While agile development is becoming ever more popular, studies have shown that few organisations successfully transition from traditional to agile practices. One such study showed that large organisations can benefit greatly from agile methods, but evaluating agile maturity and tailoring the method to the organisation’s needs is crucial. An agile maturity model is a tool with which an organisation’s practices and their conformance to agile development is evaluated. The purpose of this study is to discover the best suited agile maturity model for large organisations and to minimise costs, resources and the subjectivity of the model’s evaluation. In this study we take a closer look at four agile maturity models, the Scaled Agile Framework Maturity Model (SAFeMM) by Turetken, Stojanov and Trienekens (2017), the Scaled Agile Maturity Model (SAMM) by Chandrasekaran (2016), the Agile Adoption Framework (AAF) by Sidky, Arthur and Bohner (2007) and the Scaled Agile Framework Business Agility Assessment (SAFeBAA) by the Scaled Agile Incorporation. By evaluating each model on their scalability, completeness, generality, precision, simplicity, usability and meaningfulness, consistency, minimum overlapping, balance and proportion of automatable measurements, the best model is chosen. Based on the evaluation criteria for the maturity models, the SAFeMM is deemed the most suitable model. It proves to be a comprehensive, well-rounded tool with persistent high scores in all criteria. In order to improve the model’s objectivity and resource needs, it is also applied in a case study at the Swedish Tax Agency, where the possibilities to automate the model are investigated. The results show that the SAFeMM can be automated to roughly 50%, with the use of process mining and software system querying. Process mining uses event logs to extract and analyse information, while software querying extracts information directly from the software systems used in an organisation. The study suggests primary sources for querying and process mining techniques and perspectives to enable and encourage future research in the area of process mining within agile development. / Agil utveckling är en mycket populär utvecklingsmetod, samtidigt visar studier att få stora organisationer lyckas med övergången från traditionella metoder direkt. Som hjälpmedel kan dessa organisationer använda så kallade agila mognadsmodeller. En agil mognadsmodell är ett verktyg som mäter hur väl en organisation och dess processer överensstämmer med agila principer. Syftet med denna studie är att undersöka vilken agil mognadsmodell som är bäst lämpad för stora organisationer och kan samtidigt minimera kostnader, resurser och subjektiviteten i mätningarna. Därför tittar denna studie på fyra agila mognadsmodeller, Scaled Agile Framework Maturity Model (SAFeMM) av Turetken, Stojanov och Trienekens (2017), Scaled Agile Maturity Model (SAMM) av Chandrasekaran (2016), Agile Adoption Framework (AAF) av Sidky, Arthur och Bohner (2007) och Scaled Agile Framework Business Agility Assessment (SAFeBAA) av Scaled Agile Incorporation. Genom att utvärdera varje modell baserat på dess skalbarhet, helhetsbild, generaliserbarhet, precision, enkelhet, användbar-het och meningfullhet, kontinuitet, minimal överlappning, balans och andel automatiserbara mätvärden, bestäms vilken modell som är bäst. Resultaten visar, att baserat på de ovannämnda kriterierna, är SAFeMM modellen den bäst lämpade för stora organisationer. Den visade sig vara särsilkt helhetstäckande, enkel att förstå och använda, med höga poäng på de flesta kriterierna. För att förbättra modellens objektivitet och resurskrav, gjordes även en fallstudie där modellen applicerades på Skatteverkets IT avdelning. Där undersöktes möjligheterna för att automatisera modellen. Resultaten visar att knappt 50% av modellen är automatiserbar genom metoder såsom process mining och software querying. Process mining, använder event loggar från mjukvarusystem för att analysera och utvinna information, medan software querying utvinnar information direkt från mjukvarusystemen. Studien presenterar förslag på utvinningskällor och process mining tekniker och metoder för sammanhanget.

Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions, which is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they do not aim at providing efficient solutions; or (iii) the discovered process models are difficult to read due to the number of assignment conditions included. In this paper we address these problems and develop an efficient and effective process mining framework that provides extensive support for the discovery of patterns related to resource assignment. The framework is validated in terms of performance and applicability.

O processo de tratamento de incidentes é o mais adotado pelas empresas, porém, ainda carece de técnicas que possam gerar estimativas assertivas para o tempo de conclusão. Este trabalho atua no estudo de um processo real, por meio de um procedimento de mineração de processos, capaz de descobrir o modelo do processo sob a forma de um sistema de transição anotado e propõe meios automatizados de escolha dos atributos que o descrevam adequadamente, de modo a gerar estimativas realistas sobre o tempo necessário para sua conclusão. A estratégia resultante da aplicação de técnicas de seleção de atributos - filtro e invólucro - é capaz de propiciar a geração de sistemas de transição anotados mais precisos e com algum grau de generalização. A solução apresentada neste trabalho representa uma melhoria na mineração de processos, no contexto específico da criação de sistemas de transição anotados e no seu uso como um gerador de estatísticas para o processo nele modelado / The incident management process is the most widely adopted by companies. However, still lacks techniques that can generate precise estimates for the completion time. This work performs a study in a real incident management process, by means of process mining, able to find out the real process model in the form of annotated transition system and propose automated means for selecting attributes that describe it accordingly, in order to generate realistic estimates of the time to conclusion. The resulting strategy of application feature selection techniques - filter and wrapper - is able to provide generation of more accurate annotated transition systems with some degree of generalization. The solution presented in this paper represents an improvement in process mining on the specific context of creation annotated transition system and its use as a statistics generator for the whole modeled process

O processo de tratamento de incidentes é o mais adotado pelas empresas, porém, ainda carece de técnicas que possam gerar estimativas assertivas para o tempo de conclusão. Este trabalho atua no estudo de um processo real, por meio de um procedimento de mineração de processos, capaz de descobrir o modelo do processo sob a forma de um sistema de transição anotado e propõe meios automatizados de escolha dos atributos que o descrevam adequadamente, de modo a gerar estimativas realistas sobre o tempo necessário para sua conclusão. A estratégia resultante da aplicação de técnicas de seleção de atributos - filtro e invólucro - é capaz de propiciar a geração de sistemas de transição anotados mais precisos e com algum grau de generalização. A solução apresentada neste trabalho representa uma melhoria na mineração de processos, no contexto específico da criação de sistemas de transição anotados e no seu uso como um gerador de estatísticas para o processo nele modelado / The incident management process is the most widely adopted by companies. However, still lacks techniques that can generate precise estimates for the completion time. This work performs a study in a real incident management process, by means of process mining, able to find out the real process model in the form of annotated transition system and propose automated means for selecting attributes that describe it accordingly, in order to generate realistic estimates of the time to conclusion. The resulting strategy of application feature selection techniques - filter and wrapper - is able to provide generation of more accurate annotated transition systems with some degree of generalization. The solution presented in this paper represents an improvement in process mining on the specific context of creation annotated transition system and its use as a statistics generator for the whole modeled process

Web services have been developed as an attractive paradigm for publishing, discovering and consuming services. They are loosely-coupled applications that can be run alone or be composed to create new value-added services. They can be consumed as individual services which provide a unique interface to receive inputs and return outputs; or they can be consumed as components to be integrated into business processes. We call the first consumption case individual use and the second case business process use. The requirement of specific tools to assist consumers in the two service consumption cases involves many researches in both academics and industry. On the one hand, many service portals and service crawlers have been developed as specific tools to assist users to search and invoke Web services for individual use. However, current approaches take mainly into account explicit knowledge presented by service descriptions. They make recommendations without considering data that reflect user interest and may require additional information from users. On the other hand, some business process mechanisms to search for similar business process models or to use reference models have been developed. These mechanisms are used to assist process analysts to facilitate business process design. However, they are labor-intense, error-prone, time-consuming, and may make business analyst confused. In our work, we aim at facilitating the service consumption for individual use and business process use using recommendation techniques. We target to recommend users services that are close to their interest and to recommend business analysts services that are relevant to an ongoing designed business process. To recommend services for individual use, we take into account the user's usage data which reflect the user's interest. We apply well-known collaborative filtering techniques which are developed for making recommendations. We propose five algorithms and develop a web-based application that allows users to use services. To recommend services for business process use, we take into account the relations between services in business processes. We target to recommend relevant services to selected positions in a business process. We define the neighborhood context of a service. We make recommendations based on the neighborhood context matching. Besides, we develop a query language to allow business analysts to formally express constraints to filter services. We also propose an approach to extract the service's neighborhood context from business process logs. Finally, we develop three applications to validate our approach. We perform experiments on the data collected by our applications and on two large public datasets. Experimental results show that our approach is feasible, accurate and has good performance in real use-cases

The volume of data related to business process execution is increasing significantly in the enterprise. Many of data sources include events related to the execution of the same processes in various systems or applications. Event correlation is the task of analyzing a repository of event logs in order to find out the set of events that belong to the same business process execution instance. This is a key step in the discovery of business processes from event execution logs. Event correlation is a computationally-intensive task in the sense that it requires a deep analysis of very large and growing repositories of event logs, and exploration of various possible relationships among the events. In this dissertation, we present a scalable data analysis technique to support efficient event correlation for mining business processes. We propose a two-stages approach to compute correlation conditions and their entailed process instances from event logs using MapReduce framework. The experimental results show that the algorithm scales well to large datasets.

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, new techniques for discovering workflow models have been required. Starting point for such techniques are so-called &ldquo / workflow logs&quot / containing information about the workflow process as it is actually being executed. In this thesis, new mining technique based on time information is proposed. It is assumed that events in workflow logs bear timestamps. This information is used in to determine task orders and control flows between tasks. With this new algorithm, basic workflow structures, sequential, parallel, alternative and iterative (i.e., loops) routing, and advance workflow structure or-join can be mined. While mining the workflow structures, this algorithm also handles the noise problem.

Submitted by Jessica Andrade (jessicastefanysa@gmail.com) on 2018-06-25T18:21:40Z No. of bitstreams: 1 THALES.pdf: 4975032 bytes, checksum: cba8edebb4cead2492a1b616ef85e60f (MD5) / Rejected by Margareth Ferreira Pinto (margarethfpinto@hotmail.com), reason: Falta título em inglês.Palavra errada na citação. Nº de folhas erradas, (ver ficha catalográfica). on 2018-06-26T14:22:31Z (GMT) / Submitted by Jessica Andrade (jessicastefanysa@gmail.com) on 2018-06-28T17:17:31Z No. of bitstreams: 1 THALES.pdf: 4975032 bytes, checksum: cba8edebb4cead2492a1b616ef85e60f (MD5) / Approved for entry into archive by Margareth Ferreira Pinto (margarethfpinto@hotmail.com) on 2018-07-16T21:13:28Z (GMT) No. of bitstreams: 1 THALES.pdf: 4975032 bytes, checksum: cba8edebb4cead2492a1b616ef85e60f (MD5) / Made available in DSpace on 2018-07-16T21:13:28Z (GMT). No. of bitstreams: 1 THALES.pdf: 4975032 bytes, checksum: cba8edebb4cead2492a1b616ef85e60f (MD5) Previous issue date: 2016 / Ha muito se trata da necessidade de melhoramento na competitividade dos estaleiros da industria brasileira de construção naval em relação a concorrentes no ambito da industria internacional. Em grande parte, a baixa eficiência verificada neste setor da industria se da pela baixa priorização do emprego tecnologico para metodologias automatizadas para controle e diagnostico de processos de construção, por exemplo. Neste contexto, a mineração de processos vem sendo consolidada como soluções para descoberta de modelos, analise de conformidade e melhoramento de processos. Contudo, estas atividades nao são triviais, tendo como principal problematica a qualidade dos dados contidos nos logs de eventos. Este trabalho propoe uma metodologia para melhoramento de qualidade em logs de eventos originalmente caracterizados pela baixa granularidade das atividades nos aspectos quantitativos e temporal atraves do emprego de distribuições de probabilidades com a implementação de um novo software capaz de sintetizar um novo log de eventos, entao livre de tais problematicas de qualidade. Foi realizado um estudo de caso em estaleiro da industria brasileira, onde foram possibilitados experimentos de descoberta de modelos de processos com algoritmos livres e proprietarios, bem como a utilização de uma ferramenta de animação para detecção de gargalos no processo. Estes testes foram realizados com base no log de eventos original, provido pelo estaleiro e tambem sobre o log de eventos sintetico, gerado pelo software de simulação, para fins de validação da abordagem. Os resultados mostraram sucesso ao revelar a fragmentação oculta das atividades, possibilitando a descoberta de modelos fidedignos e abrindo precedente a trabalhos futuros. / The need for improvement in the competitiveness on the Brazilian shipbuilding in- dustry’s shipyards, in relation to its competitors from abroad, is not a novice issue. The low efficiency that can be verified in this section of the national industry is greatly caused by the low prioritization of technological usage in automated methodologies for control- ling and diagnosing the aseembly process, for example. In that context, process mining has been consolidated as the solution for discovering models, conformance analysis and enhancement of business processes. However, such activities are far from trivial oftenly facing log event data quality issues. This work proposes a novice methodology for the improvement of data quality in event logs that are originaly described as ungranular in the quantitative and temporal aspects, by using probability distributions with a new soft- ware implementation that is capable of synthesising a new event log, which is then free of such quality problematics. A case study has been performed in a shipyard from whithin the Brazilian industry, where various process discovery experiments have been executed with both free and proprietary algorithms. Also, a process model animation tool has been applied for bottleneck detection purposes. Such experiments were conducted based on he original event log that was provided by the shipyard’s administration office and also on the event log that has been generated by the simulation software, for validating the pre- sented approach. The results showed success in revealing the hidden fragmentation in the activities, enabling the discovery of trustworthy process models and opening precedents for future work.

Process mining aims at discovering processes by extracting knowledge about their different perspectives from event logs. The resource perspective (or organisational perspective) deals, among others, with the assignment of resources to process activities. Mining in relation to this perspective aims to extract rules on resource assignments for the process activities. Prior research in this area is limited by the assumption that only one resource is responsible for each process activity, and hence, collaborative activities are disregarded. In this paper, we leverage this assumption by developing a process mining approach that is able to discover team compositions for collaborative process activities from event logs. We evaluate our novel mining approach in terms of computational performance and practical applicability.

Machine learning and process mining are two techniques that are becoming more and more popular among organisations for business intelligence purposes. Results from these techniques can be very useful for organisations' decision-making. The Swedish National Forensic Centre (NFC), an organisation that performs forensic analyses, is in need of a way to visualise and understand its administration process. In addition, the organisation would like to be able to predict the time analyses will take to perform. In this project, it was evaluated if machine learning and process mining could be used on NFC's administration process-related data to satisfy the organisation's needs. Using the process mining tool Mehrwerk Process Mining implemented in the software Qlik Sense, different process variants were discovered from the data and visualised in a comprehensible way. The process variants were easy to interpret and useful for NFC. Machine learning regression models were trained on the data to predict analysis length. Two different datasets were tried, a large dataset with few features and a smaller dataset with more features. The models were then evaluated on test datasets. The models did not predict the length of analyses in an acceptable way. A reason to this could be that the information in the data was not sufficient for this prediction.