on other aspects of the process traces,such as resources or other metadata. We also will include more advanced processdiscovery algorithm in a later release.References1. Bose, R.P.J.C., van der Aalst, W.: Context aware trace clustering: Towards im-proving process mining results. In: Proceedings of the 2009 SIAM InternationalConference on Data Mining. pp. 401–412 (2009)2. De Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W., Weijters, T.,Van Dongen, B., Saccà, D.: Process mining based on clustering: A quest for preci-sion. In: Business Process Management Workshops. pp. 17–29. Springer (2007)3. Evermann, J., Thaler, T., Fettke, P.: Clustering traces using sequence alignment.In: Proceedings of the 11th International Workshop on Business Process Intelli-gence,. International Workshop on Business Process Intelligence (BPI-15), locatedat International Conference on Business Process Management, July 31 - August 3,Innsbruck, Austria. Springer Berlin Heidelberg (2015)4. Laugwitz, B., Held, T., Schrepp, M.: Construction and evaluation of a user ex-perience questionnaire. In: Holzinger, A. (ed.) USAB 2008: HCI and Usability forEducation and Work. pp. 63–76. Springer (2008)5. Schirmer, L., Campagnolo, L., González, S., Rodrigues, A., Schardong, G., França,R., Lana, M., Barbosa, S., Poggi, M., Lopes, H.: Visual support to filtering cases forprocess discovery. In: Proceedings of the 20th International Conference on EnterpriseInformation Systems. pp. 38–49. Scitepress (2018)6. Thaler, T., Ternis, S., Fettke, P., Loos, P.: A comparative analysis of process instancecluster techniques. In: Thomas, O., Teuteberg, F. (eds.) Proceedings of the 12thInternational Conference on Wirtschaftsinformatik. Universität Osnabrück (2015)7. van der Aalst, W.: Process Mining: Data Science in Action. Springer, 2nd edn.(2016)8. van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of machinelearning research 9, 2579–2605 (2008)

Interactive process clustering with t-SNE

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Interactive process clustering with t-SNE

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