Within process mining, a relevant activity is conformance checking. Such activity consists of establishing the extent to which actual executions of a process conform the expected behavior of a reference model. Current techniques focus on prescriptive models of the control-flow as references. In certain scenarios, however, a prescriptive model might not be available and, additionally, the control-flow perspective might not be ideal for this purpose. This paper tackles these two problems by suggesting a conformance approach that uses a descriptive model (i.e., a pattern of the observed behavior over a certain amount of time) which is not necessarily referring to the control-flow (e.g., it can be based on the social network of handover of work). Additionally, the entire approach can work both offline and online, thus providing feedback in real time. The approach, which is implemented in ProM, has been tested and results from 3 experiments with real world as well as synthetic data are reported.
Logs of real-life processes often feature uncertainty pertaining the recorded timestamps, data values, and/or events. We consider the problem of checking conformance of uncertain logs against data-aware reference processes. Specifically, we show how to solve it via SMT encodings, lifting previous work on data-aware SMT-based conformance checking to this more sophisticated setting. Our approach is modular, in that it homogeneously accommodates for different types of uncertainty. Moreover, using appropriate cost functions, different conformance checking tasks can be addressed. We show the correc…
University of Trier（特里尔大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University Hospital Münster（明斯特大学医院）；Heinrich Heine University（海因里希·海涅大学）
Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Thus, this technique is predestined to be used in the medical context for the comparison of treatment cases with clinical guidelines. However, medical processes are highly variable, highly dynamic, and complex. This makes the use of imperative conformance checking approaches in the medical domain difficult. Studies show that declarative approaches can better address these characteristics. However, none of the approaches has yet gained practical acceptance. Another cha…
Conformance checking techniques allow us to evaluate how well some exhibited behaviour, represented by a trace of monitored events, conforms to a specified process model. Modern monitoring and activity recognition technologies, such as those relying on sensors, the IoT, statistics and AI, can produce a wealth of relevant event data. However, this data is typically characterised by noise and uncertainty, in contrast to the assumption of a deterministic event log required by conformance checking algorithms. In this paper, we extend alignment-based conformance checking to function under a probabi…
Conformance checking is a key process mining task for comparing the expected behavior captured in a process model and the actual behavior recorded in a log. While this problem has been extensively studied for pure control-flow processes, conformance checking with multi-perspective processes is still at its infancy. In this paper, we attack this challenging problem by considering processes that combine the data and control-flow dimensions. In particular, we adopt data Petri nets (DPNs) as the underlying reference formalism, and show how solid, well-established automated reasoning techniques can…
Zhejiang University（浙江大学）；Eindhoven University of Technology（埃因霍温理工大学）
Conformance checking techniques are widely adopted to pinpoint possible discrepancies between process models and the execution of the process in reality. However, state of the art approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process. In this work, we propose a novel approach which allows to represent actors' tolerance with…
The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of process data in information systems. These are made available in the form of event logs. Process mining techniques enable the process-centric analysis of data, including automatically discovering process models and checking if event data conform to a given model. In this paper, we analyze the previously unexplored setting of uncertain event logs. In such event logs uncertainty is recorded explicitly, i.e., the time, activi…
University of Mannheim（曼海姆大学）；Kühne Logistics University（库恩物流大学）；Hasso Plattner Institute（哈索·普拉特纳研究所）；University of Potsdam（波茨坦大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially order…
LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines
Computer Science Institute, DiSIT, University of Piemonte Orientale（皮埃蒙特东方大学计算机科学研究所）；Integrated Laboratory of AI and Medical Informatics, DAIRI, SS. Antonio e Biagio e Cesare Arrigo Hospital（圣安东尼奥、比亚焦与切萨雷·阿里戈医院DAIRI人工智能与医学信息学综合实验室）
Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡大学（FAU）计算机科学系）
Process Mining has recently gained popularity in healthcare due to its potential to provide a transparent, objective and data-based view on processes. Conformance checking is a sub-discipline of process mining that has the potential to answer how the actual process executions deviate from existing guidelines. In this work, we analyze a medical training process for a surgical procedure. Ten students were trained to install a Central Venous Catheters (CVC) with ultrasound. Event log data was collected directly after instruction by the supervisors during a first test run and additionally after a …
Conformance checking techniques let us find out to what degree a process model and real execution data correspond to each other. In recent years, alignments have proven extremely useful in calculating conformance statistics. Most techniques to compute alignments provide an exact solution. However, in many applications, it is enough to have an approximation of the conformance value. Specifically, for large event data, the computing time for alignments is considerably long using current techniques which makes them inapplicable in reality. Also, it is no longer feasible to use standard hardware f…
Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS)（智能技术研究中心）；Universidade de Santiago de Compostela（圣地亚哥-德孔波斯特拉大学）
Real life business processes change over time, in both planned and unexpected ways. The detection of these changes is crucial for organizations to ensure that the expected and the real behavior are as similar as possible. These changes over time are called concept drift and its detection is a big challenge in process mining since the inherent complexity of the data makes difficult distinguishing between a change and an anomalous execution. In this paper, we present C2D2 (Conformance Checking-based Drift Detection), a new approach to detect sudden control-flow changes in the process models from…
University of Vienna（维也纳大学）；Technical University of Munich（慕尼黑工业大学）
Process mining has matured as analysis instrument for process-oriented data in recent years. Manufacturing is a challenging domain that craves for process-oriented technologies to address digitalization challenges. We found that process mining creates high expectations, but its implementation and usage by manufacturing experts such as process supervisors and shopfloor workers remain unclear to a certain extent. Reason (1) is that even though manufacturing allows for well-structured processes, the actual workflow is rarely captured in a process model. Even if a model is available, a software fo…
The University of Melbourne（墨尔本大学）；Tecnológico de Monterrey（蒙特雷科技大学）
Given an event log as a collection of recorded real-world process traces, process mining aims to automatically construct a process model that is both simple and provides a useful explanation of the traces. Conformance checking techniques are then employed to characterize and quantify commonalities and discrepancies between the log's traces and the candidate models. Recent approaches to conformance checking acknowledge that the elements being compared are inherently stochastic - for example, some traces occur frequently and others infrequently - and seek to incorporate this knowledge in their a…
RWTH Aachen University（亚琛工业大学）；University of Pisa（比萨大学）；RWTH University Hospital（亚琛工业大学医院）
The discipline of process mining has a solid track record of successful applications to the healthcare domain. Within such research space, we conducted a case study related to the Intensive Care Unit (ICU) ward of the Uniklinik Aachen hospital in Germany. The aim of this work is twofold: developing a normative model representing the clinical guidelines for the treatment of COVID-19 patients, and analyzing the adherence of the observed behavior (recorded in the information system of the hospital) to such guidelines. We show that, through conformance checking techniques, it is possible to analyz…
The University of Melbourne（墨尔本大学）；Sapienza Università di Roma（罗马第一大学）；Tecnológico de Monterrey（蒙特雷理工学院）；Queensland University of Technology（昆士兰科技大学）；Wirtschaftsuniversität Wien（维也纳经济大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encode…
Conformance checking encompasses a body of process mining techniques which aim to find and describe the differences between a process model capturing the expected process behavior and a corresponding event log recording the observed behavior. Alignments are an established technique to compute the distance between a trace in the event log and the closest execution trace of a corresponding process model. Given a cost function, an alignment is optimal when it contains the least number of mismatches between a log trace and a model trace. Determining optimal alignments, however, is computationally …
Bar-Ilan University（巴伊兰大学）；The Technion - Israel Institute of Technology（以色列理工学院）
Motivated by the abundance of uncertain event data from multiple sources including physical devices and sensors, this paper presents the task of relating a stochastic process observation to a process model that can be rendered from a dataset. In contrast to previous research that suggested to transform a stochastically known event log into a less informative uncertain log with upper and lower bounds on activity frequencies, we consider the challenge of accommodating the probabilistic knowledge into conformance checking techniques. Based on a taxonomy that captures the spectrum of conformance c…
RWTH Aachen University（亚琛工业大学）；Eindhoven University of Technology（埃因霍温理工大学）；Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所FIT）
Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process. There exist different categories of conformance measures. Recall, also called fitness, is concerned with quantifying how much of the behavior that was observed in the event log fits the process model. Precision is concerned with quantifying how much b…
Efficient Conformance Checking of Rich Data-Aware Declare Specifications (Extended)
Universidade de Santiago de Compostela（圣地亚哥-德孔波斯特拉大学）；Free University of Bozen-Bolzano（博尔扎诺自由大学）；INESC-ID/Instituto Superior Técnico, Universidade de Lisboa（INESC-ID/里斯本技术学院，里斯本大学）
Sapienza University of Rome（罗马第一大学）；Free University of Bozen-Bolzano（博尔扎诺自由大学）
We propose Answer Set Programming (ASP) as an approach for modeling and solving problems from the area of Declarative Process Mining (DPM). We consider here three classical problems, namely, Log Generation, Conformance Checking, and Query Checking. These problems are addressed from both a control-flow and a data-aware perspective. The approach is based on the representation of process specifications as (finite-state) automata. Since these are strictly more expressive than the de facto DPM standard specification language DECLARE, more general specifications than those typical of DPM can be hand…
Dongeui University（东义大学）；Pusan National University（釜山国立大学）
Process mining is a methodology for the derivation and analysis of process models based on the event log. When process mining is employed to analyze business processes, the process discovery step, the conformance checking step, and the enhancements step are repeated. If a user wants to analyze a process from multiple perspectives (such as activity perspectives, originator perspectives, and time perspectives), the above procedure, inconveniently, has to be repeated over and over again. Although past studies involving process mining have applied detailed stepwise methodologies, no attempt has be…
