Research Output per year
Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.
Clustering algorithms
Engineering & Materials Science
Data storage equipment
Engineering & Materials Science
Data mining
Engineering & Materials Science
Query processing
Engineering & Materials Science
Hidden Markov models
Engineering & Materials Science
Miners
Engineering & Materials Science
Experiments
Engineering & Materials Science
Data acquisition
Engineering & Materials Science
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Research Output 2009 2019
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., 16 Jan 2019, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers (IEEE), p. 2953-2959 7 p. 8616498Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Learning systems
Data storage equipment
Markov model
Machine Learning
Scenarios
Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts
Spenrath, Y. & Hassani, M., 1 Jan 2019, Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019. Papotti, P. (ed.). CEUR-WS.org, 8 p. (CEUR Workshop Proceedings; vol. 2322)Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Open Access
File
Industry
Overview of efficient clustering methods for high-dimensional big data streams
Hassani, M., 1 Jan 2019, Clustering Methods for Big Data Analytics. Nasraoui, O. & Ben N'Cir, C-E. (eds.). Cham: Springer, p. 25-42 18 p. (Unsupervised and Semi-Supervised Learning)Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review
Clustering algorithms
Data mining
Sensors
Big data
A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently
Cuzzocrea, A., Mumolo, E., Hassani, M. & Grasso, G. M., 1 Jan 2018, Proceedings - DMSVIVA 2018: Proceedings of the 24th International DMS Conference on Visualization and Visual Languages. Pittsburgh: Knowledge Systems Institute Graduate School, p. 83-90 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Data storage equipment
Hidden Markov models
Computer science
Learning algorithms
Learning systems
Analyzing customer journey with process mining: from discovery to recommendations
Terragni, A. & Hassani, M., 7 Sep 2018, Proceedings - 2018 IEEE 6th International Conference on Future Internet of Things and Cloud, FiCloud 2018. Younas, M. & Disso, J. P. (eds.). Piscataway: Institute of Electrical and Electronics Engineers (IEEE), p. 224-229 6 p. 8458017Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Marketing
Industry
Courses
Student theses
Analyzing customer journey with process mining: from discovery to recommendations
Author: Terragni, A., 24 Sep 2018Supervisor: Hassani, M. (Supervisor 1), Vitali, M. (External person) (External coach) & Carrá, A. (External person) (External coach)
Student thesis: Master
File
Comparative analysis of standard and process mining-based IT-auditing approaches
Author: Maliepaard, B., 25 Sep 2017Supervisor: Fahland, D. (Supervisor 1), Hassani, M. (Supervisor 2), Dijkman, R. (Supervisor 2) & van der Waerdt, T. (External person) (External coach)
Student thesis: Master
File
Effective steering of customer journeys via context-aware recommendations: introducing OARA, the Order Aware Recommendation Approach
Author: Goossens, J., 29 Oct 2018Supervisor: Hassani, M. (Supervisor 1), Neergaard, M. (External person) (External coach) & Eshuis, R. (Supervisor 2)
Student thesis: Master
File
On the potential for machine learning in prediction of insurance policy sales: helping insurance intermediaries get insights in their clients' insurance needs
Author: Ampt, A., 31 Aug 2017Supervisor: Pechenizkiy, M. (Supervisor 1), Broeders, W. (. (External person) (External coach) & Hassani, M. (Supervisor 2)
Student thesis: Master
File