Reißner, D., Armas-Cervantes, A., Conforti, R., Dumas, M., Fahland, D., & La Rosa, M. (2020). Scalable alignment of process models and event logs: An approach based on automata and S-components. Information Systems, 94, [101561]. https://doi.org/10.1016/j.is.2020.101561 Abstract Given a model of the expected behavior of a business process and given an event log recording its observed Read More …
Zaman, R., & Hassani, M. (2020). On Enabling GDPR Compliance in Business Processes Through Data-Driven Solutions. SN Computer Science, 1(4), 1-15. [210]. https://doi.org/10.1007/s42979-020-00215-x Abstract The collection and long-term retention of excessive data enables organisations to process data for insights in non-primary processes. The discovery of insights is promoted to be useful both for organisations and Read More …
Martin, N., Pufahl, L., & Mannhardt, F. (2021). Detection of batch activities from event logs. Information Systems, 95, [101642]. https://doi.org/10.1016/j.is.2020.101642 Abstract Organizations carry out a variety of business processes in order to serve their clients. Usually supported by information technology and systems, process execution data is logged in an event log. Process mining uses this Read More …
van Zelst, S. J., Mannhardt, F., de Leoni, M., & Koschmider, A. (2020). Event abstraction in process mining: literature review and taxonomy. Granular Computing, XX(XX). https://doi.org/10.1007/s41066-020-00226-2 Abstract The execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., Read More …
Martin, N., De Weerdt, J., Fernández-Llatas, C., Gal, A., Gatta, R., Ibáñez, G., Johnson, O., Mannhardt, F., Marco-Ruiz, L., Mertens, S., Munoz-Gama, J., Seoane, F., Vanthienen, J., Wynn, M. T., Boilève, D. B., Bergs, J., Joosten-Melis, M., Schretlen, S., & Acker, B. V. (2020). Recommendations for enhancing the usability and understandability of process mining in Read More …
Janiesch, C., Koschmider, A., Mecella, M., Weber, B., Burattin, A., Di Ciccio, C., Fortino, G., Gal, A., Kannengiesser, U., Leotta, F., Mannhardt, F., Marrella, A., Mendling, J., Oberweis, A., Reichert, M., Rinderle-ma, S., Serral, E., Song, W., Su, J., Torres, V., Weidlich, M., Weske, M., Zhang, L. (2020). The Internet of Things Meets Business Process Read More …
Mannhardt, F., Koschmider, A., Biermann, L., Lange, J., Tschorsch, F., & Wynn, M. (2020). Trust and Privacy in Process Analytics. Enterprise Modelling and Information Systems Architectures (EMISAJ), 15(8). https://doi.org/10.18417/emisa.15.8
Mannhardt, F., Petersen, S. A., & Oliveira, M. F. (2019). A trust and privacy framework for smart manufacturing environments. Journal of Ambient Intelligence and Smart Environments, 11(3), 201-219. https://doi.org/10.3233/AIS-190521 Abstract Operators in industrial manufacturing environments are under pressure to cope with the ever increasing flexibility and complexity of work. Transitioning towards data-driven smart manufacturing environments Read More …
Mannhardt, F., Koschmider, A., Baracaldo, N., Weidlich, M., & Michael, J. (2019). Privacy-Preserving Process Mining: Differential Privacy for Event Logs. Business and Information Systems Engineering, 61(5), 595-614. https://doi.org/10.1007/s12599-019-00613-3 Abstract Privacy regulations for data can be regarded as a major driver for data sovereignty measures. A specific example for this is the case of event data Read More …
Mannhardt, F., Petersen, S. A., & Oliveira, M. (2019). Process Mining and Privacy in Smart Manufacturing. Informatik-Spektrum, 42(5), 336-339. https://doi.org/10.1007/s00287-019-01199-6
Michael, J., Koschmider, A., Mannhardt, F., Baracaldo, N., & Rumpe, B. (2019). User Centered and Privacy-Driven Process Mining System Design: (Extended Abstract). Informatik-Spektrum, 42(5), 347-348. https://doi.org/10.1007/s00287-019-01202-0
González López de Murillas, E., Reijers, H. A., & van der Aalst, W. M. P. (2019). Connecting databases with process mining: a meta model and toolset. Software and Systems Modeling, 18(2), 1209-1247. https://doi.org/10.1007/s10270-018-0664-7 Abstract Process mining techniques require event logs which, in many cases, are obtained from databases. Obtaining these event logs is not a Read More …
Graafmans, T. L. F., Türetken, O., Poppelaars, J. J. G. H., & Fahland, D. (Accepted/In press). Process mining for six sigma: a guideline and tool support. Business & Information Systems Engineering, 63(3), 277-300. https://doi.org/10.1007/s12599-020-00649-w. Abstract Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges Read More …
van Eck, M. L., Sidorova, N., & van der Aalst, W. M. P. (2019). Guided interaction exploration and performance analysis in artifact-centric process models. Business and Information Systems Engineering, 61(6), 649-663. https://doi.org/10.1007/s12599-018-0546-0 Abstract Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the Read More …
de Murillas, E. G. L., Reijers, H. A., & van der Aalst, W. M. P. (Accepted/In press). Case notion discovery and recommendation: automated event log building on databases. Knowledge and Information Systems. https://doi.org/10.1007/s10115-019-01430-6 Abstract Process mining techniques use event logs as input. When analyzing complex databases, these event logs can be built in many ways. Read More …
van Zelst, S. J., Bolt, A., & van Dongen, B. F. (2018). Computing alignments of event data and process models. In M. Koutny, L. M. Kristensen, & W. Penczek (Eds.), Transactions on Petri Nets and Other Models of Concurrency XIII (pp. 1-26). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Read More …
Leopold, H., van der Aa, H., Offenberg, J., & Reijers, H. A. (2019). Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels. Information Systems, 83, 30-39. DOI: 10.1016/j.is.2019.02.005 Abstract Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Read More …
Li, G., & de Carvalho, R. M. (2019). Process mining in social media: applying object-centric behavioral constraint models. IEEE Access, 7, 84360-84373. [8746275]. DOI: 10.1109/ACCESS.2019.2925105 Abstract The pervasive use of social media (e.g., Facebook, Stack Exchange, and Wikipedia) is providing unprecedented amounts of social data. Data mining techniques have been widely used to extract knowledge Read More …
Hassani, M., van Zelst, S. J., & van der Aalst, W. M. P. (2019). On the application of sequential pattern mining primitives to process discovery: overview, outlook and opportunity identification. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6), [e1315]. DOI: 10.1002/widm.1315 Abstract Sequential pattern mining (SPM) is a well-studied theme in data mining, in Read More …
van Zelst, S. J., Bolt Irondio, A. J., Hassani, M., van Dongen, B. F., & van der Aalst, W. M. P. (2019). Online conformance checking: relating event streams to process models using prefix-alignments. International Journal of Data Science and Analytics, 8(3), 269-284. DOI: 10.1007/s41060-017-0078-6 Abstract Companies often specify the intended behaviour of their business processes Read More …
Leemans, S. J. J., & Fahland, D. (2019). Information-preserving abstractions of event data in process mining. Knowledge and Information Systems. DOI: 10.1007/s10115-019-01376-9 Abstract Process mining aims at obtaining information about processes by analysing their past executions in event logs, event streams, or databases. Discovering a process model from a finite amount of event data thereby Read More …
Tax, N., Alasgarov, E. E., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2019). Generating time-based label refinements to discover more precise process models. Journal of Ambient Intelligence and Smart Environments, 11(2), 165-182. DOI: 10.3233/AIS-190519 Abstract Process mining is a research field focused on the analysis of event data with the aim Read More …
Dijkman, R., Gao, J., Syamsiyah, A., van Dongen, B., Grefen, P., & ter Hofstede, A. (2019). Enabling efficient process mining on large data sets: realizing an in-database process mining operator. Distributed and Parallel Databases, 38(1), 227-253. https://doi.org/10.1007/s10619-019-07270-1 Abstract Process mining can be used to analyze business processes based on logs of their execution. These execution Read More …
Hassani, M., Töws, D., Cuzzocrea, A., & Seidl, T. (2019). BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams. International Journal of Data Science and Analytics, 8(3), 223-239. DOI: 10.1007/s41060-017-0084-8 Abstract Supporting sequential pattern mining from data streams is nowadays a relevant problem in the area of data stream mining Read More …
Syamsiyah, A., Dongen, B. F. van, & Dijkman, R. M. (2018). Native directly follows operator. arXiv. Abstract Typical legacy information systems store data in relational databases. Process mining is a research discipline that analyzes this data to obtain insights into processes. Many different process mining techniques can be applied to data. In current techniques, an Read More …
Rekik, Molka, Boukadi, Khouloud, Assy, Nour, Gaaloul, Walid & Ben-Abdallah, Hanene (2018). Optimal deployment of configurable business processes in cloud federations. IEEE Transactions on Network and Service Management, Abstract Configurable processes are increasingly being adopted by enterprises that seek experience sharing and best practice adoption. A configurable process is a customizable model that specifies how Read More …
Kalenkova, Anna, Burattin, Andrea, de Leoni, Massimiliano, van der Aalst, Wil & Sperduti, Alessandro (2019). Discovering high-level BPMN process models from event data. Business Process Management Journal, 25(5), 995-1019. DOI: 10.1108/BPMJ-02-2018-0051 Abstract Purpose: The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using Read More …
Beemt, A.A.J. van den, Buijs, J.C.A.M. & Aalst, W.M.P. van der (2018). Analysing structured learning behaviour in Massive Open Online Courses (MOOCs) : An approach based on process mining and clustering. International Review of Research in Open and Distance Learning, 19(5), 38-60. Abstract The increasing use of digital systems to support learning leads to a Read More …
Leemans, S.J.J., Fahland, D. & van der Aalst, W.M.P. (2018). Scalable process discovery and conformance checking. Software and Systems Modeling, 17(2), 599-631. Abstract Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important Read More …
Lee, Wai Lam Jonathan, Verbeek, H.M.W., Munoz-Gama, Jorge, van der Aalst, Wil M.P. & Sepúlveda, Marcos (2018). Recomposing conformance : Closing the circle on decomposed alignment-based conformance checking in process mining. Information Sciences, 466, 55-91. Abstract In the area of process mining, efficient conformance checking is one of the main challenges. Several process mining vendors Read More …
Jalali, A., Maggi, F.M. & Reijers, H.A. (2018). A hybrid approach for aspect-oriented business process modeling. Journal of Software : Evolution and Process, 30(8):e1931 Abstract Separation of concerns has long been an important strategy to deal with complexity when developing a system. Some concerns (like security) are scattered through the whole system, and different modules Read More …
van Zelst, S.J., van Dongen, B.F. & van der Aalst, W.M.P. (2018). Event stream-based process discovery using abstract representations. Knowledge and Information Systems, 54(2), 407-435. Abstract The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery Read More …
van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P. && Verbeek, H.M.W. (2018). Discovering workflow nets using integer linear programming. Computing, 100(5), 529-556. Abstract Process mining is concerned with the analysis, understanding and improvement of business processes. Process discovery, i.e. discovering a process model based on an event log, is considered the most challenging Read More …
Tax, Niek, Dalmas, Benjamin, Sidorova, Natalia, van der Aalst, Wil M.P. & Norre, Sylvie (2018). Interest-driven discovery of local process models. Information Systems, 77, 105-117. Abstract Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process Read More …
Tax, N., Lu, X., Sidorova, N., Fahland, D. & van der Aalst, W.M.P. (2018). The imprecisions of precision measures in process mining. Information Processing Letters, 135, 1-8. Abstract In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been Read More …
Tax, N., Sidorova, N. & van der Aalst, W.M.P. (2019). Discovering more precise process models from event logs by filtering out chaotic activities. Journal of Intelligent Information Systems, 52(1), 107-139. DOI: 10.1007/s10844-018-0507-6 Abstract Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that Read More …
Polato, Mirko, Sperduti, Alessandro, Burattin, Andrea & de Leoni, Massimiliano (2018). Time and activity sequence prediction of business process instances. Computing, 100(9), 1005-1031 Abstract The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to Read More …
Mendling, J., Weber, I., van der Aalst, W.M.P., vom Brocke, J., Cabanillas, C., Daniel, F., Debois, S., Di Ciccio, C., Dumas, M., Dustdar, S., Gal, A., García-Bañuelos, L., Governatori, G., Hull, R., La Rosa, Marcello, Leopold, Henrik, Leymann, Frank, Recker, Jan, Reichert, Manfred, Reijers, H.A., Rinderlema, Stefanie, Solti, Andreas, Rosemann, Michael, Schulte, Stefan, Singh, Munindar Read More …
Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P. & Toussaint, P. (2018). Guided Process Discovery : A Pattern-based Approach. Information Systems, 76, 1-18 Abstract Process mining techniques analyze processes based on events stored in event logs. Yet, low-level events recorded by information systems may not directly match high-level activities that make sense Read More …
Fahland, Dirk & Völzer, Hagen (2018). Dynamic skipping and blocking, dead path elimination for cyclic workflows, and a local semantics for inclusive gateways. Information Systems, 78, 126-143. Abstract We propose and study dynamic versions of the classical flexibility constructs ‘skip’ and ‘block’ for workflows and motivate and define a formal semantics for them. We show Read More …
