Greedy Approach to Compute Alignments of Process Models and Event Logs

openIn Process Mining, computing alignments is a conformance-checking technique to compare a process model with an event log of the same process to pinpoint difference between how the model would prescribe the process to be executed, and how the event log states the process has been executed. The complexity of this problem is naturally exponential with respect to the size of the model, and benefits can be achieved using divide-and-conquer approaches: the model is decomposed into small fragment for which we can compute alignments. This thesis compares the time to compute alignments using the traditional approaches and our decomposition-based approaches to identify the possible benefits. The results are also compared with different approaches based on process-model decompositions.In Process Mining, computing alignments is a conformance-checking technique to compare a process model with an event log of the same process to pinpoint difference between how the model would prescribe the process to be executed, and how the event log states the process has been executed. The complexity of this problem is naturally exponential with respect to the size of the model, and benefits can be achieved using divide-and-conquer approaches: the model is decomposed into small fragment for which we can compute alignments. This thesis compares the time to compute alignments using the traditional approaches and our decomposition-based approaches to identify the possible benefits. The results are also compared with different approaches based on process-model decompositions

Conformance Checking of Large Process Model: An Approach based on Decomposition

Conformance checking is the problem to pinpoint deviations between how processes are executed in reality and how processes are expected to be performed according to norms, regulations and protocols. The executions are recorded in event logs, while the expected behaviors are encoded in a process model. The complexity of the problem is exponential with respect to the size of the model, this makes the problem not scale when models become very large. To keep the problem tractable, one can decompose the model into parts for which conformance checking is carried out

Decomposed process discovery and conformance checking

Decomposed process discovery and decomposed conformance checking are the corresponding variants of the two monolithic fundamental problems in process mining (van der Aalst 2011): automated process discovery, which considers the problem of discovering a process model from an event log (Leemans 2009), and conformance checking, which addresses the problem of analyzing the adequacy of a process model with respect to observed behavior (Munoz-Gama 2009), respectively. The term decomposed in the two definitions is mainly describing the way the two problems are tackled operationally, to face their computational complexity by splitting the initial problem into smaller problems, that can be solved individually and often more efficiently.Postprint (author's final draft

Alignment-based process model repair and its application to the evolutionary tree miner

Process mining is an emerging research discipline that provides techniques that can be used to discover, monitor and improve real processes using event data. In this thesis we present several approaches and extensions that improve the effectiveness of the Evolutionary Tree Miner, a genetic process mining algorithm. These approaches and extensions enable the Evolutionary Tree Miner to make smart changes to process models, in order to obtain models of a higher quality in less time than the original implementation, while taking into account the four process model quality dimensions of replay fitness, simplicity, precision and generalisation. The approaches and extensions are based on concepts and ideas from process model repair, which have been applied in the context of the Evolutionary Tree Miner. We show, through experiments on both artificial and randomly generated event logs, that our approach is superior to the original implementation of the Evolutionary Tree Miner in its ability to quickly produce high quality model. Process mining is an emerging research discipline that provides techniques that can be used to discover, monitor and improve real processes using event data. In this thesis we present several approaches and extensions that improve the effectiveness of the Evolutionary Tree Miner, a genetic process mining algorithm. These approaches and extensions enable the Evolutionary Tree Miner to make smart changes to process models, in order to obtain models of a higher quality in less time than the original implementation, while taking into account the four process model quality dimensions of replay fitness, simplicity, precision and generalisation. The approaches and extensions are based on concepts and ideas from process model repair, which have been applied in the context of the Evolutionary Tree Miner. We show, through experiments on both artificial and randomly generated event logs, that our approach is superior to the original implementation of the Evolutionary Tree Miner in its ability to quickly produce high quality model

Alignment-based conformance checking if hierarchical process models

Process mining has received much attention in the field of business pro cess management. Event logs that are generated from information systems can be correlated with the process models for conformance checking. The process models describe event activities at an abstraction level. However, hierarchical business pro cesses, as a kind of typical complex process scenario, describe sub-processes invoca tion and multi-instantiation patterns. As existing conformance checking approaches cannot identify sub-processes within hierarchical process models. They cannot be used for conformance checking of hierarchical process models. To handle this limi tation, a definition of hierarchically alignment sequences is presented in this paper. Meanwhile, a novel conformance checking approach for hierarchical process models and event logs is proposed. The proposed method has been implemented within the ProM toolkit, which is an open-source process mining software. To evaluate the effectiveness of the proposed approach, both artificial and real-world event logs are utilized in a comparative analysis against existing state-of-the-art approaches

Conformance checking and diagnosis in process mining

In the last decades, the capability of information systems to generate and record overwhelming amounts of event data has experimented an exponential growth in several domains, and in particular in industrial scenarios. Devices connected to the internet (internet of things), social interaction, mobile computing, and cloud computing provide new sources of event data and this trend will continue in the next decades. The omnipresence of large amounts of event data stored in logs is an important enabler for process mining, a novel discipline for addressing challenges related to business process management, process modeling, and business intelligence. Process mining techniques can be used to discover, analyze and improve real processes, by extracting models from observed behavior. The capability of these models to represent the reality determines the quality of the results obtained from them, conditioning its usefulness. Conformance checking is the aim of this thesis, where modeled and observed behavior are analyzed to determine if a model defines a faithful representation of the behavior observed a the log. Most of the efforts in conformance checking have focused on measuring and ensuring that models capture all the behavior in the log, i.e., fitness. Other properties, such as ensuring a precise model (not including unnecessary behavior) have been disregarded. The first part of the thesis focuses on analyzing and measuring the precision dimension of conformance, where models describing precisely the reality are preferred to overly general models. The thesis includes a novel technique based on detecting escaping arcs, i.e., points where the modeled behavior deviates from the one reflected in log. The detected escaping arcs are used to determine, in terms of a metric, the precision between log and model, and to locate possible actuation points in order to achieve a more precise model. The thesis also presents a confidence interval on the provided precision metric, and a multi-factor measure to assess the severity of the detected imprecisions. Checking conformance can be time consuming for real-life scenarios, and understanding the reasons behind the conformance mismatches can be an effort-demanding task. The second part of the thesis changes the focus from the precision dimension to the fitness dimension, and proposes the use of decomposed techniques in order to aid in checking and diagnosing fitness. The proposed approach is based on decomposing the model into single entry single exit components. The resulting fragments represent subprocesses within the main process with a simple interface with the rest of the model. Fitness checking per component provides well-localized conformance information, aiding on the diagnosis of the causes behind the problems. Moreover, the relations between components can be exploded to improve the diagnosis capabilities of the analysis, identifying areas with a high degree of mismatches, or providing a hierarchy for a zoom-in zoom-out analysis. Finally, the thesis proposed two main applications of the decomposed approach. First, the theory proposed is extended to incorporate data information for fitness checking in a decomposed manner. Second, a real-time event-based framework is presented for monitoring fitness.En las últimas décadas, la capacidad de los sistemas de información para generar y almacenar datos de eventos ha experimentado un crecimiento exponencial, especialmente en contextos como el industrial. Dispositivos conectados permanentemente a Internet (Internet of things), redes sociales, teléfonos inteligentes, y la computación en la nube proporcionan nuevas fuentes de datos, una tendencia que continuará en los siguientes años. La omnipresencia de grandes volúmenes de datos de eventos almacenados en logs abre la puerta al Process Mining (Minería de Procesos), una nueva disciplina a caballo entre las técnicas de gestión de procesos de negocio, el modelado de procesos, y la inteligencia de negocio. Las técnicas de minería de procesos pueden usarse para descubrir, analizar, y mejorar procesos reales, a base de extraer modelos a partir del comportamiento observado. La capacidad de estos modelos para representar la realidad determina la calidad de los resultados que se obtengan, condicionando su efectividad. El Conformance Checking (Verificación de Conformidad), objetivo final de esta tesis, permite analizar los comportamientos observados y modelados, y determinar si el modelo es una fiel representación de la realidad. La mayoría de los esfuerzos en Conformance Checking se han centrado en medir y asegurar que los modelos fueran capaces de capturar todo el comportamiento observado, también llamado "fitness". Otras propiedades, tales como asegurar la "precisión" de los modelos (no modelar comportamiento innecesario) han sido relegados a un segundo plano. La primera parte de esta tesis se centra en analizar la precisión, donde modelos describiendo la realidad con precisión son preferidos a modelos demasiado genéricos. La tesis presenta una nueva técnica basada en detectar "arcos de escape", i.e. puntos donde el comportamiento modelado se desvía del comportamiento reflejado en el log. Estos arcos de escape son usados para determinar, en forma de métrica, el nivel de precisión entre un log y un modelo, y para localizar posibles puntos de mejora. La tesis también presenta un intervalo de confianza sobre la métrica, así como una métrica multi-factorial para medir la severidad de las imprecisiones detectadas. Conformance Checking puede ser una operación costosa para escenarios reales, y entender las razones que causan los problemas requiere esfuerzo. La segunda parte de la tesis cambia el foco (de precisión a fitness), y propone el uso de técnicas de descomposición para ayudar en la verificación de fitness. Las técnicas propuestas se basan en descomponer el modelo en componentes con una sola entrada y una sola salida, llamados SESEs. Estos componentes representan subprocesos dentro del proceso principal. Verificar el fitness a nivel de subproceso proporciona una información detallada de dónde están los problemas, ayudando en su diagnóstico. Además, las relaciones entre subprocesos pueden ser explotadas para mejorar las capacidades de diagnóstico e identificar qué áreas concentran la mayor densidad de problemas. Finalmente, la tesis propone dos aplicaciones directas de las técnicas de descomposición: 1) la teoría es extendida para incluir información de datos a la verificación de fitness, y 2) el uso de sistemas descompuestos en tiempo real para monitorizar fitnes

The alignment of formal, structured and unstructured process descriptions

Nowadays organizations are experimenting a drift on the way processes are managed. On the one hand, formal notations like Petri nets or Business Process Model and Notation (BPMN) enable the unambiguous reasoning and automation of designed processes. This way of eliciting processes by manual design, which stemmed decades ago, will still be an important actor in the future. On the other hand, regulations require organizations to store their process executions in structured representations, so that they are known and can be analyzed. Finally, due to the different nature of stakeholders within an organization (ranging from the most technical members, e.g., developers, to less technical), textual descriptions of processes are also maintained to enable that everyone in the organization understands their processes. In this paper I will describe techniques for facilitating the interconnection between these three process representations. This requires interdisciplinary research to connect several fields: business process management, formal methods, natural language processing and process mining.Peer ReviewedPostprint (author's final draft