Scientific workflows are a popular mechanism for specifying and automating data-driven in
silico experiments. A significant aspect of their value lies in their potential to be reused. Once …

In this survey, we discuss the challenges of executing scientific workflows as well as existing
Machine Learning (ML) techniques to alleviate those challenges. We provide the context …

Recording the provenance of scientific computation results is key to the support of
traceability, reproducibility and quality assessment of data products. Several data models …

Workflow technology continues to play an important role as a means for specifying and
enacting computational experiments in modern science. Reusing and re-purposing …

A workflow-centric research object bundles a workflow, the provenance of the results
obtained by its enactment, other digital objects that are relevant for the experiment (papers …

How easy is it to reproduce the results found in a typical computational biology paper?
Either through experience or intuition the reader will already know that the answer is with …

Provenance models are crucial for describing experimental results in science. The W3C
Provenance Working Group has recently released the PROV family of specifications for …

Distributed information infrastructures are increasingly used in the geospatial domain. In the
infrastructures, data are being collected by distributed sensor services, served by distributed …

This paper presents an extension to the W3C PROV provenance model, aimed at
representing process structure. Although the modelling of process structure is out of the …

Big data management is no longer an issue for large enterprises only; it has also become a
challenge for small and middle-sized enterprises, too. Today, enterprises have to handle …