Related papers: OPerA: Object-Centric Performance Analysis
Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing…
A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures…
The Object-Centric Event Data (OCED) is a novel meta-model aimed at providing a common ground for process data records centered around events and objects. One of its objectives is to foster interoperability and process information exchange.…
Recently, information systems like ERP, CRM and WFM record different business events or activities in a log named as event log. Process mining aims at extracting information from event logs to capture business process as it is being…
Process mining is a set of techniques that are used by organizations to understand and improve their operational processes. The first essential step in designing any process reengineering procedure is to find process improvement…
Process mining is shifting towards use cases that explicitly leverage the relations between data objects and events under the term of object-centric process mining. Realizing this shift and generally simplifying the exchange and…
Object-centric process mining is emerging as a promising paradigm across diverse industries, drawing substantial academic attention. To support its data requirements, existing object-centric data formats primarily facilitate the exchange of…
Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated…
Conformance checking is a crucial aspect of process mining, where the main objective is to compare the actual execution of a process, as recorded in an event log, with a reference process model, e.g., in the form of a Petri net or a BPMN.…
The discipline of process mining deals with analyzing execution data of operational processes, extracting models from event data, checking the conformance between event data and normative models, and enhancing all aspects of processes.…
Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn. One can use process data for many downstream tasks such as…
Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process,…
Software performance modeling plays a crucial role in developing and maintaining software systems. A performance model analytically describes the relationship between the performance of a system and its runtime activities. This process…
Advances in Internet-of-Things (IoT) technologies have prompted the integration of IoT devices with business processes (BPs) in many organizations across various sectors, such as manufacturing, healthcare and smart spaces. The proliferation…
Process mining techniques including process discovery, conformance checking, and process enhancement provide extensive knowledge about processes. Discovering running processes and deviations as well as detecting performance problems and…
Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-centric event data contain events associated with multiple…
Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify…
Process models generated through process mining depict the as-is state of a process. Through annotations with metrics such as the frequency or duration of activities, these models provide generic information to the process analyst. To…
The idea of automatizing the assessment of objectoriented design is not new. Different approaches define and apply their own quality models, which are composed of single metrics or combinations thereof, to operationalize software design.…
Predictive Process Monitoring aims to forecast the future progress of process instances using historical event data. As predictive process monitoring is increasingly applied in online settings to enable timely interventions, evaluating the…