A Arya Adriansyah

Process Mining Manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Conformance Checking Using Cost-Based Fitness Analysis

The growing complexity of processes in many organizations stimulates the adoption of business process analysis techniques. Typically, such techniques are based on process models and assume that the operational processes in reality conform to these models. However, experience shows that reality often deviates from hand-made models. Therefore, the problem of checking to what extent the operational process conforms to the process model is important for process management, process improvement, and compliance. In this paper, we present a robust replay analysis technique that is able to measure the conformance of an event log for a given process model. The approach quantifies conformance and provides intuitive diagnostics (skipped and inserted activities). Our technique has been implemented in the ProM 6framework. Comparative evaluations show that the approach overcomes many of the limitations of existing conformance checking techniques.

Causal nets: a modeling language tailored towards process discovery

Process discovery--discovering a process model from example behavior recorded in an event log--is one of the most challenging tasks in process mining. The primary reason is that conventional modeling languages (e.g., Petri nets, BPMN, EPCs, and ULM ADs) have difficulties representing the observed behavior properly and/or succinctly. Moreover, discovered process models tend to have deadlocks and livelocks. Therefore, we advocate a new representation more suitable for process discovery: causal nets. Causal nets are related to the representations used by several process discovery techniques (e.g., heuristic mining, fuzzy mining, and genetic mining). However, unlike existing approaches, we provide declarative semantics more suitable for process mining. To clarify these semantics and to illustrate the non-local nature of this new representation, we relate causal nets to Petri nets.

Alignment based precision checking

Most organizations have process models describing how cases need to be handled. In fact, legislation and standardization (cf. the Sarbanes-Oxley Act, the Basel II Accord, and the ISO 9000 family of standards) are forcing organizations to document their processes. These processes are often not enforced by information systems. However, torrents of event data are recorded by today’s information systems. These recorded events reflect how processes are really executed. Often reality deviates from the modeled behavior. Therefore, measuring the extent process executions conform to a predefined process model is increasingly important. In this paper, we propose an approach to measure the precision of a process model with respect to an event log. Unlike earlier approaches, we first align model and log thus making our approach more robust, even in case of deviations. The approach has been implemented in the ProM 6 tool and evaluated using both artificial and real life cases.

Cost-Based Fitness in Conformance Checking

Experience in business process analysis shows that operational processes often do not conform to process models. Although classical conformance checking techniques can identify deviations of process executions from predefined models, they may produce inaccurate results due to strong assumptions. In this paper, we present a robust conformance checking technique based on Petri net techniques allowing us to lift assumptions and to take into account the cost of deviating from given models.

Process mining : fuzzy clustering and performance visualization

The goal of performance analysis of business processes is to gain insights into operational processes, for the purpose of optimizing them. To intuitively show which parts of the process might be improved, performance analysis results can be projected onto process models. This way, bottlenecks can quickly be identified and resolved.

Controlling Break-the-Glass through Alignment

Modern IT systems have to deal with unpredictable situations and exceptions more and more often. In contrast, security mechanisms are usually very rigid. Functionality like break-the-glass is thus employed to allow users to bypass security mechanisms in case of emergencies. However, break-the-glass introduces a weak point in the system. In this paper, we present a flexible framework for controlling the use of break-the-glass using the notion of alignments. The framework measures to what extent a process execution diverges from the specification (i.e., using optimal alignments) and revokes the exceptional permissions granted to cope with the emergency when the severity of deviations cannot be tolerated. For the quantification of the severity of deviations, we extend alignment-based deviation analysis techniques by supporting the detection of high-level deviations such as activity replacements and swaps, hence providing a more accurate diagnosis of deviations than classical optimal alignments.

Mining Process Performance from Event Logs

In systems where process executions are not strictly enforced by a predefined process model, obtaining reliable performance information is not trivial. In this paper, we analyzed an event log of a real-life process, taken from a Dutch financial institute, using process mining techniques. In particular, we exploited the alignment technique [2] to gain insights into the control flow and performance of the process execution. We showed that alignments between event logs and discovered process models from process discovery algorithms reveal insights into frequently occurring deviations and how such insights can be exploited to repair the original process models to better reflect reality. Furthermore, we showed that the alignments can be further exploited to obtain performance information. All analysis in this paper is performed using plug-ins within the open-source process mining toolkit ProM.

Privacy Analysis of User Behavior Using Alignments

Abstract Privacy is becoming a urgent issue in information systems nowadays because of the stringent requirements imposed by data protection regulations. Traditional security approaches based on access control and authorization are not adequate to address these requirements. The underlying fundamental problem is that those approaches are preventive and thus they are not able to deal with exceptions. In this paper, we present a practical privacy framework that shifts the problem of preventing infringements into a problem of detecting infringements. The framework is based on systematic log auditing, use of patterns and privacy metrics to detect and quantify infringements.