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The automatic detection of divergences from a desired process behavior is a common research topic in the business process management community. An established technique to analyze processes is called conformance checking. Given a definition of a process in form of a process model, conformance checking can be used to test whether the executions of a process contained in a so-called event log data structure are conforming with the process as it was defined. The result is a comparison of the execution traces and their respective correct execution, according to the process model. This technique provides insights into where the divergence has occurred and how the execution must be altered to conform to the process model. However, a problem is that it requires a process model to be available. Process models in the correct format are not always available. Contrary to conformance checking, process anomaly detection aims to find anomalous executions without relying on a predefined process model. A process anomaly detection algorithm derives the process logic from the event log itself and exploits the patterns found within the event log to distinguish normal from anomalous process executions. Though process anomaly detection provides the benefit of not relying on a process model, it typically does not provide the level of detail that conformance checking does. A process execution can either be normal or it can be anomalous. This dissertation proposes process anomaly correction, a novel approach that combines the benefits of conformance checking and process anomaly detection. Given only an event log, process anomaly correction detects anomalous executions, clearly indicates where the anomaly has occurred during the execution and suggests possible corrective measures. The solution presented in this work is based on a new concept to the field of process anomaly detection: Process learning. In process learning, the task of understanding the process based on the example data is transformed into a learning problem in which a neural network is trained to predict the very next activity in a running process execution. The resulting machine learning model thus represents an approximation of the real process that created the data. This cumulative dissertation consists of five contributions to the field of business process management that demonstrate how, starting from a process anomaly detection, process anomaly correction is achieved in a series of four steps. (1) Process learning is employed to generate an approximated model of the process logic. (2) The limitation of only distinguishing between normal and anomalous process executions is overcome by employing the process learning model which processes the process executions on a finer level of detail than existing approaches. (3) The necessity of providing manual threshold settings, as it is typical for process anomaly detection algorithms, is replaced by an automatic parameterization utilizing the process learning model. (4) The predictive capabilities of the process learning model are exploited to generate possible corrections of detected anomalies. The resulting process anomaly correction approach can be employed in scenarios where classic conformance checking would be infeasible, due to the restriction of relying on a process model. Furthermore, it can be employed alongside classical conformance checking, for it incorporates more information coming from the event log than classical conformance checking (such as employees executing a process step, in which country the process is executed, etc.), and thus provides a new perspective for the process analyst.
25Discovering process models for the analysis of application failures under uncertainty of event logs
Discovering process models for the analysis of application failures under uncertainty of event logs
1Point Process Modeling with Spatiotemporal Covariates for Predicting Crime
Self-exciting point processes are widely used to model events occurring in time and space whose rate depends on the past history of the process, such as earthquake<br>aftershocks, crime, and neural spike trains. By modeling the event rate as the sum of a background (or immigrant) process, often an inhomogeneous Poisson process, and<br>an offspring process consisting of events triggered by previous events, self-exciting point process models naturally account for complex clustering behavior. When the<br>model is physically motivated, as are models of earthquake aftershock sequences, model parameters have direct interpretations in terms of the generative mechanism.<br>In this thesis, I focus in particular on the application of self-exciting point processes to crime. Crime rates are known to vary greatly in space within a city, as a result of many demographic and economic factors, and crime often exhibits “nearrepeats,” when one crime is followed by another soon after, either from retaliation or because offenders tend to return to the same areas. Point process models have<br>been used to predict crime, but the available models can be improved: they cannot explicitly account for spatially varying covariates and estimate their effects, and there are no inference tools that could be used to test criminological theories or evaluate interventions. After extensively reviewing the literature on self-exciting point processes, I introduce<br>a new model which accounts for both spatial covariates and self-excitation, and explore its benefits over simple lagged regressions and other commonly used methods. After discussing computational issues in fitting the model, I use simulations to explore methods for parameter inference, review a set of residual diagnostics and animations, and use these diagnostics to explore the model’s behavior under<br>various forms of model misspecification, giving practical advice for the interpretation of model fits. To demonstrate the model’s utility, I then analyze large databases<br>of Pittsburgh and Baltimore crime records, linking crime rates to several relevant spatial covariate and leading indicator events, and comparing several model variations.<br>
Read more6Using Process Analytics to Improve Healthcare Processes
Healthcare processes are inherently complex as each patient is unique and medical staff deviate from protocols, often for valid reasons. Event logs collected by modern process-aware (healthcare) information systems provide a wealth of data and can be used to analyze the adherence to these protocols. Process mining is a young research area combining data science (machine learning, data mining, etc.) and business process management. Its main contributions have been techniques for process discovery (the automatic learning of process models from event data) and conformance checking (aligning observed and modeled behavior). However, existing techniques face challenging issues discovering high-quality process models in a healthcare setting. In this chapter we introduce the key concepts of process mining such as event logs, process models, and process discovery. We then show the application of two recent process mining techniques on a public event data set from the healthcare domain to demonstrate how some of the common pitfalls can be overcome. A first presented technique projects data statistics on a process model, allowing the analysis of correlations between patient characteristics and the executed activities (e.g., type of treatment). A second technique analyzes process performance without a specific need for a process model by considering contextual information. It correlates process characteristics with process performance.
Read more12Process mining using BPMN
Process mining is an emerging discipline incorporating methods and tools for the analysis of system/process executions captured in the form of event logs [1]. Traditionally process mining can be divided into three research areas: discovery (construction of process models from event logs), conformance checking (finding log and model deviations), and enhancement of existing process models with additional event log data.BPMN (Business Process Model and Notation) 2.0 [2] is a widely used process modeling notation, supported by various process modeling and analysis tools, and is a de-facto process modeling standard. Using BPMN within process mining opens perspectives for applicability of the existing process mining techniques: for instance discovered process models can be analyzed or enacted using existing BPMN-based software, and vice versa, manually created models can be imported to a process mining tool, verified against event logs, and enhanced with additional data.In this work we bridge the gap between conventional process modeling formalisms used in the context of process mining (e.g., Petri nets, causal nets, process trees) and BPMN. For that purpose we developed a suite of conversion algorithms and provide formal guarantees relating the behavior of Petri nets (including non-free-choice nets) to the corresponding BPMN models (and vice versa). The derived relations are used to enhance the BPMN models with information learned from the event logs.The developed conversion techniques are described in detail in [3] and have been implemented [4] as a part of ProM (Process Mining Framework) [5] -- an open source tool for process mining and verified on real event log data. Moreover, cases for which conversion algorithms give more compact process models in comparison with the initial models are identified. Although the developed algorithms deal with basic control flow constructs only, they can be applied in the discovery of advanced BPMN modeling elements [2], including subprocesses [6-7], cancellations [8], conditional branching and data objects, swimlanes, message flows, and others.
Read moreO uso do conhecimento como técnica evolutiva voltada à melhoria dos processos e de tomada de decisão
In the current scenario of organizations Process Management aims to perform process in an organized, prioritizing actions through techniques and methods that are linked to the improvement of the organization in the market segment it operates. As a determinant for achieving goals successfully, organizational memory is all important in the management of processes, allowing all areas meet in a systemic way, crediting their information to the various organizational sectors and thus using knowledge to direct an action with a focus planning to achieve strategic organizational goal. The organizational memory can be registered through the development of a knowledge base. The work in question aims to provide a better understanding of the importance of the knowledge base in an organization, to perform appropriate actions, planning, simulating and reaching a decision through meaningful data. Soon after the development of a bibliographic research, a bibliometric study on the proposed topic was accomplished with the main events of the Brazilian scientific areas of production engineering, analyzing how the topic has been addressed by authors in the areas of Process Management and Base Knowledge. The main results so far observed the need for the implementation of knowledge-based models in systems that seek to improve the execution of processes and thus reduce failures and decision-making processes more appropriate.
Read more9Global conformance checking measures using shallow representation and deep learning
Global conformance checking measures using shallow representation and deep learning
A framework for purpose-guided event logs generation
Process mining is a prominent discipline in business process management. It collects a variety of techniques for gathering information from event logs, each fulfilling a different mining purpose. Event logs are always necessary for assessing and validating mining techniques in relation to specific purposes. Unfortunately, event logs are hard to find and usually contain noise that can influence the validity of the results of a mining technique. In this paper, we propose a framework, named PURPLE, for generating, through business model simulation, event logs tailored for different mining purposes, i.e., discovery, what-if analysis, and conformance checking. It supports the simulation of models specified in different languages, by projecting their execution onto a common behavioral model, i.e., a labeled transition system. We present eleven instantiations of the framework implemented in a software tool by-product of this paper. The framework is validated against reference log generators through experiments on the purposes presented in the paper.
Read more26Object-Aware Process Management
Companies increasingly adopt process management systems (PrMS), which offer promising perspectives for a more flexible and efficient process execution. However, for many process-aware application systems (e.g., ERP or CRM systems), the underlying process logic is still hard-coded. As a consequence, these business applications are both complex to design and costly to maintain; i.e., they require long development cycles, and even simple process changes might result in costly code adaptions and high efforts for testing. One reason for this situation stems from the fact that contemporary PrMS were primarily designed for the support of highly structured, repetitive business processes. By contrast, many processes found in practice are rather unstructured or semi-structured, i.e., they are knowledge-intensive and driven by user decisions. In addition, the business functions to be integrated with these processes usually cannot be straight-jacketed into activities. For all these reasons, the activity-centered paradigm of contemporary PrMS is by far too inflexible for realizing more advanced business applications. This deficiency mainly stems from the unsatisfactory integration of processes and data in existing PrMS. Despite emerging approaches, which target at a tighter integration of process and data, a unified and comprehensive understanding of the relationships between them is still missing. This thesis first analyzes real processes not adequately supported by existing PrMS and elaborates their characteristic properties. As a major insight it became clear that in many application scenarios comprehensive process support requires both object- and process-awareness. This means, business processes and business data must not be treated independently from each other. Instead, business processes must comply with the underlying data structure. In particular, in accordance to the given data model comprising object types and object relations, the modeling, execution and monitoring of processes must be based on two levels of granularity: object behavior and object interactions. Further, the individual processes, coordinating the behavior of single object instances, must be coordinated with the ones of related object instances. Opposed to these well-defined process support granularity levels, activities must be executable at different levels of granularity. In particular, while a particular user may only want to work on a particular object instance, another one may want to process a number of related object instances in one go. Furthermore, process execution must be accomplished in a data-driven manner; i.e., the progress of a process instance mainly depends on available business objects and on value changes of their attributes. Finally, authorized users must be able to access and manage process-related objects at any point in time. Based on the properties identified, this thesis elaborates major requirements for enabling object-awareness in processes management systems. The major contribution of the thesis is the PHILharmonicFlows framework, which addresses these requirements and enables object-aware process management in a comprehensive manner. In particular, the framework not only provides a new process modeling approach, but also establishes a well-defined operational semantics enabling the automatic and dynamic generation of end-user components for object- and process-aware business applications at run-time (e.g., overview tables and user forms). Overall, a holistic framework integrating data, processes and users offers promising perspectives in order to overcome the numerous limitations of contemporary PrMS. This thesis considers research in this area as fundamental maturation of process management technology.
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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
Read more49Distributed Process Discovery and Conformance Checking
Process mining techniques have matured over the last decade and more and more organization started to use this new technology. The two most important types of process mining are process discovery (i.e., learning a process model from example behavior recorded in an event log) and conformance checking (i.e., comparing modeled behavior with observed behavior). Process mining is motivated by the availability of event data. However, as event logs become larger (say terabytes), performance becomes a concern. The only way to handle larger applications while ensuring acceptable response times, is to distribute analysis over a network of computers (e.g., multicore systems, grids, and clouds). This paper provides an overview of the different ways in which process mining problems can be distributed. We identify three types of distribution: replication, a horizontal partitioning of the event log, and a vertical partitioning of the event log. These types are discussed in the context of both procedural (e.g., Petri nets) and declarative process models. Most challenging is the horizontal partitioning of event logs in the context of procedural models. Therefore, a new approach to decompose Petri nets and associated event logs is presented. This approach illustrates that process mining problems can be distributed in various ways.
Read more33Enabling flexible location-aware business process modeling and execution
Enabling flexible location-aware business process modeling and execution
5Conformance checking with different levels of granularity : A case study on bariatric surgery
Conformance checking provides a set of techniques for comparing observed behavior (event logs) with process models. Health care providers apply it for verifying compliance between activities realized by the patients and clinical protocols. However, health care processes are characterized by high complexity and variability, and clinical protocols can have a level of granularity different from the one in the recorded events. We proposed a new method for configuring the cost function for the alignment-based conformance checking, handling the different levels of granularity to address this problem. The costs are configured based on grouping activities and the specification of undesirable events. We applied this method using the ProM Framework in a real-world dataset containing events from patients who realized a bariatric surgery in 2016. The technique can also be applied in other conformance checking scenarios, where the granularity of the events differs from the process model. The case study result allowed us to automatically identify critical violations of the clinical protocol by analyzing the fitness metric. The proposed method could help auditors to identify crucial cases.
Read moreImpact of Threshold Setting for Event Log Repair on Conformance Checking.
Conformance checking is a method to compare the actually executed business process recorded as an event log with the business process described as a business process model and to identify differences. For human or technical reasons, event logs that contain noise and are of low quality may be recorded. Therefore, methods have been proposed to repair low-quality event logs, but they require the setting of a threshold, and it is difficult to set a suitable threshold. In this paper, we investigate the effect of low-quality event log repair methods on conformance checking. Through experiments, it was shown that the appropriate threshold depends on the type of event log and the amount of noise.
Read more6A new method for organizational process model discovery through the analysis of workflows and data exchange networks
Nowadays, organizations use process-aware information systems to understand and apply rapid changes to their processes. Process mining techniques automatically extract true dimensions of organizational processes including process models from data sets like event logs stored in these information systems. In most studies performed in the area of process model discovery, only information of the event logs is used. However, in this research, a novel method of process discovery is proposed, which uses event logs as well as the information on the data exchange among organizational roles, which is derived from physical generalized flow diagram model. This information formed the basis of a two-layered network that represents handover flow and data exchange flow among organizational roles. Then, by extracting and analyzing motifs existing in this network, five rules are set that map motifs with certain features to logical structures constructing process models. Finally, by integrating those structures, the process model will be discovered. The advantage of the proposed method over the previous ones is that from the business process management viewpoint, it is more efficient in detecting sophisticated structures in the process model. It is also highly resistant to noise. These benefits are derived from the fact that it exerts data exchange information along with event log information. Doing various experiments and evaluation of their results using the F-measure confirmed the superiority of this method to previous ones from the viewpoint of the business process management.
Read moreSituation Management with Complex Event Processing
With the broader dissemination of digital technologies, visionary concepts like the Internet of Things also affect an increasing number of use cases with interfaces to humans, e.g. manufacturing environments with technical operators monitoring the processes. This leads to additional challenges, as besides the technical issues also human aspects have to be considered for a successful implementation of strategic initiatives like Industrie 4.0. From a technical perspective, complex event processing has proven itself in practice to be capable of integrating and analyzing huge amounts of heterogeneous data and establishing a basic level of situation awareness by detecting situations of interests. Whereas this reactive nature of complex event processing systems may be sufficient for machine-to-machine use cases, the new characteristic of application fields with humans remaining in the control loop leads to an increasing action distance and delayed reactions. Taking human aspects into consideration leads to new requirements, with transparency and comprehensibility of the processing of events being the most important ones. Improving the comprehensibility of complex event processing and extending its capabilities towards an effective support of human operators allows tackling technical and non-technical challenges at the same time. The main contribution of this thesis answers the question of how to evolve state-of-the-art complex event processing from its reactive nature towards a transparent and holistic situation management system. The goal is to improve the interaction among systems and humans in use cases with interfaces between both worlds. Realizing a holistic situation management requires three missing capabilities to be introduced by the contributions of this thesis: First, based on the achieved transparency, the retrospective analysis of situations is enabled by collecting information related to a situation's occurrence and development. Therefore, CEP engine-specific situation descriptions are transformed into a common model, allowing the automatic decomposition of the underlying patterns to derive partial patterns describing the intermediate states of processing. Second, by introducing the psychological model of situation awareness into complex event processing, human aspects of information processing are taken into consideration and introduced into the complex event processing paradigm. Based on this model, an extended situation life-cycle and transition method are derived. The introduced concepts and methods allow the implementation of the controlling function of situation management and enable the effective acquisition and maintenance of situation awareness for human operators to purposefully direct their attention towards upcoming situations. Finally, completing the set of capabilities for situation management, an approach is presented to support the generation and integration of prediction models for predictive situation management. Therefore, methods are introduced to automatically label and extract relevant data for the generation of prediction models and to enable the embedding of the resulting models for an automatic evaluation and execution. The contributions are introduced, applied and evaluated along a scenario from the manufacturing domain.
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