Overview of efficient clustering methods for high-dimensional big data streams

Hassani, M. (2019). Overview of efficient clustering methods for high-dimensional big data streams. In O. Nasraoui, & C-E. Ben N’Cir (Eds.), Clustering Methods for Big Data Analytics (pp. 25-42). (Unsupervised and Semi-Supervised Learning). Cham: Springer. https://doi.org/10.1007/978-3-319-97864-2_2 Abstract The majority of clustering approaches focused on static data. However, a big variety of recent applications and research Read More …

Process mining meets GDPR compliance: the right to be forgotten as a use case

Zaman, R., & Hassani, M. (2019). Process mining meets GDPR compliance: the right to be forgotten as a use case. In B. van Dongen, & J. Claes (Eds.), ICPM Doctoral Consortium 2019: Proceedings of the ICPM 2019 Doctoral Consortium co-located with 1st International Conference on Process Mining (ICPM 2019) (CEUR Workshop Proceedings; Vol. 2432). CEUR-WS.org. Read More …

Optimizing customer journey using process mining and sequence-aware recommendation

Terragni, A., & Hassani, M. (2019). Optimizing customer journey using process mining and sequence-aware recommendation. In SAC ’19 Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 57-65). New York: Association for Computing Machinery, Inc. DOI: 10.1145/3297280.3297288 Abstract Customer journey analysis aims at understanding customer behavior both in the traditional offline setting and through Read More …

Online comparison of streaming process discovery algorithms

Baskar, K., & Hassani, M. (2019). Online comparison of streaming process discovery algorithms. In B. Depaire, J. De Smedt , & M. Dumas (Eds.), Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019 co-located with 17th International Conference on Business Process Management (BPM 2019) (pp. 164-168). (CEUR Workshop Proceedings; Vol. 2420). Read More …

Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts

Spenrath, Y., & Hassani, M. (2019). Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. In P. Papotti (Ed.), Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019 (CEUR Workshop Proceedings; Vol. 2322). CEUR-WS.org. Abstract Bottleneck prediction is an important sub-task of process mining that aims at optimizing Read More …

Concept drift detection of event streams using an adaptive window

Hassani, M. (2019). Concept drift detection of event streams using an adaptive window. In 33rd International ECMS Conference on Modelling and Simulation, ECMS 2019 (pp. 230-239). [DSM 73] (Proceedings – European Council for Modelling and Simulation, ECMS; Vol. 33). Abstract Process mining is an emerging data mining task of gathering valuable knowledge out of the Read More …

An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models

Cuzzocrea, A., Mumolo, E., & Hassani, M. (2019). An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models. In Proceedings – 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 2953-2959). [8616498] Institute of Electrical and Electronics Engineers. DOI: 10.1109/SMC.2018.00502 Abstract This paper Read More …

Effective steering of customer journey via order-aware recommendation

Goossens, J. A. J., Demewez, T., & Hassani, M. (2018). Effective steering of customer journey via order-aware recommendation. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 828-837). Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/ICDMW.2018.00123 Abstract The analysis of customer journeys is a subject undergoing an intense study recently . The Read More …

On the application of sequential pattern mining primitives to process discovery: overview, outlook and opportunity identification

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 …

Online conformance checking: relating event streams to process models using prefix-alignments

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 …

BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams

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 …

Analyzing customer journey with process mining : from discovery to recommendations

Terragni, Alessandro & Hassani, Marwan (2018). Analyzing customer journey with process mining : from discovery to recommendations. In Muhammad Younas & Jules Pagna Disso (Eds.), Proceedings – 2018 IEEE 6th International Conference on Future Internet of Things and Cloud, FiCloud 2018 (pp. 224-229). Piscataway: Institute of Electrical and Electronics Engineers (IEEE). Abstract Customer journey analysis Read More …

Towards effective generation of synthetic memory references via markovian models

Cuzzocrea, Alfredo, Mumolo, Enzo, Hassani, Marwan & Grasso, Giorgio Mario (2018). Towards effective generation of synthetic memory references via markovian models. In Ling Liu, Claudio Demartini, Ji-Jiang Yang, Thomas Conte, Kamrul Hasan, Edmundo Tovar, Zhiyong Zhang, Sheikh Iqbal Ahamed, Stelvio Cimato, Toyokazu Akiyama, Sorel Reisman, William Claycomb, Motonori Nakamura, Hiroki Takakura & Chung-Horng Lung (Eds.), Read More …

A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently

Cuzzocrea, Alfredo, Mumolo, Enzo, Hassani, Marwan & Grasso, Giorgio Mario (2018). A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Proceedings – DMSVIVA 2018 (pp. 83-90). Pittsburgh: Knowledge Systems Institute Graduate School. Abstract Driven by several real-life case studies and in-lab developments, synthetic memory reference generation has a long tradition Read More …

Publications in 2017

Article Scientific peer reviewed Arriagada-Benítez, M., Sepúlveda, M., Munoz-Gama, J. & Buijs, J.C.A.M. (2017). Strategies to automatically derive a process model from a configurable process model based on event data. Applied Sciences, 7(10):1023. Bolt, A., de Leoni, M. & van der Aalst, W.M.P. (2017). Process variant comparison: using event logs to detect differences in behavior Read More …