Automated simulation and verification of process models discovered by process mining

This paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel\u27s Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair

Improved Visualization of Frequent Itemset Relationships Using the Minimal Spanning Tree Algorithm

Descriptive data mining techniques offer a way of extracting useful information out of large datasets and presenting it in an interpretable fashion to be used as a basis for future decisions. Since users interpret information most easily through visual means, techniques which produce concise, visually attractive results are usually preferred. We define a method, which converts transactional data into tree-like data structures, which depict important relationships between items contained in this data. The new approach we propose is offering a way to mitigate the loss of information present in previously developed algorithms, which use mined frequent itemsets and construct tree structures. We transfer the problem to the domain of graph theory and through minimal spanning tree construction achieve more informative visualizations. We highlight the new approach with comparison to previous ones by applying it on a real-life datasets – one connected to market basket data and the other from the educational domain

Automatic extraction of learning concepts from exam query repositories

Educational data mining (or EDM) is an emerging interdisciplinary research field concerned with developing methods for exploring the specific and diverse data encountered in the field of education. One of the most valuable data sources in the educational domain are exam query repositories, which are commonly pre-dating modern e-learning systems. Exam queries in those repositories usually lack additional metadata which helps establish relationships between questions and corresponding learning concepts whose adoption is being tested. In this paper we present our novel approach of using data mining methods able to automatically annotate pre-existing exam queries with information about learning concepts they relate to, leveraging both textual and visual information contained in the queries. This enables automatic categorization of exam queries which allows for both better insight into the usability of the current exam query corpus as well as easier reporting of learning concept adoption after these queries are used in exams. We apply this approach to real-life exam questions from a high education university course and show validation of our results performed in consultation with experts from the educational domain

Formal concept analysis and combinatorial testing for automated assessment in e-learning systems

Tema ove disertacije je istraživanje primjene metode formalne analize koncepata i tehnike kombinatornog testiranja za automatiziranje pripreme i odabira pitanja za provjere znanja u sustavima za e-učenje. Na temelju skupa ispitnih pitanja označenih s definiranim atributima gradi se primjenom metode za formalnu analizu koncepata formalni kontekst. Potom se automatiziranom metodom kombinatornog testiranja generira gotovo minimalan broj testnih slučajeva opisanih s definiranim atributima iz formalnog konteksta tako da je svaka n-torka atributa zadane veličine n pokrivena s barem jednim testnim slučajem odnosno opisom pitanja. Nakon što se pronađe sažeti skup pitanja koji pokriva sve generirane testne slučajeve prelazi se na izgradnju konceptualne rešetke primjenom metode formalne analize koncepata. Konceptualna rešetka predstavlja ontologiju dijela nastavnog gradiva opisanog s odabranim sažetim skupom ispitnih pitanja. Konceptualna rešetka se automatski topološki sortira kako bi se iz nje dobio odgovarajući potpuno uređeni skup formalnih koncepata, a iz njega se potom automatski izdvajaju prikladni nizovi pitanja za formativnu provjeru znanja u sustavu za e-učenje. Formativna provjera se provodi kroz vlastiti sustav za e-učenje, koji studente vodi po pripremljenim nizovima pitanja i pritom nudi pomoć u obliku interaktivnih nastavnih materijala. Izgrađen je i model za verifikaciju predložene metode uz pomoć nadograđenog i automatiziranog L* algoritma i metode provjere modela s alatom Spin. Izvorni doprinosi su: 1) formalni opis metode strojnog učenja za izgradnju ontologije domenskog znanja u obliku konceptualne rešetke na temelju sažetog skupa semantički označenih ispitnih pitanja, 2) metoda za automatiziranu provjeru znanja utemeljena na automatskom odabiru nizova ispitnih pitanja iz konceptualne rešetke i 3) prototip sustava za verifikaciju metode za automatiziranu provjeru znanja utemeljen na formalnoj metodi provjere modela.The topic of this dissertation is research on the application of Formal concept analysis method and combinatorial testing technique to automate preparation and selection of questions for assessments in e-learning systems. Based on the exam question set labelled with defined attributes a formal context is built using formal concept analysis method. Then, combinatorial testing method generates almost minimal number of test cases described with defined attributes from the formal context so that each tuple of attributes of a given size n is covered by at least one test case or a question description. After finding a concise set of questions that covers all generated test cases, we can build its conceptual lattice using the method of formal concept analysis, as an ontology of the teaching material described with a selected concise set of exam questions. The concept lattice is automatically topologically sorted to obtain a totally ordered set of formal concepts, and then appropriate sequences of questions for formative assessment are automatically extracted from it. Students can the formative assessment though our e-learning system which guides them over prepared question sequences and offers help in the form of interactive teaching materials. A model for verification of the proposed method was built using upgraded and automated L* algorithm and the model checking method with the Spin tool. Original scientific contribution of the dissertation: 1) formal description of a machine learning method for building domain knowledge ontology as a concept lattice based on a concise set of semantically labelled exam questions, 2) method for automated assessment based on automatic selection of exam questions sequences from a concept lattice and 3) prototype verification

Formal concept analysis and combinatorial testing for automated assessment in e-learning systems

Tema ove disertacije je istraživanje primjene metode formalne analize koncepata i tehnike kombinatornog testiranja za automatiziranje pripreme i odabira pitanja za provjere znanja u sustavima za e-učenje. Na temelju skupa ispitnih pitanja označenih s definiranim atributima gradi se primjenom metode za formalnu analizu koncepata formalni kontekst. Potom se automatiziranom metodom kombinatornog testiranja generira gotovo minimalan broj testnih slučajeva opisanih s definiranim atributima iz formalnog konteksta tako da je svaka n-torka atributa zadane veličine n pokrivena s barem jednim testnim slučajem odnosno opisom pitanja. Nakon što se pronađe sažeti skup pitanja koji pokriva sve generirane testne slučajeve prelazi se na izgradnju konceptualne rešetke primjenom metode formalne analize koncepata. Konceptualna rešetka predstavlja ontologiju dijela nastavnog gradiva opisanog s odabranim sažetim skupom ispitnih pitanja. Konceptualna rešetka se automatski topološki sortira kako bi se iz nje dobio odgovarajući potpuno uređeni skup formalnih koncepata, a iz njega se potom automatski izdvajaju prikladni nizovi pitanja za formativnu provjeru znanja u sustavu za e-učenje. Formativna provjera se provodi kroz vlastiti sustav za e-učenje, koji studente vodi po pripremljenim nizovima pitanja i pritom nudi pomoć u obliku interaktivnih nastavnih materijala. Izgrađen je i model za verifikaciju predložene metode uz pomoć nadograđenog i automatiziranog L* algoritma i metode provjere modela s alatom Spin. Izvorni doprinosi su: 1) formalni opis metode strojnog učenja za izgradnju ontologije domenskog znanja u obliku konceptualne rešetke na temelju sažetog skupa semantički označenih ispitnih pitanja, 2) metoda za automatiziranu provjeru znanja utemeljena na automatskom odabiru nizova ispitnih pitanja iz konceptualne rešetke i 3) prototip sustava za verifikaciju metode za automatiziranu provjeru znanja utemeljen na formalnoj metodi provjere modela.The topic of this dissertation is research on the application of Formal concept analysis method and combinatorial testing technique to automate preparation and selection of questions for assessments in e-learning systems. Based on the exam question set labelled with defined attributes a formal context is built using formal concept analysis method. Then, combinatorial testing method generates almost minimal number of test cases described with defined attributes from the formal context so that each tuple of attributes of a given size n is covered by at least one test case or a question description. After finding a concise set of questions that covers all generated test cases, we can build its conceptual lattice using the method of formal concept analysis, as an ontology of the teaching material described with a selected concise set of exam questions. The concept lattice is automatically topologically sorted to obtain a totally ordered set of formal concepts, and then appropriate sequences of questions for formative assessment are automatically extracted from it. Students can the formative assessment though our e-learning system which guides them over prepared question sequences and offers help in the form of interactive teaching materials. A model for verification of the proposed method was built using upgraded and automated L* algorithm and the model checking method with the Spin tool. Original scientific contribution of the dissertation: 1) formal description of a machine learning method for building domain knowledge ontology as a concept lattice based on a concise set of semantically labelled exam questions, 2) method for automated assessment based on automatic selection of exam questions sequences from a concept lattice and 3) prototype verification

Formal concept analysis and combinatorial testing for automated assessment in e-learning systems

Tema ove disertacije je istraživanje primjene metode formalne analize koncepata i tehnike kombinatornog testiranja za automatiziranje pripreme i odabira pitanja za provjere znanja u sustavima za e-učenje. Na temelju skupa ispitnih pitanja označenih s definiranim atributima gradi se primjenom metode za formalnu analizu koncepata formalni kontekst. Potom se automatiziranom metodom kombinatornog testiranja generira gotovo minimalan broj testnih slučajeva opisanih s definiranim atributima iz formalnog konteksta tako da je svaka n-torka atributa zadane veličine n pokrivena s barem jednim testnim slučajem odnosno opisom pitanja. Nakon što se pronađe sažeti skup pitanja koji pokriva sve generirane testne slučajeve prelazi se na izgradnju konceptualne rešetke primjenom metode formalne analize koncepata. Konceptualna rešetka predstavlja ontologiju dijela nastavnog gradiva opisanog s odabranim sažetim skupom ispitnih pitanja. Konceptualna rešetka se automatski topološki sortira kako bi se iz nje dobio odgovarajući potpuno uređeni skup formalnih koncepata, a iz njega se potom automatski izdvajaju prikladni nizovi pitanja za formativnu provjeru znanja u sustavu za e-učenje. Formativna provjera se provodi kroz vlastiti sustav za e-učenje, koji studente vodi po pripremljenim nizovima pitanja i pritom nudi pomoć u obliku interaktivnih nastavnih materijala. Izgrađen je i model za verifikaciju predložene metode uz pomoć nadograđenog i automatiziranog L* algoritma i metode provjere modela s alatom Spin. Izvorni doprinosi su: 1) formalni opis metode strojnog učenja za izgradnju ontologije domenskog znanja u obliku konceptualne rešetke na temelju sažetog skupa semantički označenih ispitnih pitanja, 2) metoda za automatiziranu provjeru znanja utemeljena na automatskom odabiru nizova ispitnih pitanja iz konceptualne rešetke i 3) prototip sustava za verifikaciju metode za automatiziranu provjeru znanja utemeljen na formalnoj metodi provjere modela.The topic of this dissertation is research on the application of Formal concept analysis method and combinatorial testing technique to automate preparation and selection of questions for assessments in e-learning systems. Based on the exam question set labelled with defined attributes a formal context is built using formal concept analysis method. Then, combinatorial testing method generates almost minimal number of test cases described with defined attributes from the formal context so that each tuple of attributes of a given size n is covered by at least one test case or a question description. After finding a concise set of questions that covers all generated test cases, we can build its conceptual lattice using the method of formal concept analysis, as an ontology of the teaching material described with a selected concise set of exam questions. The concept lattice is automatically topologically sorted to obtain a totally ordered set of formal concepts, and then appropriate sequences of questions for formative assessment are automatically extracted from it. Students can the formative assessment though our e-learning system which guides them over prepared question sequences and offers help in the form of interactive teaching materials. A model for verification of the proposed method was built using upgraded and automated L* algorithm and the model checking method with the Spin tool. Original scientific contribution of the dissertation: 1) formal description of a machine learning method for building domain knowledge ontology as a concept lattice based on a concise set of semantically labelled exam questions, 2) method for automated assessment based on automatic selection of exam questions sequences from a concept lattice and 3) prototype verification

Formal Concept Analysis – Overview and Applications

AbstractIn this article we give a brief overview of the theory behind the formal concept analysis, a novel method for data representation and analysis. From given tabular input data this method finds all formal concepts and computes a concept lattice, a directed, acyclic graph, in which all formal concepts are hierarchically ordered. We describe the link between this method and formal logic, as well as graph theory. Finally we present one example of an application of this method in the field of computer aided learning