Using semantic-based approach to manage perspectives of process mining: Application on improving learning process domain data

Book chapter


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
AuthorsKingsley, Okoye, Tawil, Abdel-Rahman H., Naeem, U., Islam, S. and Lamine, Elyes
Abstract

Mining useful knowledge from data readily available in today's information systems has been a common challenge in recent years as more and more events are being recorded, and there is need to improve and support many organisational processes in a competitive and rapidly changing environments. The work in this paper shows using a case study of Learning Process - how data from various process domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time processes. In so doing, it enables the prediction of individual patterns/behaviour through further semantic analysis of the discovered models. Our aim is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved process analysis of the captured data. The approach involves augmenting the informative value of the resulting model derived from mining event data about the process by semantically annotating the process elements with concepts they represent in real time using process descriptions languages, and linking them to an ontology specifically designed for representing learning processes to allow for the analysis of the extracted event logs based on concepts rather than the event tags of the process. The semantic analysis allows the meaning of the learning object properties and model to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are then used to determine useful learning patterns by means of the proposed Semantic Learning Process Mining (SLPM) formalization - described technically as Semantic-Fuzzy Miner. As a result, the approach provides us with the capability to infer new and discover hidden relationships/attributes the process instances share amongst themselves within the knowledge base, and the ability to identify and address the problem of determining the presence of different learning patterns or behaviour. Inference knowledge discovered due to semantic enrichment of the process model is advantageous especially in solving some didactic issues and answering some questions with regards to different Learners behaviour within the context of process mining and semantic model analysis. To this end, we show that information derived from process mining algorithms can be improved by adding semantic knowledge to the resulting model.

Keywordsevent logs; process model; process mining; semantic annotation; ontology; learning process
Book title2016 IEEE International Conference on Big Data (Big Data)
Page range3529-3538
Year2017
PublisherIEEE
Publication dates
Print06 Feb 2017
Publication process dates
Deposited11 May 2017
Accepted2016
EventInternational Conference on Big Data (Big Data) 2016 IEEE
ISBN978-1-4673-9005-7
978-1-4673-9006-4
Digital Object Identifier (DOI)doi:10.1109/BigData.2016.7841016
Additional information

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Accepted author manuscript
Permalink -

https://repository.uel.ac.uk/item/84x30

  • 14
    total views
  • 53
    total downloads
  • 2
    views this month
  • 3
    downloads this month

Related outputs

Tracking Functional Decline using Ambient Intelligence for Alzheimer's Patients
Lee, Sin Wee, Naeem, U., Anthony, Richard, Tawil, A., Azam, Muhammad Awais and Preston, David 2014. Tracking Functional Decline using Ambient Intelligence for Alzheimer's Patients. in: Dogru, Ali H. and Suh, Sang (ed.) Proceedings of the 19th International Conference on Transformative Science and Engineering, Business and Social Innovation Society for Design and Process Science. pp. 107-116
Inference of Activities with Unexpected Actions Using Pattern Mining
Nasreen, Shamila, Azam, Muhammad Awais, Naeem, U. and Ghazanfar, Mustansar Ali 2015. Inference of Activities with Unexpected Actions Using Pattern Mining. in: UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers ACM. pp. 1479-1488
Inference of Hygiene Behaviours While Recognising Activities of Daily Living
Naeem, U., Tawil, Abdel-Rahman, Semelis, Ivans, Judah, Gaby and Aunger, Robert 2015. Inference of Hygiene Behaviours While Recognising Activities of Daily Living. in: Mansoor, Wathiq, Maamar, Zakaria and Rabhi, Fethi (ed.) ICCASA '14: Proceedings of the 3rd International Conference on Context-Aware Systems and Applications ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). pp. 154-161
Semantic Process Mining Towards Discovery and Enhancement of Learning Model Analysis
Okoye, Kingsley, Tawil, Abdel Rahman H., Naeem, U. and Lamine, Elyes 2015. Semantic Process Mining Towards Discovery and Enhancement of Learning Model Analysis. in: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems Institute of Electrical and Electronics Engineers (IEEE). pp. 363-370
Recognition Framework for Inferring Activities of Daily Living Based on Pattern Mining
Nasreen, Shamila, Azam, Muhammad Awais, Naeem, U., Ghazanfar, Mustansar Ali and Khalid, Asra 2016. Recognition Framework for Inferring Activities of Daily Living Based on Pattern Mining. Arabian Journal for Science and Engineering. 41 (8), pp. 3113-3126.
Neural Predictors of Gait Stability When Walking Freely in the Real-World.
Pizzamiglio, S., Abdalla, H., Naeem, U. and Turner, D. 2018. Neural Predictors of Gait Stability When Walking Freely in the Real-World. Journal of NeuroEngineering and Rehabilitation. 15 (11).
Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network
Sharif, M., Naeem, U., Islam, S. and Karami, A. 2018. Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network. in: Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2 Springer, Cham.
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. in: Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 Springer, Cham.
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.
Identifying Smartphone Users based on their Activity Patterns via Mobile Sensing
Ehatisham-ul-Haq, M., Azam, Muhammad Awais, Naeem, U., Rѐhman, Shafiq Ur and Khalid, Asra 2017. Identifying Smartphone Users based on their Activity Patterns via Mobile Sensing. Procedia Computer Science. 113, pp. 202-209.
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 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 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 ACM. pp. 642-645
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.
Neural correlates of single- and dual-task walking in the real world
Pizzamiglio, Sara, Naeem, U., Abdalla, H. and Turner, D. 2017. Neural correlates of single- and dual-task walking in the real world. Frontiers in Human Neuroscience. 11, p. Art 460.
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) Institute of Electrical and Electronics Engineers (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) Institute of Electrical and Electronics Engineers (IEEE).
Recognizing activities of daily living from patterns and extraction of web knowledge
Ihianle, I., Naeem, U., Tawil, A. and Azam, Muhammad Awais 2016. Recognizing activities of daily living from patterns and extraction of web knowledge. in: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp '16 New York, NY, USA ACM. pp. 1255-1262
Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities within the Home
Naeem, U., Tawil, A., Semelis, Ivans, Azam, Muhammad Awais and Ghazanfar, Mustansar Ali 2017. Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities within the Home. in: Bi, Yaxin, Kapoor, Supriya and Bhatia, Rahul (ed.) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 Springer, Cham. pp. 969-986
Integration operators for generating RDF/OWL-based user defined mediator views in a grid environment
Tawil, Abdel-Rahman H., Taweel, Adel, Naeem, U., Montebello, Matthew, Bashroush, R. and Al-Nemrat, A. 2014. Integration operators for generating RDF/OWL-based user defined mediator views in a grid environment. Journal of Intelligent Information Systems. 43 (1), pp. 1-32.
A Semantic Reasoning Method Towards Ontological Model for Automated Learning Analysis
Okoye, Kingsley, Tawil, A., Naeem, U. and Lamine, Elyes 2015. A Semantic Reasoning Method Towards Ontological Model for Automated Learning Analysis. in: Pillay, Nelishia, Engelbrecht, Andries P., Abraham, Ajith, Plessis, Mathys C. du, Snášel, Václav and Muda, Azah Kamilah (ed.) Advances in Nature and Biologically Inspired Computing Cham, Switzerland Springer International Publishing.
A dynamic segmentation based activity discovery through topic modelling
Kennedy, Ihianle Isibor, Naeem, U. and Tawil, A. 2016. A dynamic segmentation based activity discovery through topic modelling. in: IET International Conference on Technologies for Active and Assisted Living (TechAAL) 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 ACM. pp. 93-102
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 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 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.
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.
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.
High-Frequency Intermuscular Coherence between Arm Muscles during Robot-Mediated Motor Adaptation
Pizzamiglio, Sara, De Lillo, Martina, Naeem, U., Abdalla, Hassan and Turner, D. 2017. High-Frequency Intermuscular Coherence between Arm Muscles during Robot-Mediated Motor Adaptation. Frontiers in Physiology. 7 (668), pp. 1-14.
Recognition of Activities of Daily Living from Topic Model
Ihianle, I., Naeem, U. and Tawil, A. 2016. Recognition of Activities of Daily Living from Topic Model. Procedia Computer Science. 98, pp. 24-31.
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
Activities of daily life recognition using process representation modelling to support intention analysis
Naeem, U., Bashroush, R., Anthony, Richard, Azam, Muhammad Awais, Tawil, Abdel Rahman, Lee, S. and Mou-Ling, Dennis 2015. Activities of daily life recognition using process representation modelling to support intention analysis. International Journal of Pervasive Computing and Communications. 11 (3), pp. 347-371.
Novel centroid selection approaches for KMeans-clustering based recommender systems
Zahra, Sobia, Ghazanfar, Mustansar Ali, Khalid, Asra, Azam, Muhammad Awais, Naeem, U. and Prugel-Bennett, Adam 2015. Novel centroid selection approaches for KMeans-clustering based recommender systems. Information Sciences. 320, pp. 156-189.
A Semantic Rule-based Approach Supported by Process Mining for Personalised Adaptive Learning
Okoye, Kingsley, Tawil, A., Naeem, U., Bashroush, Rabih and Lamine, Elyes 2014. A Semantic Rule-based Approach Supported by Process Mining for Personalised Adaptive Learning. Procedia Computer Science. 37, pp. 203-210.
Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey
Nasreen, Shamila, Azam, Muhammad Awais, Shehzad, Khurram, Naeem, U. and Ghazanfar, Mustansar Ali 2014. Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey. Procedia Computer Science. 37, pp. 109-116.
A Framework to Recognise Daily Life Activities with Wireless Proximity and Object Usage Data
Azam, M.A., Loo, J., Naeem, U., Khan, S.K.A., Lasebae, A. and Gemikonakli, O. 2012. A Framework to Recognise Daily Life Activities with Wireless Proximity and Object Usage Data. in: Proceedings of 23rd IEEE International Symposium on Personal, Indoor and Mobile Radio Communication 2012 IEEE. pp. 590-595
Behavioural Patterns Analysis of Low Entropy People Using Proximity Data
Azam, M.A., Loo, J., Khan, S.K.A., Naeem, U., Adeel, M. and Ejaz, Waleed 2012. Behavioural Patterns Analysis of Low Entropy People Using Proximity Data. Innovative Information Science & Technology Research Group.
A Comparison of Two Hidden Markov Approaches to Task Identification in the Home Environment
Naeem, U. and Bigham, John 2007. A Comparison of Two Hidden Markov Approaches to Task Identification in the Home Environment. Proceedings of the 2nd International Conference on Pervasive Computing and Applications, Birmingham, UK, 2007, pp. 383-388
Recognising Activities of Daily Life Using Hierarchical Plans
Naeem, U., Bigham, John and Wang, Jinfu 2007. Recognising Activities of Daily Life Using Hierarchical Plans. Proceedings of the 2nd European Conference on Smart Sensing and Context, LNCS 4793, Lake District, UK, 2007, pp. 175-189
A Hierarchal Approach to Activity Recognition in the Home Environment based on Object Usage
Naeem, U. and Bigham, John 2008. A Hierarchal Approach to Activity Recognition in the Home Environment based on Object Usage. Proceedings of the 2008 Networking and Electronic Commerce Research Conference (NAEC 2008), Lake Garda, Italy, 2008, pp. 48-54
Recognising Activities of Daily Life through the Usage of Everyday Objects around the Home
Naeem, U. and Bigham, John 2009. Recognising Activities of Daily Life through the Usage of Everyday Objects around the Home. Proceedings of the 3rd International Conference on Pervasive Computing Technologies for Healthcare. Technologies to Counter Cognitive Decline Workshop London
Activity Recognition using a Hierarchical Framework
Naeem, U. and Bigham, John 2008. Activity Recognition using a Hierarchical Framework. Proceedings of the 2nd International Conference on Pervasive Computing Technologies for Healthcare, Ambient Technologies for Diagnosing and Monitoring Chronic Patients Workshop, Tampere, Finland, 2008, IEEE pp. 24-27