Local Concurrency Detection in Business Process Event Logs

Process mining techniques aim at analysing records generated during the execution of a business process in order to provide insights on the actual performance of the process. Detecting concurrency relations be- tween events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concur- rency relations at the level of event types under a global interpretation. If two event types are declared to be concurrent, every occurrence of one event type is deemed to be concurrent to one occurrence of the other. In practice, this interpretation is too coarse-grained and leads to over- generalization. This paper proposes a finer-grained approach, whereby two event types may be deemed to be in a concurrency relation relative to one state of the process, but not relative to other states. In other words, the detected concurrency relation holds locally, relative to a set of states. Experimental results both with artificial and real-life logs show that the proposed local concurrency detection approach improves the accuracy of existing concurrency detection techniques

A Framework for the Multi-Modal Analysis of Novel Behavior in Business Processes

Novelty detection refers to the task of finding observations that are new or unusual when compared to the ‘known’ behavior. Its practical and challenging nature has been proven in many application domains while in process mining field has very limited researched. In this paper we propose a framework for the multi-modal analysis of novel behavior in business processes. The framework exploits the potential of representation learning, and allows to look at the process from different perspectives besides that of the control flow. Experiments on a real-world dataset confirm the quality of our proposal

Identifying cohorts: Recommending drill-downs based on differences in behaviour for process mining

Process mining aims to obtain insights from event logs to im- prove business processes. In complex environments with large variances in process behaviour, analysing and making sense of such complex pro- cesses becomes challenging. Insights in such processes can be obtained by identifying sub-groups of traces (cohorts) and studying their differences. In this paper, we introduce a new framework that elicits features from trace attributes, measures the stochastic distance between cohorts defined by sets of these features, and presents this landscape of sets of features and their influence on process behaviour to users. Our framework differs from existing work in that it can take many aspects of behaviour into account, including the ordering of activities in traces (control flow), the relative frequency of traces (stochastic perspective), and cost. The framework has been instantiated and implemented, has been evaluated for feasibility on multiple publicly available real-life event logs, and evaluated on real-life case studies in two Australian universities

Nouveaux concepts de consultations médicales en oncologie

International audienceDe nouveaux concepts de consultations sont actuellement en train de profondément changer la façon d’exercer la médecine. L’utilisation de questionnaires standardisés, des questionnaires patients (PRO, patient-reported outcome, et son application informatique, ePRO) ont déjà fait irruption dans nos consultations. En parallèle, la télémédecine, voire l’utilisation d’agents conversationnels automatiques médicaux, permettent d’assurer une consultation à distance, plus accessible. Ces différents outils ont un intérêt majeur en oncologie, notamment dans le contexte de la chronicisation des maladies et du suivi à long terme que les oncologues radiothérapeutes prennent en charge. Dans cet article, nous détaillons chacun de ces nouveaux concept

Filtering Spurious Events from Event Streams of Business Processes

Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions un- fold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviour. Hence, applying these techniques on real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to effectively filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results

Single-event transients in Indium Gallium Arsenide MOSFETs for Sub-10 nm CMOS technology

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique

Finding non-compliances with declarative process constraints through semantic technologies

Business process compliance checking enables organisations to assess whether their processes fulfil a given set of constraints, such as regulations, laws, or guidelines. Whilst many process analysts still rely on ad-hoc, often handcrafted per-case checks, a variety of constraint languages and approaches have been developed in recent years to provide automated compliance checking. A salient example is Declare, a well-established declarative process specification language based on temporal logics. Declare specifies the behaviour of processes through temporal rules that constrain the execution of tasks. So far, however, automated compliance checking approaches typically report compliance only at the aggregate level, using binary evaluations of constraints on execution traces. Consequently, their results lack granular information on violations and their context, which hampers auditability of process data for analytic and forensic purposes. To address this challenge, we propose a novel approach that leverages semantic technologies for compliance checking. Our approach proceeds in two stages. First, we translate Declare templates into statements in SHACL, a graph-based constraint language. Then, we evaluate the resulting constraints on the graph-based, semantic representation of process execution logs. We demonstrate the feasibility of our approach by testing its implementation on real-world event logs. Finally, we discuss its implications and future research directions