Predictive Monitoring of Business Processes

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital

What Shall I Do Next? Intention Mining for Flexible Process Enactment

International audienceBesides the benefits of flexible processes, practical implementations of process aware information systems have also revealed difficulties encountered by process participants during enactment. Several support and guidance solutions based on process mining have been proposed, but they lack a suitable semantics for human reasoning and decisions making as they mainly rely on low level activities. Applying design science, we created FlexPAISSeer, an intention mining oriented approach, with its component artifacts: 1) IntentMiner which discovers the intentional model of the executable process in an unsupervised manner; 2) In-tentRecommender which generates recommendations as intentions and confidence factors, based on the mined intentional process model and probabilistic calculus. The artifacts were evaluated in a case study with a Netherlands software company, using a Childcare system that allows flexible data-driven process enactment

Slice, mine and dice: Complexity-aware automated discovery of business process models

Automated process discovery techniques aim at extracting models from information system logs in order to shed light into the business processes supported by these systems. Existing techniques in this space are effective when applied to relatively small or regular logs, but otherwise generate large and spaghetti-like models. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. The result is a collection of process models -- each one representing a variant of the business process -- as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically by means of subprocess extraction. The proposed technique allows users to set a desired bound for the complexity of the produced models. Experiments on real-life logs show that the technique produces collections of models that are up to 64% smaller than those extracted under the same complexity bounds by applying existing trace clustering techniques

Conceptual Modelling - What and Why in Current Practice

Much research has been devoted over the years to investigating and advancing the techniques and tools used by analysts when they model. As opposed to what academics, software providers and their resellers promote as should be happening, the aim of this research was to determine whether practitioners still embraced conceptual modelling seriously. In addition, what are the most popular techniques and tools used for conceptual modelling? What are the major purposes for which conceptual modelling is used? The study found that the top six most frequently used modelling techniques and methods were ER diagramming, data flow diagramming, systems flowcharting, workflow modelling, RAD, and UML. However, the primary contribution of this study was the identification of the factors that uniquely influence the continued-use decision of analysts, viz., communication (using diagrams) to/from stakeholders, internal knowledge (lack of) of techniques, user expectations management, understanding models integration into the business, and tool/software deficiencies

Anomaly Detection Using Process Mining

Recently, several large companies have been involved in financial scandals related to mismanagement, resulting in financial damages for their stockholders. In response, certifications and manuals for best practices of governance were developed, and in some cases, tougher federal laws were implemented (e.g. the Sarboness Oxley Act). Companies adhered to these changes adopting the best practices for corporate governance by deploying Process Aware Information Systems (PAISs) to automate their business processes. However, these companies demand a rapid response to strategic changes, so the adoption of normative PAISs may compromise their competitiveness. On one hand companies need flexible PAISs for competitiveness reasons. On the other hand flexibility may compromise security of system because users can execute tasks that could result into violation of financial loses. In order to re-balance this trade-off, we present in this work how ProM tools can support anomaly detection in logs of PAIS. Besides, we present the results of the application of our approach with a real case

Genetic process mining

The topic of process mining has attracted the attention of both researchers and tool vendors in the Business Process Management (BPM) space. The goal of process mining is to discover process models from event logs, i.e., events logged by some information system are used to extract information about activities and their causal relations. Several algorithms have been proposed for process mining. Many of these algorithms cannot deal with concurrency. Other typical problems are the presence of duplicate activities, hidden activities, non-free-choice constructs, etc. In addition, real-life logs contain noise (e.g., exceptions or incorrectly logged events) and are typically incomplete (i.e., the event logs contain only a fragment of all possible behaviors). To tackle these problems we propose a completely new approach based on genetic algorithms. As can be expected, a genetic approach is able to deal with noise and incompleteness. However, it is not easy to represent processes properly in a genetic setting. In this paper, we show a genetic process mining approach using the so-called causal matrix as a representation for individuals. We elaborate on the relation between Petri nets and this representation and show that genetic algorithms can be used to discover Petri net models from event logs. Keywords: Process Mining, Petri Nets, Genetic Algorithms, Process Discovery, Business Process Intelligence, Business Activity Monitoring

Genetic process mining : a basic approach and its challenges

One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. To overcome these problems, we try to use genetic algorithms to mine process models. The non-trivial constructs are tackled by choosing an internal representation that supports them. The noise problem is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The definition of a good fitness measure is the most critical challenge in a genetic approach. This paper presents the current status of our research and the pros and cons of the fitness measure that we used so far. Experiments show that the fitness measure leads to the mining of process models that can reproduce all the behavior in the log, but these mined models may also allow for extra behavior. In short, the current version of the genetic algorithm can already be used to mine process models, but future research is necessary to always ensure that the mined models do not allow for extra behavior. Thus, this paper also discusses some ideas for future research that could ensure that the mined models will always only reflect the behavior in the log