Artifact Lifecycle Discovery

Artifact-centric modeling is a promising approach for modeling business processes based on the so-called business artifacts - key entities driving the company's operations and whose lifecycles define the overall business process. While artifact-centric modeling shows significant advantages, the overwhelming majority of existing process mining methods cannot be applied (directly) as they are tailored to discover monolithic process models. This paper addresses the problem by proposing a chain of methods that can be applied to discover artifact lifecycle models in Guard-Stage-Milestone notation. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) process discovery and analysis methods can be reused in a flexible manner. The methods presented in this paper are implemented as software plug-ins for ProM, a generic open-source framework and architecture for implementing process mining tools

Deriving Event Logs from Legacy Software Systems

Abstract. The modernization of legacy software systems is one of the key challenges in software industry, which requires comprehensive system analysis. In this context, process mining has proven to be useful for understanding the (business) processes implemented by the legacy software system. However, process mining algorithms are highly dependent on both the quality and existence of suitable event logs. In many scenarios, existing software systems (e.g., legacy applications) do not leverage process engines capable of producing such high-quality event logs, which hampers the application of process mining algorithms. Deriving suitable event log data from legacy software systems, therefore, constitutes a relevant task that fosters data-driven analysis approaches, including process mining, data-based process documentation, and process-centric software migration. This paper presents an approach for deriving event logs from legacy software systems by combining knowledge from source code and corresponding database operations. The goal is to identify relevant business objects as well as to document user and software interactions with them in an event log suitable for process mining

What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)

This technical report describes the intersection of process mining and large language models (LLMs), specifically focusing on the abstraction of traditional and object-centric process mining artifacts into textual format. We introduce and explore various prompting strategies: direct answering, where the large language model directly addresses user queries; multi-prompt answering, which allows the model to incrementally build on the knowledge obtained through a series of prompts; and the generation of database queries, facilitating the validation of hypotheses against the original event log. Our assessment considers two large language models, GPT-4 and Google's Bard, under various contextual scenarios across all prompting strategies. Results indicate that these models exhibit a robust understanding of key process mining abstractions, with notable proficiency in interpreting both declarative and procedural process models. In addition, we find that both models demonstrate strong performance in the object-centric setting, which could significantly propel the advancement of the object-centric process mining discipline. Additionally, these models display a noteworthy capacity to evaluate various concepts of fairness in process mining. This opens the door to more rapid and efficient assessments of the fairness of process mining event logs, which has significant implications for the field. The integration of these large language models into process mining applications may open new avenues for exploration, innovation, and insight generation in the field

Simplified literature review on the applicability of process mining to RPA

Business processes play an important role in any enterprise value chain and are involved in key activities such as the purchase of material, sales, and hiring of staff. Hence, mediumsized and large companies are inherently process-oriented. Managing business processes is yet, due to new regulations, technologies, and market changes, not a trivial task. In addition to that, the execution of business processes may be repetitive, tedious and time demanding. For this reason, there is a high motivation to automate such processes, which has been facilitated by the popularisation of Robotic Process Automation (RPA). RPA brings a cost-efficient solution for process automation along with a substantial challenge that is to decide what process to automate and how. Process Mining tools and techniques have been largely adopted to address challenges faced during RPA implementations. The goal of this work is to present the usage of Process Mining in RPA implementations through a simplified systematic literature review.Processos de negócio possuem um papel importante em qualquer cadeia de valores corporativa e estão envolvidos em atividades chave como compras de suprimentos, vendas e contratações de recursos humanos. Por esse motivo, empresas de médio e grande porte são inerentemente orientadas a processos. Devido à novas regulamentações, tecnologias e mudanças de mercado, a gestão de processos de negócio é ainda uma tarefa não trivial. Além disso, a execução de processos de negócio pode ser repetitiva, entendiante e demandar tempo. Por isso, existe uma alta motivação para automatizar processos de negócio, o que tem sido facilitado pela popularização da Automação de Processos Robóticos (Robotic Process Automation - RPA). RPA provê uma solução eficiente em custo para automação de processos e trás desafios no âmbito das escolhas de quais precessos automatizar e como. As ferramentas e metodologias de Mineração de Processos têm sido amplamente utilizadas para endereçar os desafios provenietes de implementações de RPA. O objetivo deste trabalho é apresentar as aplicações da Mineração de Processos em RPA, através de uma revisão sistemática simplificada da literatura

Predictive Process Monitoring for Lead-to-Contract Process Optimization

Äriprotsesside toetamiseks on üha laiemalt kasutusele võetud ettevõtte ressursside planeerimise (ERP) tööriistad, sealhulgas CRM süsteemid müügiprotsessi jaoks. ERP süsteemid salvestavad oma töö käigus protsesside logisid, mille oskuslik käsitlemine võimaldab efektiivistada äriprotsesse. Protsessilogide analüüsimiseks on välja töötatud protsessikaeve meetodid, mis oskavad logidest pöördprojekteerida tegelikult käivitatud protsesside mudeleid. Neid meetodeid on rakendatud koos ennustava seire meetoditega protsesside tulemuste soovitud ja soovimatute tulemuste varajaseks tuvastamiseks.\n\rKuigi ennustav seire on hiljuti rohkelt tähelepanu saanud ja leidnud rakendamist soovitusmootorites, mis pakuvad välja soovitusi äriprotsesside parendamiseks, ei ole seni palju uuritud kontekstiandmete, nt müügisüsteemi kirjetes klientide finantsandmed, mõju ennustava seire tulemustele soovituste kontekstis.\n\rKäesolevas magistritöös uuritakse kontekstiandmete mõju ennustava seire mudelite kvaliteedile müügiprotsessi optimeerimise kontekstis. Eksperimendid näitavad, et välistel kontekstiandmetel on pigem negatiivne mõju, samas kui sisemistel, protsessi käigus kogutud kontekstiandmetel on positiivne mõju mudelite kvaliteedile. Muuhulgas selgub eksperimentidest, et juba kolme esimese sündmuse baasil saab müügiprotsessis ennustada müügi õnnestumist.Business processes today are supported by enterprise systems such as Enter-\n\rprise Resource Planning systems. These systems store large amounts of process execution\n\rlog data that can be used to improve business processes across the organization. The\n\rprocess mining methods have been developed to analyze such logs, which are capable of\n\rextracting process models. These methods, in turn, have been applied in conjunctions\n\rwith predictive monitoring methods for early differentiation of desired and undesired\n\routcomes. Although predictive monitoring approach has recently caught attention and\n\rfound application in recommendation engines, which suggest cases to improve business\n\rprocess outcomes, there is no much research on how contextual data, such as clients fi-\n\rnancial indicators and other external data, may improve the quality of recommendations.\n\rThis thesis examines whether including the external data with the event data affects the\n\raccuracy of predictive monitoring for early predictions positively. More specifically, this\n\rthesis reveals usage of context data had the adverse effect on the performance of learned\n\rmodels. Furthermore, the study indicated that the usage of first three events from the\n\revent logs with internal data is sufficient to predict the label of an opportunity in the\n\rsales funnel

Workflow simulation for operational decision support using YAWL and ProM

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of work ow management. To do this we exploit not only the work ow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the work ow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for dierent scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM. This technical report contains a detailed description of how a simulation model including operational decision support can be generated by our software based on the running example