Seminar Process Analytics (2IMI00) 2022

This seminar prepares students for their Master project. By studying recent literature, we discuss and identify how to develop a research question, select the right research method, and plan and conduct the right evaluation. In addition, students get in touch with recent and ongoing research and practical application in the area of Processes and Information Read More …

Advanced Process Mining (2AMI20) 2022

Many real-life phenomena studied with Data Science methods unfold over time. They often involve many people, objectes, agents, machines, entites, etc. that interact with each other while distributed in time and space. Such dynamics are called processes and are present everywhere: in software systems medical treatments, logistics systems, manufacturing, and even entire organizations. Process mining Read More …

A Framework for Efficient Memory Utilization in Online Conformance Checking

Zaman, R., Hassani, M., & van Dongen, B. F. (2021). A Framework for Efficient Memory Utilization in Online Conformance Checking. arXiv.org. https://arxiv.org/pdf/2112.13640.pdf Abstract Conformance checking (CC) techniques of the process mining field gauge the conformance of the sequence of events in a case with respect to a business process model, which simply put is an Read More …

Prefix Imputation of Orphan Events in Event Stream Processing

Zaman, R., Hassani, M., & van Dongen, B. F. (2021). Prefix Imputation of Orphan Events in Event Stream Processing. Frontiers in Big Data, 4, [705243]. https://doi.org/10.3389/fdata.2021.705243 Abstract In the context of process mining, event logs consist of process instances called cases. Conformance checking is a process mining task that inspects whether a log file is Read More …

What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning

Ketykó, I., Mannhardt, F., Hassani, M., & van Dongen, B. F. (2021). What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning. CoRR, abs/2110.10225. https://arxiv.org/abs/2110.10225 Abstract Deep Learning is proven to be an effective tool for modeling sequential data as shown by the success in Natural Language, Computer Vision and Signal Read More …

Online Prediction and Recommendation of Volksbank Customer Journey under Concept Drifts

  Background Within companies customer journey thinking is getting more and more established. Customers are getting more volatile and competition is fierce. With customer journey analytics is it possible to use a personal approach on large dataset of customer behaviour and customer experience. In the field of customer journey analytics multiple well-known fields are combined, Read More …

Predicting Next Touch Point In A Customer Journey – A Use Case In Telecommunication

Hassani, M., & Habets, S. (2021). Predicting Next Touch Point In A Customer Journey – A Use Case In Telecommunication. In 35th ECMS INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION (1 ed., Vol. 35, pp. 48-54). (Proceedings – European Council for Modelling and Simulation, ECMS). https://doi.org/10.7148/2021-0048 Abstract Customer journey analysis is rapidly increasing in popularity, as Read More …

Data Minimisation as Privacy and Trust Instrument in Business Processes

Zaman, R., Hassani, M., & van Dongen, B. F. (Accepted/In press). Data Minimisation as Privacy and Trust Instrument in Business Processes. In Business Process Management Workshops (BPM 2020) Springer. Abstract Data is vital for almost all sorts of business processes and workflows. However, the possession of personal data of other beings bear consequences. Data is Read More …

Facilitating GDPR compliance: the H2020 BPR4GDPR approach

Lioudakis, G. V., Koukovini, M. N., Papagiannakopoulou, E. I., Dellas, N., Kalaboukas, K., de Carvalho, R. M., Hassani, M., Bracciale, L., Bianchi, G., Juan-Verdejo, A., Alexakis, S., Gaudino, F., Cascone, D., & Barracano, P. (2020). Facilitating GDPR compliance: the H2020 BPR4GDPR approach. In I. O. Pappas, I. O. Pappas, P. Mikalef, L. Jaccheri, J. Krogstie, Read More …

On Enabling GDPR Compliance in Business Processes Through Data-Driven Solutions

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 …

Predicting Business Process Bottlenecks In Online Events Streams Under Concept Drifts

Spenrath, Y., & Hassani, M. (2020). Predicting Business Process Bottlenecks In Online Events Streams Under Concept Drifts. In M. Steglich, C. Muller, G. Neumann, & M. Walther (Eds.), Proceedings of European Council for Modelling and Simulation (ECMS) 2020 (pp. 190-196). (Proceedings European Council for Modelling and Simulation; Vol. 34, No. 1). European Council for Modeling Read More …

Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data

Spenrath, Y., Hassani, M., van Dongen, B. F., & Tariq, H. Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. In Y. Dong, D. Mladenic, & C. Saunders (Eds.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track – European Conference, ECML PKDD 2020, Proceedings (pp. Read More …

An innovative online process mining framework for supporting incremental GDPR compliance of business processes

Zaman, R., Cuzzocrea, A., & Hassani, M. (2019). An innovative online process mining framework for supporting incremental GDPR compliance of business processes. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), 2019 IEEE International Conference on Big Data, Big Read More …

Smart Journey Mining: Towards successful digitalisation of services

The digitalisation of our society’s service systems has fundamentally changed the way services are delivered to, and experienced by, humans. Although digital services are supposed to simplify our lives and increase our efficiency, they often frustrate and burden customers, users, and employees. The overall goal is to increase the quality of services and support the Read More …

Online Spatial Prediction Model for Citizens’ Public Space Complaints in Eindhoven

  Smart cities approach does not only emphasize the implementations of new technologies in a city but also highlights the importance of using new technologies for enabling citizens’ engagement in urban planning processes. In that regard, ICTs play a vital role in (i) supporting citizens to report their complaints related to the public spaces (i.e. Read More …

2AMI20 Advanced Process Mining

Understanding and predicting behavior of people and machines in a shared setting (task, project, factory, process, organization) is central to Data Science and Artificial Intelligence. Actions of people and machines can be recorded as discrete events in event sequences (logs), event databases (tables, graphs), and real-time event streams. Learning behavioral models of discrete event data Read More …

Applying Process Mining to Predict Customer Behavior and Recommend Actions

Background In the Dutch health care system health care insurance is obligated for all residents. The government sets the basis package and insurers compete based on price and service. Customer service is therefore very important for every health insurance company; especially in the fast changing digital world. As a result customer satisfaction is the most Read More …

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 …

Real-Time Prediction of Traveler Flow within Digital Stations

In the last decennia the pressure on different types of mobility have severely increased in the Netherlands. Therefor the need for availability and reliability has increased. Siemens Mobility supplies solutions in the Netherlands that contribute to the accessibility and quality of life in this regard. With the help of different technologies, data is being unlocked Read More …

Improving Traffic Flow Prediction in Urban Areas by Incorporating a Real-Time Outlier Detection Model

In the last decennia the pressure on different types of mobility have severely increased in the Netherlands. Therefor the need for availability and reliability has increased. Siemens Mobility supplies solutions in this regard that contribute to everyday accessibility and quality of life. With the help of different technologies, data is being unlocked through which the operation Read More …

Log-based vs. Model-based Concept Drift Detection

StrProMCDD is a recently published work that detects concept drifts in event streams (see the figure below). StrProMCDD uses several model-based distance measures to detect these deviations using an adaptive window concept. In this assignment, we would like to compare the performance of this model-based approach with log-based stream clustering approaches that try to detect drifts in 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 …

Real-Time Process Mining for Customer Journey Data

Available process discovery have been tested in the customer journey context under offline settings. Recent online process discovery approaches like: https://ieeexplore.ieee.org/document/7376771 bring however a lot of added value for a real-time customer journey optimization. The objective of this assignment is to use two different customer journey datasets to test the effectiveness of such approaches for Read More …

Finding Patterns in Evolving Graphs

The analysis of the temporal evolution of dynamic graphs like social networks is a key challenge for understanding complex processes hidden in graph structured data. Graph evolution rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing rule discovery methods make restrictive assumptions on the Read More …

Using Sequential Pattern Mining to Detect Drifts in Streaming Data

BFSPMiner is an effective and efficient batch-free algorithm for mining sequential patterns over data streams was published very recently https://link.springer.com/article/10.1007/s41060-017-0084-8. An implementation of the algorithm is available here: https://github.com/Xsea/BFSPMiner. As BFSPMiner has proven to be effective (see Figures 10-14 of the paper) in different domains (see Table 1 in the paper), we would like to Read More …

Resolving kpn Customer Journey Variances through a Suitable Similarity Measure

The customer journey approach is quickly becoming the game changer within KPN  to become a customer-centric service provider and to improve the customer experience to an un-telco like level. Within this context, we have already connected various touch points of the customer, including calls, chats, store visits, online visits and engineer visits, and we are Read More …

Enriching Customer Journey Prediction in KPN with Context Data

The customer journey approach is quickly becoming the game changer within KPN to become a customer-centric service provider and to improve the customer experience to an un-telco like level. Within this context, we have already connected various touch points of the customer, including calls, chats, store visits, online visits and engineer visits, and we are 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 …

Detecting root causes of complaints and investigating the continuation within the customer journey

In the Dutch health care system health care insurance is obligated for all residents. The government sets the basis package and insurers compete based on price and service. Customer service is therefore very important for every health insurance company; especially in the fast changing digital world. As a result customer satisfaction is the most important Read More …