Related papers: Process Mining for Python (PM4Py): Bridging the Ga…
We describe how Python can be leveraged to streamline the curation, modelling and dissemination of drug discovery data as well as the development of innovative, freely available tools for the related scientific community. We look at various…
The widespread development and adoption of open-source software have built an ecosystem for open development and collaboration. In this ecosystem, individuals and organizations collaborate to create high-quality software that can be used by…
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential equation simulation. PNMs naturally incorporate prior…
In the contemporary world, with the incubation of advanced technologies and tremendous outbursts of research works, analyzing big data to incorporate research strategies becomes more helpful using the tools and techniques presented in the…
New and upgraded radio interferometers produce data at massive rates and will require significant improvements in analysis techniques to reach their promised levels of performance in a routine manner. Until these techniques are fully…
Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by…
The rise in usage of Large Language Models to near ubiquitousness in recent years has risen societal concern about their applications in decision-making contexts, such as organizational justice or healthcare. This, in turn, poses questions…
Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for…
Compendium Manager is a command-line tool written in Python to automate the provisioning, launch, and evaluation of bioinformatics pipelines. Although workflow management tools such as Snakemake and Nextflow enable users to automate the…
Business process management (BPM) and accompanying systems aim at enabling enterprises to become adaptive. In spite of the dependency of enterprises on secure business processes, BPM languages and techniques provide only little support for…
AI Planning, Machine Learning and Process Mining have so far developed into separate research fields. At the same time, many interesting concepts and insights have been gained at the intersection of these areas in recent years. For example,…
Considering processes of a business in a recommender system is highly advantageous. Although most studies in the business process analysis domain are of descriptive and predictive nature, the feasibility of constructing a process-aware…
We investigate the potential of using Large Language Models (LLM) to support process model creation in organizational contexts. Specifically, we carry out a case study wherein we develop and test an LLM-based chatbot, PRODIGY (PROcess…
Process mining techniques can help organizations to improve their operational processes. Organizations can benefit from process mining techniques in finding and amending the root causes of performance or compliance problems. Considering the…
Recent advances in Artificial Intelligence (AI) have made algorithmic trading play a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for…
Background: The study of genome-scale metabolic models and their underlying networks is one of the most important fields in systems biology. The complexity of these models and their description makes the use of computational tools an…
Unraveling the causal relationships among the execution of process activities is a crucial element in predicting the consequences of process interventions and making informed decisions regarding process improvements. Process discovery…
Analyzing process data at varying levels of granularity is important to derive actionable insights and support informed decision-making. Object-Centric Event Data (OCED) enhances process mining by capturing interactions among events and…
Software development processes are subject to variations in time and space, variations that can originate from learning effects, differences in application domains, or a number of other causes. Identifying and analyzing such differences is…
The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of…