相关论文: Empirical Analysis on Comparing the Performance of…
In the era of big data, conventional RDBMS models have become impractical for handling colossal workloads. Consequently, NoSQL databases have emerged as the preferred storage solutions for executing processing-intensive Online Analytical…
Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain. There is high diversity considering the variety of data collected from healthcare processes: operational…
Process mining discovers and analyzes a process model from historical event logs. The prior art methods use the key attributes of case-id, activity, and timestamp hidden in an event log as clues to discover a process model. However, a user…
Complex Event Recognition (CER) systems are a prominent technology for finding user-defined query patterns over large data streams in real time. CER query evaluation is known to be computationally challenging, since it requires maintaining…
Process mining enables business owners to discover and analyze their actual processes using event data that are widely available in information systems. Event data contain detailed information which is incredibly valuable for providing…
SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ERP is enormous. Thus, the identification of the processes…
Modern information systems are able to collect event data in the form of event logs. Process mining techniques allow to discover a model from event data, to check the conformance of an event log against a reference model, and to perform…
Process analytic approaches play a critical role in supporting the practice of business process management and continuous process improvement by leveraging process-related data to identify performance bottlenecks, extracting insights about…
Process mining is moving beyond mining traditional event logs and nowadays includes, for example, data sourced from sensors in the Internet of Things (IoT). The volume and velocity of data generated by such sensors makes it increasingly…
Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting in a type of user interaction data often called click data.…
We propose a new framework that focuses on on-site entities in the digital twin, a pairing of the real world and digital space. Characteristics include active sensing to generate event logs, spatial and temporal partitioning of complex…
In the present paper, we propose a Neuroelectromagnetic Ontology Framework (NOF) for mining Event-related Potentials (ERP) patterns as well as the process. The aim for this research is to develop an infrastructure for mining, analysis and…
Operational processes in production, logistics, material handling, maintenance, etc., are supported by cyber-physical systems combining hardware and software components. As a result, the digital and the physical world are closely aligned,…
Recent advancements in Artificial Intelligence (AI), particularly Large Language Models (LLMs), have enhanced organizations' ability to reengineer business processes by automating knowledge-intensive tasks. This automation drives digital…
Modern Internet of Things (IoT) systems are equipped with a large quantity of sensors providing real-time data about the current operations of their components, which is crucial for the systems' internal control systems and processes.…
The Alpha algorithm was the first process discovery algorithm that was able to discover process models with concurrency based on incomplete event data while still providing formal guarantees. However, as was stated in the original paper,…
Process mining extends far beyond process discovery and conformance checking, and also provides techniques for bottleneck analysis and organizational mining. However, these techniques are mostly backward-looking. PMSD is a web application…
Performance analysis in process mining aims to provide insights on the performance of a business process by using a process model as a formal representation of the process. Such insights are reliably interpreted by process analysts in the…
The uptake of Artificial Intelligence (AI) impacts the way we work, interact, do business, and conduct research. However, organizations struggle to apply AI successfully in industrial settings where the focus is on end-to-end operational…
Performance appraisal (PA) is an important HR process to periodically measure and evaluate every employee's performance vis-a-vis the goals established by the organization. A PA process involves purposeful multi-step multi-modal…