Papers by Kingsley Okoye (PhD)
Discovery of worthwhile process models and effective data representation must be performed with d... more Discovery of worthwhile process models and effective data representation must be performed with due regard to the transformation that needs to be achieved, and the available data processing tools both at the pre-modelling and post-modelling stages. Indeed, such transformations should be aimed at turning data into real value. Presently, the field of process mining has been proven to provide valuable techniques that are used to improve real time processes by extracting knowledge from event logs readily available in many organisations information systems. Practically, there are two main drivers for such growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about history of processes as they happen in reality. On the other hand, there is need to improve and support business processes in a competitive and rapidly changing environment. Process mining means extracting valuable, process-related information from event logs about any real time process. Besides, process discovery has been lately seen as the most important and most visible intellectual challenge related to process mining. The arrangement involves automatic construction of process models from event log about any domain process, and describes causal dependencies between the various activities that are performed within the process base i.e, execution enviroment. In principle, one can use process discovery to obtain process models that describes reality. In view of that, the work in this paper presents a Fuzzy-BPMN mining approach that uses a training event log representing 10 different realtime business process executions to provide a method for discovery of useful process models, and then cross-validating the derived models with a set of test event logs in order to measure the performance of the employed discovery method. Our aim is on carrying out a classification task to determine the traces, ie. individual cases, that makes up the test event log to determine which traces that can be replayed by the original model. Thus, we focus on providing a model which is as good in balancing between overfitting and underfitting as it is able to correctly classify the traces that can be replayed (allowed) or non-replayable (disallowed) based on the analysis of the event logs and the discovered processs models. In other words, we show through a series of validation experiments, how given any classified trace (for the test event log) and discovered process model (the training log) in the deployed Fuzzy-BPMN replaying notation, it can be unambiguously determined whether or not the traces found can be replayed on the discovered model. In addition, we discuss the replaying semantics of the process modelling notation that has been employed, and also provide a description of the tools used to discover the process models as well as evaluation of the results of the classification task. Above all, the work looks at the sophistication of the proposed Fuzzy-BPMN mining approach, validation of the classification tasks, and the discovered process models. The outcome of the experimentations and data validation shows that the proposed mining approach has correctly classified 85.5% of the traces in the original process model.
Bookmarks Related papers MentionsView impact
Purpose: The purpose of this paper is to propose a system for automated learning directed towards... more Purpose: The purpose of this paper is to propose a system for automated learning directed towards discovering and enhancement of the set of recurrent behaviours that can be found within a learning knowledge base and how they can be modelled to enable a more effective reasoning and tactical strategies for personalized adaptation and decision making.
Design/Methodology/Approach: The paper introduces a methodology that makes use of information readily available within a learning process to explore and analyse the data to obtain inference knowledge capable of enhancing individual learning performance. The author valuated this approach through a user-centric design prototype and a model developed using Business Process Model Notations to corroborate the focus on improving performance of e-learning systems.
Findings: The technological response to satisfying the increasing demand for richer and more precise depiction of e-learning applications capable of providing platforms for pattern exploration where users can browse for knowledge they might consider as interesting; is by providing a personalized adaptive learning system for the users. Adoption of such technological developments will spark a great success for many learners and needed to provide continuous intelligent recommendation, guidance and feedbacks on learner’s performance especially in achieving the overall learning objective.
Research limitations/implications: To meet the overall needs of intended users, there is requirement for e-learning systems to embody technologies that support learners in achieving their learning goals and this process don’t happen automatically. Such process should take into account the fact that there is an additional task of matching these persons (user profiling) with solutions that best fits their particular learning needs (personalization).
Practical implications: The work in this paper considers the implication of the user centred design approach, and to this end, identify some of the common design problems with e-learning systems as means to revealing the implications for designers to stick to user-centric standards when developing automated learning systems in order to ensure learners satisfaction and reliability.
Originality/value: The approach and prototype described in this paper provides a more enhanced model for learning which is useful towards the development of e-learning systems that are more intelligent, predictive and robotically adaptive, which also aid in discovery of new learning patterns and enhancement of existing learning processes.
Bookmarks Related papers MentionsView impact
The Process Discovery approach described in this document is directed towards discovery of proces... more The Process Discovery approach described in this document is directed towards discovery of process models from a Training Event log representing 10 different real time business process executions, and cross-validating the derived model with a set of two Test Event logs provided for evaluation of the process discovery technique. Each of the Test event logs ((test_log_april_1 to test_log_april_10) and (test_log_may_1 to test_log_may_10)) represents part of the model from the Training Log with complete total of 20 traces for each of the logs, and are characterized by having 10 traces that can be replayed (allowed) and 10 traces that cannot be replayed (disallowed) by the model. The total number of traces for the Test event logs (i.e. April log, and May log) is therefore ((10 logs x 20 traces) x 2) = 400 Traces. Our aim is to carry out a classification task to determine the 400 individual traces that makes up the two test event log and then provide a Petri Net representation of the Training model as well as Business Process Model Notation (BPMN) mapping that allows for testing and evaluation of the behaviours/traces recorded in the Test logs. The objective of the proposed approach is to discover and provide process models that matches the original process models in term of balancing between " overfitting " and " underfitting ". A process model is seen as overfitting (the event log) if it is too restrictive, disallowing behaviour which is part of the underlying process. On the other hand, it is underfitting (the reality) if it is not restrictive enough, allowing behaviour which is not part of the underlying process. Following this challenge, we aim to provide a model which is as good in balancing " overfitting " and " underfitting " as it is able to correctly classify the traces that can be replayed based on the analysis of the " test " event log: Thus, Given a trace (t) representing real process behaviour, the process model (m) classifies it as allowed, or Given a trace (t) representing a behaviour not related to the process, the process model (m) classifies it as disallowed. This document contains the classification attempts for the events logs provided and discusses the replaying semantics of the process modelling notation that has been employed. In other words, we discuss how, given any process trace t (for the Test event Log) and process model m (for the training log) in the discovered Petri Net and BPMN replaying notation, it can be unambiguously determined whether or not trace t can be replayed on model (m). We also provide a description of the tools used to discover the process models as well as checking the result of the classification task. The approach we use to solve the process discovery contest is supported by some of the definitions and technique described in [1]
Bookmarks Related papers MentionsView impact
Semantic concepts can be layered on top of existing learner information asset to provide a more c... more Semantic concepts can be layered on top of existing learner information asset to provide a more conceptual analysis of real time processes capable of providing real world answers that are closer to human understanding. Challenges from current research shows that even though learning data are captured and modelled with acceptable performance to accurately reflect process executions, they are still limited for many process mining analysis because they lack the abstraction level required from real world perspectives. The work in this paper describes a Semantic Process Mining approach directed towards enriching streams of event data logs from a learning process using semantic descriptions that references concepts in an Ontology specifically designed for representing learning processes. The proposed approach involves the extraction of process history data from learning execution environments unfolding how we extract the input data necessary to be mapped unto the learning process logs, which is then followed by submitting the resulting eXtensible Event Streams-XES and Mining eXtensible Markup Language-MXML format to the process analytics environment for mining and further analysis. The consequence is a learning process model which we semantically annotate with concepts they represent in real time using semantic descriptions, and then linking them to an ontology to allow for analysis of the extracted event logs streams based on concepts rather than the event tags of the process. The aim is to provide real time knowledge about the learning process which are more intuitive and closer to human understanding. By referring to ontologies and piloting series of validation experiments, the approach provides us with the capability to infer new and discover relationships the process instances share amongst themselves and to address the problem of determining the presence of different learning patterns within the learning knowledge base. To this end, we demonstrate how data from learning process can be extracted, semantically prepared, and transformed into mining executable formats to enable prediction of individual learning patterns and outcomes through further semantic analysis of the discovered models. Therefore, our approach is grounded on Process Mining and Semantic Modelling Techniques.
Bookmarks Related papers MentionsView impact
Extraction, synthesizing and analyzing of different types of data described as learning component... more Extraction, synthesizing and analyzing of different types of data described as learning components; is indispensable to perform Learning Process mining. The work in this paper adopts process mining technique to propose a novel approach for automated learning that is capable of detecting changes in interaction patterns, and trends in behaviour of learners within a learning execution environment. We take into account the context of the learner in order to find the best possible way for them to learn intuitively in less time and still maintain performance. Our goal is to identify patterns that has effect on user performance, and then respond by making decisions based on adaptive rules centered on captured user profiles through semantic modelling and reasoning. We use the semantics of the captured processes and discovered patterns to create new knowledge for enhancement of existing processes in a learning model. The focus is on augmenting information values of the resulting model based on individual process instances. To ensure validity, we look at the degree at which the learning goal is met, and reliability means the consistency of developing a well-suited inference knowledge to draw conclusions based on improved learning process analysis.
Bookmarks Related papers MentionsView impact
In recent years, automated learning systems are
widely used for educational and training purposes... more In recent years, automated learning systems are
widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for automated learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their learning goals. This paper proposes a novel approach for automated learning that is capable of detecting changing trends in learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs
within the learning process in order to discover patterns
automatically by means of semantic reasoning. Therefore, our
proposed approach is grounded on Semantic modelling and
process mining techniques. To this end, it is possible to apply
effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to automated discovery of learning patterns or behaviour.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Drafts by Kingsley Okoye (PhD)
This review work investigates the available researches in relation to the proposed topic area and... more This review work investigates the available researches in relation to the proposed topic area and compares the studies based on the domain area, scope, tools, the scientific contribution of the papers and the results. The findings of the identified papers were summarized in order to point out potential confounding variables and flaws that might have been neglected. A critical analysis of the studies were done in an effort to rate the value of the stated results. The results of the investigated papers were summarized and were presented in a tabular format, and a conclusion as well as recommendation for future research was also provided.
Bookmarks Related papers MentionsView impact
Uploads
Papers by Kingsley Okoye (PhD)
Design/Methodology/Approach: The paper introduces a methodology that makes use of information readily available within a learning process to explore and analyse the data to obtain inference knowledge capable of enhancing individual learning performance. The author valuated this approach through a user-centric design prototype and a model developed using Business Process Model Notations to corroborate the focus on improving performance of e-learning systems.
Findings: The technological response to satisfying the increasing demand for richer and more precise depiction of e-learning applications capable of providing platforms for pattern exploration where users can browse for knowledge they might consider as interesting; is by providing a personalized adaptive learning system for the users. Adoption of such technological developments will spark a great success for many learners and needed to provide continuous intelligent recommendation, guidance and feedbacks on learner’s performance especially in achieving the overall learning objective.
Research limitations/implications: To meet the overall needs of intended users, there is requirement for e-learning systems to embody technologies that support learners in achieving their learning goals and this process don’t happen automatically. Such process should take into account the fact that there is an additional task of matching these persons (user profiling) with solutions that best fits their particular learning needs (personalization).
Practical implications: The work in this paper considers the implication of the user centred design approach, and to this end, identify some of the common design problems with e-learning systems as means to revealing the implications for designers to stick to user-centric standards when developing automated learning systems in order to ensure learners satisfaction and reliability.
Originality/value: The approach and prototype described in this paper provides a more enhanced model for learning which is useful towards the development of e-learning systems that are more intelligent, predictive and robotically adaptive, which also aid in discovery of new learning patterns and enhancement of existing learning processes.
widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for automated learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their learning goals. This paper proposes a novel approach for automated learning that is capable of detecting changing trends in learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs
within the learning process in order to discover patterns
automatically by means of semantic reasoning. Therefore, our
proposed approach is grounded on Semantic modelling and
process mining techniques. To this end, it is possible to apply
effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to automated discovery of learning patterns or behaviour.
Drafts by Kingsley Okoye (PhD)
Design/Methodology/Approach: The paper introduces a methodology that makes use of information readily available within a learning process to explore and analyse the data to obtain inference knowledge capable of enhancing individual learning performance. The author valuated this approach through a user-centric design prototype and a model developed using Business Process Model Notations to corroborate the focus on improving performance of e-learning systems.
Findings: The technological response to satisfying the increasing demand for richer and more precise depiction of e-learning applications capable of providing platforms for pattern exploration where users can browse for knowledge they might consider as interesting; is by providing a personalized adaptive learning system for the users. Adoption of such technological developments will spark a great success for many learners and needed to provide continuous intelligent recommendation, guidance and feedbacks on learner’s performance especially in achieving the overall learning objective.
Research limitations/implications: To meet the overall needs of intended users, there is requirement for e-learning systems to embody technologies that support learners in achieving their learning goals and this process don’t happen automatically. Such process should take into account the fact that there is an additional task of matching these persons (user profiling) with solutions that best fits their particular learning needs (personalization).
Practical implications: The work in this paper considers the implication of the user centred design approach, and to this end, identify some of the common design problems with e-learning systems as means to revealing the implications for designers to stick to user-centric standards when developing automated learning systems in order to ensure learners satisfaction and reliability.
Originality/value: The approach and prototype described in this paper provides a more enhanced model for learning which is useful towards the development of e-learning systems that are more intelligent, predictive and robotically adaptive, which also aid in discovery of new learning patterns and enhancement of existing learning processes.
widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for automated learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their learning goals. This paper proposes a novel approach for automated learning that is capable of detecting changing trends in learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs
within the learning process in order to discover patterns
automatically by means of semantic reasoning. Therefore, our
proposed approach is grounded on Semantic modelling and
process mining techniques. To this end, it is possible to apply
effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to automated discovery of learning patterns or behaviour.