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Incremental Discovery of Process Models Using Trace Fragments
Process discovery learns process models from event data and is a crucial discipline within process mining. Most existing approaches are fully...
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Contextual topic discovery using unsupervised keyphrase extraction and hierarchical semantic graph model
Recent technological advancements have led to a significant increase in digital documents. A document’s key information is generally represented by...
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Generative aptamer discovery using RaptGen
Nucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment...
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Automating Workflow/Pipeline Design
This chapter discusses the design of workflows (or pipelines), which represent solutions that involve more than one algorithm. This is motivated by...
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Explainable Machine Learning and Visual Knowledge Discovery
The importance of visual methods in machine learning (ML) as tools to increase the interpretability and validity of models, is growing. The visual...
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Basic and personalized pattern-based workflow fragments discovery
With an increasing number of scientific workflows accessible on public repositories, the mechanism for discovering and recommending workflow...
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Comparing Ordering Strategies for Process Discovery Using Synthesis Rules
Process discovery aims to learn process models from observed behaviors, i.e., event logs, in the information systems. The discovered models serve as...
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Evaluation of Machine Learning Techniques for Predicting Run Times of Scientific Workflow Jobs
Predicting execution time of computational jobs helps improve resource management, reduce execution cost, and optimize energy consumption. In this...
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Scalable Discovery and Continuous Inventory of Personal Data at Rest in Cloud Native Systems
Cloud native systems are processing large amounts of personal data through numerous and possibly multi-paradigmatic data stores (e.g., relational and...
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A transferable recommender approach for selecting the best density functional approximations in chemical discovery
Approximate density functional theory has become indispensable owing to its balanced cost–accuracy trade-off, including in large-scale screening. To...
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UCAD: commUnity disCovery method in Attribute-based multicoloreD networks
Many hierarchical methods for community detection in multicolored networks are capable of finding clusters when there are interslice correlation...
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On the Concept of Discovery Power of Enterprise Modeling Languages and Its Relation to Their Expressive Power
The paper introduces a new concept – discovery power – that can be used to characterize an enterprise modeling language. The concept is different...
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DualDNSMiner: A Dual-Stack Resolver Discovery Method Based on Alias Resolution
With the rapid development of IPv6 network applications, the transition to IPv6 dns has accelerated. In this process, dual-stack resolvers take on...
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Technologies for design-build-test-learn automation and computational modelling across the synthetic biology workflow: a review
Motivated by the need to parameterize and functionalize dynamic, multiscale simulations, as well as bridge the gap between advancing in silico and...
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Optimization of the Workflow in a BOINC-Based Desktop Grid for Virtual Drug Screening
This paper presents an analysis of a BOINC-based volunteer computing project SiDock@home. The project implements virtual drug screening. We analyse...
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Jeu de mots paronomasia: a StackOverflow-driven bug discovery approach
Locating bug code snippets (short for BugCode) has been a complex problem throughout the history of software security, mainly because the constraints...
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Replication and data management-based workflow scheduling algorithm for multi-cloud data centre platform
Scientific workflow applications have a large amount of tasks and data sets to be processed in a systematic manner. These applications benefit from...
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Automating Process Discovery Through Meta-learning
Analyzing event logs generated during the execution of digital processes, organizations can monitor the behavior of dysfunctional or unspecified...
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Creating Translucent Event Logs to Improve Process Discovery
Event logs capture information about executed activities. However, they do not capture information about activities that could have been performed,...
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Data driven discovery of systems of ordinary differential equations using nonconvex multitask learning
In physical sciences, dynamic systems are modeled using their parameters within governing equations that often form a system of ordinary differential...
