Mining complete, precise and simple process models

Process discovery algorithms are generally used to discover the underlying process that has been followed to achieve an objective. In general, these algorithms do not take into account any domain knowledge to derive process models, allowing to apply them in a general manner. However, depending on the selected approach, a different kind of process models can be discovered, as each technique has its strengths and weaknesses, e.g., the expressiveness of the used notation. Hence, it is important to take into account the requirements of the domain when deciding which algorithm to use, as the correct assumptions can lead to richer process models. For instance, among the different domains of application of process mining we can identify several fields that share an interesting requirement about the discovered process models. In security audits, discovered processes have to fulfill strict requisites. This means that the process model should reproduce as much behavior as possible; otherwise some violations may go undetected (replay fitness). On the other hand, in order to avoid false positives, process models should reproduce only the recorded behavior (precision). Finally, process models should be easily readable to better detect deviations (simplicity). Another clear example concerns the educational domain, as in order to be of value for both teachers and learners, a discovered learning process should satisfy the aforementioned requirements. That is, to guarantee feasible and correct evaluations, teachers need to access to all the activities performed by learners, thereby the learning process should be able to reproduce as much behavior as possible (replay fitness). Furthermore, the learning process should focus on the recorded behavior seen in the event log (precision), i.e., show only what the students did, and not what they might have done, while being easily interpretable by the teachers (simplicity). One of the previous requirements is related to the readability of process models: simplicity. In process mining, one of the identified challenges is the appropriate visualization of process models, i.e., to present the results of process discovery in such a way that people actually gain insights about the process. Process models that are unnecessary complex can hinder the real behavior of the process rather than to provide an intuition of what is really happening in an organization. However, achieving a good level of readability is not always straightforward, for instance, due the used representation. Within the different approaches focused to reduce the complexity of a process model, the interest in this PhD Thesis relies on two techniques. On the one hand, to improve the readability of an already discovered process model through the inclusion of duplicate labels. On the other hand, the hierarchization of a process model, i.e., to provide a well known structure to the process model. However, regarding the latter, this technique requires to take into account domain knowledge, as different domains may rely on different requirements when improving the readability of the process model. In other words, in order to improve the interpretability and understandability of a process model, the hierarchization has to be driven by the domain. To sum up, concerning the aim of this PhD Thesis, we can identify two main topics of interest. On the one hand, we are interested in retrieving process models that reproduce as much behavior recorded in the log as possible, without introducing unseen behavior. On the other hand, we try to reduce the complexity of the mined models in order to improve their readability. Hence, the aim of this PhD Thesis is to discover process models considering replay fitness, precision and simplicity, while paying special attention in retrieving highly interpretable process models

Discovering learning processes using inductive miner: A case study with learning management systems (LMSs)

Resumen tomado de la publicaciónDescubriendo procesos de aprendizaje aplicando Inductive Miner: un estudio de caso en Learning Management Systems (LMSs). Antecedentes: en la minería de procesos con datos educativos se utilizan diferentes algoritmos para descubrir modelos, sobremanera el Alpha Miner, el Heuristic Miner y el Evolutionary Tree Miner. En este trabajo proponemos la implementación de un nuevo algoritmo en datos educativos, el denominado Inductive Miner. Método: hemos utilizado datos de interacción de 101 estudiantes universitarios en una asignatura de grado desarrollada en la plataforma Moodle 2.0. Una vez prepocesados se ha realizado la minería de procesos sobre 21.629 eventos para descubrir los modelos que generan los diferentes algoritmos y comparar sus medidas de ajuste, precisión, simplicidad y generalización. Resultados: en las pruebas realizadas en nuestro conjunto de datos el algoritmo Inductive Miner es el que obtiene mejores resultados, especialmente para el valor de ajuste, criterio de mayor relevancia en lo que respecta al descubrimiento de modelos. Además, cuando ponderamos con pesos las diferentes métricas seguimos obteniendo la mejor medida general con el Inductive Miner. Conclusiones: la implementación de Inductive Miner en datos educativos es una nueva aplicación que, además de obtener mejores resultados que otros algoritmos con nuestro conjunto de datos, proporciona modelos válidos e interpretables en términos educativos.Universidad de Oviedo. Biblioteca de Psicología; Plaza Feijoo, s/n.; 33003 Oviedo; Tel. +34985104146; Fax +34985104126; [email protected]

Supporting flexible processes through recommendations based on history

In today's fast changing business environment exible information systems are required to allow companies to rapidly adjust their business processes to changes in the environment. However, increasing exibility in large information system usually leads to less guidance for its users and consequently requires more experienced users. In order to allow for exible systems with a high degree of guidance, intelligent user assistance is required. In this paper we propose a recommendation service, which, when used in combination with exible information systems, can guide end users during process execution by giving recommendations on possible next steps. Recommendations are generated based on similar past process executions by considering the specific optimization goals. This paper also describes an implementation of the proposed recommendation service in the context of ProM and the declarative work ow management system DECLARE

An analysis of students’ behaviour in a Learning Management System through Process Mining

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe exponential growth and transformation of the Internet and information technology in recent years led to the development of several analytical tools. As is the case with process mining, it emerged to fulfill the need to extract and analyze information from event logs by representing it in the form of process models. Process mining is an acclaimed tool and proved crucial in several areas, from healthcare to manufacturing and finance. Nevertheless, and despite the crucial role of digital systems in supporting learning activities and generating large amounts of data about learning processes, limited research focused on process mining applied to the educational context. Therefore, the aim of this dissertation is to apply a process-oriented approach and demonstrate the applicability of process mining techniques to explore and analyze students’ behavior and interaction patterns, based on data collected from Moodle, the widely used Learning Management System. We cover definitions of process mining, education, and a detailed search of the existing literature on educational process mining during this work. Furthermore, the paper analyzes and discusses the findings of the study that combines process mining techniques, specifically process discovery implanted in the Disco tool, with cluster analysis. Through the application of these two techniques, it was possible to recognize the relationship between the students’ behavior registered in the process models and the success of the students in the course, along with the general and specific information about the students’ learning paths. Besides, we obtained findings that allow us to predict the group of students at risk of failing. Finally, with the analysis of these results, we were able to provide improvement proposals and recommendations to enhance the learning experience

A Web-based Framework for the Evaluation of Predictive Process Monitoring Techniques

Äriprotsesside juhtimine keskendub ettevõtte siseste tegevuste optimeerimisele peamiste tulemuslikkuse näitajate suhtes. Protsesside jälgimine on üks äriprotsesside juhtimise osadest, mille eesmärgiks on teha kindlaks, et peamiste tulemuslikkuse näitajate nõuded oleksid täidetud. Ennustuslik protsesside jälgimine (EPJ) on uus protsesside jälgimise tüüp, mis tuleneb onlain protsesside jälgimise tehnikast. EPJ kasutab andmeid, mis kirjeldavad varasemalt toimunud äriprotsesse selleks, et konstrueerida masinõppe meetodite abil ennustatavat mudelit. Treenitud mudelit rakendatakse reaalajas toimuvate protsesside voole, selleks et ennustada protsesside käitumist.EPJ tüüpilisteks ülesanneteks on ennustada, kas antud äriprotsess lõpeb õigeks ajaks või mitte või milline on järgmine ülesanne, mida hakatakse protsessi raames täitma. Kuigi nende probleemide lahendamiseks on olemas vabavaralisi tarkvara lahendusi, tavaliselt nad keskenduvad ainult ühele eelmainitud probleemidest. Lisaks, sellised lahendused keskenduvad kogenud kasutajatele ja tarbivad palju riistvara ressursse simulatsioonide jooksutamiseks.Antud töö kirjeldab autori poolt loodud veebirakendust, mis lubab erineva kogemustasemega kasutajatel treenida, valideerida ja võrrelda treenitud mudeleid ning võimaldab ka mitme tulemuslikkuse näitaja ennustamist, kasutades erinevaid EPJ tehnikaid, millest räägitakse täpsemalt 'seotud töö' peatükis. Rakendus jooksutab kõiki simulatsioone serveris, seega ei pea kasutaja omama võimsat riistvara simulatsioonide jooksutamiseks.Lisaks, autor võrdleb oma poolt loodud rakendust juba olemasolevate rakendustega ning toob esile nendevahelisi erinevusi.Business process management (BPM) focuses on optimizations of various activities within the organization, with respect to key performance indicators (KPI). An important task among BPM-related activities is process monitoring which aims to make sure that business processes comply with KPIs.Process monitoring can be performed either offline, using historical data to analyze process execution in the past or online, i.e. analyzing event streams in real-time to identify the problems as soon as they arise. Predictive monitoring is an emerging type of online process monitoring that uses historical data to construct a predictive model using various machine learning methods and then applies this model to a live event stream in order to predict the future performance of ongoing process cases. Various techniques have been proposed to address typical predictive monitoring problems, such as whether this ongoing case will finish on time or what activity will be executed next in the case.Even though many of these techniques have publicly available software implementations, they typically target one specific predictive monitoring problem. Furthermore, due to variations in evaluation procedures (different data splits, different evaluation metrics reported, etc.), users do not have a readily available way to compare predictive accuracy across multiple techniques.Finally, such solutions are targeting experienced users and also consume a lot of users hardware resources to run the simulations. In this thesis, we have built a web application that allows users with various degrees of expertise in the subject to train, validate and compare models to predict multiple KPIs, using a wide range of predictive monitoring techniques proposed in related work.Moreover, the models can be exported for further use. This application runs all of the computations on the server side, thus eliminating the need for the powerful hardware to construct the models. We compare our solution with existing implementations and highlight clear distinctions and differences

Anàlisi pedagògic de les possibilitats de l'aplicació de la tecnologia Business Intelligence a l'entorn virtuals d'aprenentatge Moodle

El present document es tracta de la memòria del Treball Final de Màster dels estudis de Màster en Formació al Professorat de Tecnologia a la Facultat d'Informàtica de Barcelona (FIB) de la Universitat Politècnica de Catalunya (UPC). Són moltes les oportunitats que hi ha darrere del tractament de les dades. Cada cop més es gener més quantitat i s'analitzen de millor manera ja que en elles es pot aprendre més ràpidament del procés que es tracta, ja sigui a nivell empresarial o a nivell, com és aquest cas, educatiu. Es vol apropar la tecnologia de l'anàlisi de dades, sobretot la tècnica de monitorització de dades, al centre educatiu, ja que són moltes les possibilitats que hi apareixen, des de la pròpia informació de l'alumne per tenir més autoconeixement del seu progrés com del centre amb els seus docents o el seguiment del curs per part dels professors. Un dels aspectes més importants és poder analitzar de manera individualitzada les dades que ens ofereixen els entorns virtuals d'aprenentatge de cada alumne per tal de poder extreure un coneixement encarat a prendre determinades decisions

Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4

Fault Management in Distributed Systems

In the past decade, distributed systems have rapidly evolved, from simple client/server applications in local area networks, to Internet-scale peer-to-peer networks and large-scale cloud platforms deployed on tens of thousands of nodes across multiple administrative domains and geographical areas. Despite of the growing popularity and interests, designing and implementing distributed systems remains challenging, due to their ever- increasing scales and the complexity and unpredictability of the system executions. Fault management strengthens the robustness and security of distributed systems, by detecting malfunctions or violations of desired properties, diagnosing the root causes and maintaining verifiable evidences to demonstrate the diagnosis results. While its importance is well recognized, fault management in distributed systems, on the other hand, is notoriously difficult. To address the problem, various mechanisms and systems have been proposed in the past few years. In this report, we present a survey of these mechanisms and systems, and taxonomize them according to the techniques adopted and their application domains. Based on four representative systems (Pip, Friday, PeerReview and TrInc), we discuss various aspects of fault management, including fault detection, fault diagnosis and evidence generation. Their strength, limitation and application domains are evaluated and compared in detail

A proactive fault tolerance framework for high performance computing (HPC) systems in the cloud

High Performance Computing (HPC) systems have been widely used by scientists and researchers in both industry and university laboratories to solve advanced computation problems. Most advanced computation problems are either data-intensive or computation-intensive. They may take hours, days or even weeks to complete execution. For example, some of the traditional HPC systems computations run on 100,000 processors for weeks. Consequently traditional HPC systems often require huge capital investments. As a result, scientists and researchers sometimes have to wait in long queues to access shared, expensive HPC systems. Cloud computing, on the other hand, offers new computing paradigms, capacity, and flexible solutions for both business and HPC applications. Some of the computation-intensive applications that are usually executed in traditional HPC systems can now be executed in the cloud. Cloud computing price model eliminates huge capital investments. However, even for cloud-based HPC systems, fault tolerance is still an issue of growing concern. The large number of virtual machines and electronic components, as well as software complexity and overall system reliability, availability and serviceability (RAS), are factors with which HPC systems in the cloud must contend. The reactive fault tolerance approach of checkpoint/restart, which is commonly used in HPC systems, does not scale well in the cloud due to resource sharing and distributed systems networks. Hence, the need for reliable fault tolerant HPC systems is even greater in a cloud environment. In this thesis we present a proactive fault tolerance approach to HPC systems in the cloud to reduce the wall-clock execution time, as well as dollar cost, in the presence of hardware failure. We have developed a generic fault tolerance algorithm for HPC systems in the cloud. We have further developed a cost model for executing computation-intensive applications on HPC systems in the cloud. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in the cloud can be considerably reduced compared to checkpoint and redundancy techniques used in traditional HPC systems