Conformance Checking Techniques of Process Mining: A Survey

Conformance Checking (CC) techniques enable us to gives the deviation between modelled behavior and actual execution behavior. The majority of organizations have Process-Aware Information Systems for recording the insights of the system. They have the process model to show how the process will be executed. The key intention of Process Mining is to extracting facts from the event log and used them for analysis, ratification, improvement, and redesigning of a process. Researchers have proposed various CC techniques for specific applications and process models. This paper has a detailed study of key concepts and contributions of Process Mining. It also helps in achieving business goals. The current challenges and opportunities in Process Mining are also discussed. The survey is based on CC techniques proposed by researchers with key objectives like quality parameters, perspective, algorithm types, tools, and achievements.

Multilevel Privacy Assurance Evaluation of Healthcare Metadata

Healthcare providers are legally bound to ensure the privacy preservation of healthcare metadata. Usually, privacy concerning research focuses on providing technical and inter-/intra-organizational solutions in a fragmented manner. In this wake, an overarching evaluation of the fundamental (technical, organizational, and third-party) privacy-preserving measures in healthcare metadata handling is missing. Thus, this research work provides a multilevel privacy assurance evaluation of privacy-preserving measures of the Dutch healthcare metadata landscape. The normative and empirical evaluation comprises the content analysis and process mining discovery and conformance checking techniques using real-world healthcare datasets. For clarity, we illustrate our evaluation findings using conceptual modeling frameworks, namely e3-value modeling and REA ontology. The conceptual modeling frameworks highlight the financial aspect of metadata share with a clear description of vital stakeholders, their mutual interactions, and respective exchange of information resources. The frameworks are further verified using experts’ opinions. Based on our empirical and normative evaluations, we provide the multilevel privacy assurance evaluation with a level of privacy increase and decrease. Furthermore, we verify that the privacy utility trade-off is crucial in shaping privacy increase/decrease because data utility in healthcare is vital for efficient, effective healthcare services and the financial facilitation of healthcare enterprises.

Conformance Checking of Dwelling Time Using a Token-based Method

Background: Standard operating procedure (SOP) is a series of business activities to achieve organisational goals, with each activity carried to be recorded and stored in the information system together with its location (e.g., SCM, ERP, LMS, CRM). The activity is known as event data and is stored in a database known as an event log.Objective: Based on the event log, we can calculate the fitness to determine whether the business process SOP is following the actual business process.Methods: This study obtains the event log from a terminal operating system (TOS), which records the dwelling time at the container port. The conformance checking using token-based replay method calculates fitness by comparing the event log with the process model.Results: The findings using the Alpha algorithm resulted in the most traversed traces (a, b, n, o, p). The fitness calculation returns 1.0 were produced, missing, and remaining tokens are replied to each of the other traces.Conclusion: Thus, if the process mining produces a fitness of more than 0.80, this shows that the process model is following the actual business process. Keywords: Conformance Checking, Dwelling time, Event log, Fitness, Process Discovery, Process Mining

Prefix Imputation of Orphan Events in Event Stream Processing

In the context of process mining, event logs consist of process instances called cases. Conformance checking is a process mining task that inspects whether a log file is conformant with an existing process model. This inspection is additionally quantifying the conformance in an explainable manner. Online conformance checking processes streaming event logs by having precise insights into the running cases and timely mitigating non-conformance, if any. State-of-the-art online conformance checking approaches bound the memory by either delimiting storage of the events per case or limiting the number of cases to a specific window width. The former technique still requires unbounded memory as the number of cases to store is unlimited, while the latter technique forgets running, not yet concluded, cases to conform to the limited window width. Consequently, the processing system may later encounter events that represent some intermediate activity as per the process model and for which the relevant case has been forgotten, to be referred to as orphan events. The naïve approach to cope with an orphan event is to either neglect its relevant case for conformance checking or treat it as an altogether new case. However, this might result in misleading process insights, for instance, overestimated non-conformance. In order to bound memory yet effectively incorporate the orphan events into processing, we propose an imputation of missing-prefix approach for such orphan events. Our approach utilizes the existing process model for imputing the missing prefix. Furthermore, we leverage the case storage management to increase the accuracy of the prefix prediction. We propose a systematic forgetting mechanism that distinguishes and forgets the cases that can be reliably regenerated as prefix upon receipt of their future orphan event. We evaluate the efficacy of our proposed approach through multiple experiments with synthetic and three real event logs while simulating a streaming setting. Our approach achieves considerably higher realistic conformance statistics than the state of the art while requiring the same storage.

Blockchain support for execution, monitoring and discovery of inter-organizational business processes

In an Inter-Organizational Business Process (IOBP), independent organizations (collaborators) exchange messages to perform business transactions. With process mining, the collaborators could know what they are actually doing from process execution data and take actions for improving the underlying business process. However, process mining assumes that the knowledge of the entire process is available, something that is difficult to achieve in IOBPs since process execution data generally is not shared among the collaborating entities due to regulations and confidentiality policies (exposure of customers’ data or business secrets). Additionally, there is an inherently lack-of-trust problem in IOBP as the collaborators are mutually untrusted and executed IOBP can be subject to dispute on counterfeiting actions. Recently, Blockchain has been suggested for IOBP execution management to mitigate the lack-of-trust problem. Independently, some works have suggested the use of Blockchain to support process mining tasks. In this paper, we study and address the problem of IOBP mining whose management and execution is supported by Blockchain. As contribution, we present an approach that takes advantage of Blockchain capabilities to tackle, at the same time, the lack-of-trust problem (management and execution) and confident execution data collection for process mining (discovery and conformance) of IOBPs. We present a method that (i) ensures the business rules for the correct execution and monitoring of the IOBP by collaborators, (ii) creates the event log, with data cleaning integrated, at the time the IOBP executes, and (iii) produces useful event log in XES and CSV format for the discovery and conformance checking tasks in process mining. By a set of experiments on real IOBPs, we validate our method and evaluate its impact in the resulting discovered models (fitness and precision metrics). Results revealed the effectiveness of our method to cope with both the lack-of-trust problem in IOBPs at the time that contributes to collect the data for process mining. Our method was implemented as a software tool available to the community as open-source code.

Event logs gerados da simulação de diferentes cenários e analisados com mineração de processos.

A cada dia uma quantidade enorme de dados é gerada de sistemas gerenciados por informações. Geralmente esta informação é armazenada em banco de dados ou event logs. Mineração de processos pode utilizar esta informação para prover conhecimento útil para empresas. O objetivo deste trabalho é produzir event logs de diferentes cenários de simulação e analisá-los utilizando mineração de processos. Estes cenários tentam simular atividades contidianas em um ambiente de escritório. Um exemplo é o cenário de recurso fuzzy que tenta simular a incerteza inerente em atividades realizas por humanos. Para alcançar este objetivo algumas ferramentas open-source foram utilizadas. CPN Tools foi utilizada para construir e simular a Workflow net baseada na rede “Handle Complaint Process” e gerar os event logs durante as simulações. ProM foi utilizado para aplicar os algoritmos de process discovery e conformance checking nos event logs gerados. O algoritmo utilizado foi o Inductive Visual Miner. A comparação entre os cenários mostrou uma diferença significativa entre os tempos de execução devido ao propósito de cada cenário. Com este tipo de simulação de cenários, donos de negócios podem realizar simulações de possíveis cenários de sua empresa e estimar melhores deadlines para seus clientes.

Database-Less Extraction of Event Logs from Redo Logs

Process mining is widely adopted in organizations to gain deep insights about running business processes. This can be achieved by applying different process mining techniques like discovery, conformance checking, and performance analysis. These techniques are applied on event logs, which need to be extracted from the organization’s databases beforehand. This not only implies access to databases, but also detailed knowledge about the database schema, which is often not available. In many real-world scenarios, however, process execution data is available as redo logs. Such logs are used to bring a database into a consistent state in case of a system failure. This paper proposes a semi-automatic approach to extract an event log from redo logs alone. It does not require access to the database or knowledge of the databaseschema. The feasibility of the proposed approach is evaluated on two synthetic redo logs.