Semantic-Based Process Mining Technique for Annotation and Modelling of Domain Processes

Article

Okoye, K., Islam, S., Naeem, U. and Sharif, S. 2020. Semantic-Based Process Mining Technique for Annotation and Modelling of Domain Processes. International Journal of Innovative Computing, Information and Control. 16 (3), pp. 899-921. https://doi.org/10.24507/ijicic.16.03.899
AuthorsOkoye, K., Islam, S., Naeem, U. and Sharif, S.
Abstract

Semantic technologies aim to represent information or models in formatsthat are not just machine-readable but also machine-understandable. To this effect, thispaper shows how the semantic concepts can be layered on top of the derived models toprovide a more contextual analysis of the models through the conceptualization method.Technically, the method involves augmentation of informative value of the resulting mod-els by semantically annotating the process elements with concepts that they represent inreal-time settings, and then linking them to an ontology in order to allow for a moreabstract analysis of the extracted logs or models. The work illustrates the method usingthe case study of a learning process domain. Consequently, the results show that a systemwhich is formally encoded with semantic labelling (annotation), semantic representation(ontology) and semantic reasoning (reasoner) has the capacity to lift the process miningand analysis from the syntactic to a more conceptual level.

JournalInternational Journal of Innovative Computing, Information and Control
Journal citation16 (3), pp. 899-921
ISSN1349-418X
Year2020
PublisherICIC International
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Anyone
Digital Object Identifier (DOI)https://doi.org/10.24507/ijicic.16.03.899
Web address (URL)https://doi.org/10.24507/ijicic.16.03.899
Publication dates
OnlineJun 2020
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Deposited25 Jun 2020
Accepted13 Jan 2020
Copyright holder© 2020 ICIC International
Copyright informationAll Rights Reserved.
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Sharif, M., Naeem, U., Islam, S. and Karami, A. 2018. Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network. Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Intelligent Systems Conference (IntelliSys) 2018. London, UK 06 - 07 Sep 2018 Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_34
The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain
Okoye, Kingsley, Islam, S., Naeem, U., Sharif, M., Azam, Muhammad Awais and Karami, A. 2018. The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain. Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Intelligent Systems Conference (IntelliSys) 2018. London, UK 06 - 07 Sep 2018 Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_96
Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing
Ehatisham-ul-Haq, Muhammad, Azam, Muhammad Awais, Loo, Jonathan, Shuang, Kai, Islam, S., Naeem, U. and Amin, Yasar 2017. Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing. Sensors. 17 (9), p. 2043. https://doi.org/10.3390/s17092043
Taskification – Gamification of Tasks
Naeem, U., Islam, S., Sharif, M., Sudakov, Sergey and Azam, Awais 2017. Taskification – Gamification of Tasks. in: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers Association for Computing Machinery (ACM). pp. 631-634
SignalSense - Towards Quality Service
Islam, S., Sharif, M., Naeem, U. and Geehan, James 2017. SignalSense - Towards Quality Service. in: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers Association for Computing Machinery (ACM). pp. 627-630
CrimeSafe - Helping you stay safe
Islam, S., Naeem, U., Sharif, M. and Dovnarovic, Arnold 2017. CrimeSafe - Helping you stay safe. in: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers Association for Computing Machinery (ACM). pp. 642-645
The Future of Enterprise Security with Regards to Mobile Technology and Applications
Tagoe, F. T. and Sharif, M. 2017. The Future of Enterprise Security with Regards to Mobile Technology and Applications. in: Jahankhani, Hamid, Carlile, Alex, Emm, David, Hosseinian-Far, Amin, Brown, Guy, Sexton, Graham and Jamal, Arshad (ed.) Global Security, Safety and Sustainability - The Security Challenges of the Connected World. ICGS3 2017 Proceedings Springer International Publishing.
Actor-Network Theory as a Framework to Analyse Technology Acceptance Model’s External Variables: The Case of Autonomous Vehicles
Seuwou, Patrice, Banissi, Ebad, Ubakanma, George, Sharif, M. and Healey, Ann 2017. Actor-Network Theory as a Framework to Analyse Technology Acceptance Model’s External Variables: The Case of Autonomous Vehicles. in: Jahankhani, Hamid, Carlile, Alex, Emm, David, Hosseinian-Far, Amin, Brown, Guy, Sexton, Graham and Jamal, Arshad (ed.) Global Security, Safety and Sustainability - The Security Challenges of the Connected World. ICGS3 2017 Proceedings Springer International Publishing.
Using semantic-based approach to manage perspectives of process mining: Application on improving learning process domain data
Kingsley, Okoye, Tawil, Abdel-Rahman H., Naeem, U., Islam, S. and Lamine, Elyes 2017. Using semantic-based approach to manage perspectives of process mining: Application on improving learning process domain data. in: 2016 IEEE International Conference on Big Data (Big Data) IEEE. pp. 3529-3538
An efficient system for preprocessing confocal corneal images for subsequent analysis
Sharif, M., Qahwaji, Rami, Hayajneh, Sofyan, Ipson, Stanley, Alzubaidi, Rania and Brahma, Arun 2014. An efficient system for preprocessing confocal corneal images for subsequent analysis. in: 2014 14th UK Workshop on Computational Intelligence (UKCI) IEEE.
Dependence Cluster Visualization
Islam, S., Krinke, Jens and Binkley, David 2010. Dependence Cluster Visualization. in: Proceedings of the 5th international symposium on Software visualization New York, NY, USA Association for Computing Machinery (ACM). pp. 93-102
Artificial Neural Network-Based System for PET Volume Segmentation
Sharif, M., Abbod, Maysam, Amira, Abbes and Zaidi, Habib 2010. Artificial Neural Network-Based System for PET Volume Segmentation. International Journal of Biomedical Imaging. 2010 (105610). https://doi.org/10.1155/2010/105610
Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification
Sharif, M., Abbod, Maysam, Amira, Abbes and Zaidi, Habib 2012. Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification. Advances in Fuzzy Systems. 2012 (327861). https://doi.org/10.1155/2012/327861
Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study
Sharif, M., Abbod, Maysam, Sonoda, Luke I. and Sanghera, Bal 2013. Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study. British Journal of Applied Science & Technology. 4 (4), pp. 634-649. https://doi.org/10.9734/bjast/2014/5084
Preparation of 2D sequences of corneal images for 3D model building
Elbita, Abdulhakim, Qahwaji, Rami, Ipson, Stanley, Sharif, M. and Ghanchi, Faruque 2015. Preparation of 2D sequences of corneal images for 3D model building. Computer Methods and Programs in Biomedicine. 114 (2), pp. 194-205. https://doi.org/10.1016/j.cmpb.2014.01.009
Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images
Sharif, M., Qahwaji, R., Ipson, S. and Brahma, A. 2015. Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images. Applied Soft Computing. 36 (Nov.), pp. 269-282. https://doi.org/10.1016/j.asoc.2015.07.019
An efficient intelligent analysis system for confocal corneal endothelium images
Sharif, M., Qahwaji, R., Shahamatnia, E., Alzubaidi, R., Ipson, S. and Brahma, A. 2015. An efficient intelligent analysis system for confocal corneal endothelium images. Computer Methods and Programs in Biomedicine. 122 (3), pp. 421-436. https://doi.org/10.1016/j.cmpb.2015.09.003
In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study
Alzubaidi, Rania, Sharif, M., Qahwaji, Rami, Ipson, Stanley and Brahma, Arun 2015. In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study. British Journal of Ophthalmology. 100 (1), pp. 41-55. https://doi.org/10.1136/bjophthalmol-2015-306934
Less is more: Temporal fault predictive performance over multiple Hadoop releases
Harman, Mark, Islam, S., Jia, Yue, Minku, Leandro L., Sarro, Federica and Srivisut, Komsan 2014. Less is more: Temporal fault predictive performance over multiple Hadoop releases. in: Goues, Claire Le and Yoo, Shin (ed.) Search-Based Software Engineering Springer International Publishing.
ORBS: Language-Independent Program Slicing
Binkley, David, Gold, Nicolas, Harman, Mark, Islam, S., Krinke, Jens and Yoo, Shin 2014. ORBS: Language-Independent Program Slicing. in: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering New York, NY, USA Association for Computing Machinery (ACM). pp. 109-120
Jolinar: Analysing the Energy Footprint of Software Applications (demo)
Noureddine, A., Islam, S. and Bashroush, R. 2016. Jolinar: Analysing the Energy Footprint of Software Applications (demo). in: Proceedings of the 25th International Symposium on Software Testing and Analysis New York, NY, USA Association for Computing Machinery (ACM). pp. 445-448
Semantic-Based Model Analysis Towards Enhancing Information Values of Process Mining: Case Study of Learning Process Domain
Okoye, Kingsley, Tawila, Abdel-Rahman H., Naeem, U., Islam, S. and Lamine, Elyes 2017. Semantic-Based Model Analysis Towards Enhancing Information Values of Process Mining: Case Study of Learning Process Domain. in: Abraham, Ajith, Cherukuri, Aswani Kumar, Madureira, Ana Maria and Muda, Azah Kamilah (ed.) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016) Springer, Cham.
Assessing the impact of global variables on program dependence and dependence clusters
Binkley, David, Harman, Mark, Hassoun, Youssef, Islam, S. and Li, Zheng 2009. Assessing the impact of global variables on program dependence and dependence clusters. Journal of Systems and Software. 83 (1), pp. 96-107. https://doi.org/10.1016/j.jss.2009.03.038
Requirements for the formal representation of pathophysiology mechanisms by clinicians
de Bono, B., Helvensteijn, M., Kokash, N., Martorelli, I., Sarwar, D., Islam, S., Grenon, P. and Hunter, P. 2016. Requirements for the formal representation of pathophysiology mechanisms by clinicians. Interface Focus. 6 (2), p. 20150075. https://doi.org/10.1098/rsfs.2015.0099
An empirical study on dependence clusters for effort-aware fault-proneness prediction
Yang, Yibiao, Harman, Mark, Krinke, Jens, Islam, S., Binkley, David, Zhou, Yuming and Xu, Baowen 2016. An empirical study on dependence clusters for effort-aware fault-proneness prediction. in: Lo, David, Apel, Sven and Khurshid, Sarfraz (ed.) ASE’16 Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering IEEE/ACM. pp. 296-307
Towards Cloud Security Monitoring: A Case Study
Ismail, Umar Mukhtar, Islam, S. and Islam, S. 2016. Towards Cloud Security Monitoring: A Case Study. in: 2016 Cybersecurity and Cyberforensics Conference (CCC) IEEE.
gUML: Reasoning about Energy at Design Time by Extending UML Deployment Diagrams with Data Centre Contextual Information
Jebraeil, Nigar, Noureddine, A., Doyle, J., Islam, S. and Bashroush, R. 2017. gUML: Reasoning about Energy at Design Time by Extending UML Deployment Diagrams with Data Centre Contextual Information. in: 2017 IEEE World Congress on Services (SERVICES) IEEE. pp. In Press
Cloud Strife: Expanding the Horizons of Cloud Gaming Services
Doyle, J., Islam, S., Bashroush, R. and O'Mahony, Donal 2017. Cloud Strife: Expanding the Horizons of Cloud Gaming Services. in: 2017 IEEE World Congress on Services (SERVICES) IEEE.
A Mutlimodal Approach to Measure the Levels Distraction of Pedestrians using Mobile Sensing
Pizzamiglio, S., Naeem, U., ur Réhman, Shafiq, Sharif, M., Abdalla, H. and Turner, D. 2017. A Mutlimodal Approach to Measure the Levels Distraction of Pedestrians using Mobile Sensing. Procedia Computer Science. 113, pp. 89-96. https://doi.org/10.1016/j.procs.2017.08.297
Measuring energy footprint of software features
Islam, S., Noureddine, A. and Bashroush, Rabih 2016. Measuring energy footprint of software features. in: 2016 IEEE 24th International Conference on Program Comprehension (ICPC) IEEE.
PORBS: A parallel observation-based slicer
Islam, S. and Binkley, David 2016. PORBS: A parallel observation-based slicer. in: 2016 IEEE 24th International Conference on Program Comprehension (ICPC) IEEE.
Efficient Identification of Linchpin Vertices in Dependence Clusters
Binkley, David, Gold, Nicolas, Harman, Mark, Islam, S., Krinke, Jens and Li, Zheng 2013. Efficient Identification of Linchpin Vertices in Dependence Clusters. ACM Transactions on Programming Languages and Systems. 35 (2), pp. 1-35.
ORBS and the limits of static slicing
Binkley, David, Gold, Nicolas, Harman, Mark, Islam, S., Krinke, Jens and Yoo, Shin 2015. ORBS and the limits of static slicing. in: 2015 IEEE 15th International Working Conference on Source Code Analysis and Manipulation (SCAM) IEEE. pp. 1-10
Coherent clusters in source code
Islam, S., Krinke, Jens, Binkley, David and Harman, Mark 2013. Coherent clusters in source code. Journal of Systems and Software. 88, pp. 1-24.
Uncovering Dependence Clusters and Linchpin Functions
Binkley, David, Beszédes, Árpád, Islam, S., Jász, Judit and Vancsics, Béla 2015. Uncovering Dependence Clusters and Linchpin Functions. in: 2015 IEEE 31st International Conference on Software Maintenance and Evolution (ICSME) IEEE. pp. 141-150