| On the Anonymization of Workflow Provenance without Compromising the Transparency of Lineage | 0 | 0.34 | 2022 |
| On Discovering Data Preparation Modules Using Examples. | 0 | 0.34 | 2020 |
| Scalable Saturation of Streaming RDF Triples. | 0 | 0.34 | 2020 |
| Lineage-Preserving Anonymization of the Provenance of Collection-Based Workflows. | 0 | 0.34 | 2020 |
| Findable and reusable workflow data products: A genomic workflow case study | 0 | 0.34 | 2020 |
| On privacy-aware eScience workflows | 0 | 0.34 | 2020 |
| On the elicitation and annotation of business activities based on emails. | 1 | 0.38 | 2019 |
| Streaming saturation for large RDF graphs with dynamic schema information | 0 | 0.34 | 2019 |
| Mining Business Process Information from Email Logs for Business Process Models Discovery. | 0 | 0.34 | 2019 |
| Privacy-Preserving Data Analysis Workflows for eScience. | 0 | 0.34 | 2019 |
| Email Business Activities Extraction and Annotation. | 0 | 0.34 | 2018 |
| An Ontology-Based Approach for Mining Radicalization Indicators from Online Messages | 0 | 0.34 | 2018 |
| πSOD-M: building SOC applications in the presence of non-functional requirements. | 0 | 0.34 | 2018 |
| Quality Based Data Integration for Enriching User Data Sources in Service Lakes | 1 | 0.39 | 2018 |
| On Answering Why-Not Queries Against Scientific Workflow Provenance. | 0 | 0.34 | 2018 |
| A Domain-Independent Ontology for Capturing Scientific Experiments. | 0 | 0.34 | 2018 |
| Labelflow Framework For Annotating Workflow Provenance | 0 | 0.34 | 2018 |
| Mining Business Process Activities from Email Logs | 0 | 0.34 | 2017 |
| Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities. | 11 | 0.56 | 2017 |
| Keyword-Based Search of Workflow Fragments and Their Composition. | 1 | 0.35 | 2017 |
| SHARP: Harmonizing Galaxy and Taverna Workflow Provenance. | 0 | 0.34 | 2017 |
| Business Process Instances Discovery from Email Logs | 0 | 0.34 | 2017 |
| On Enriching User-Centered Data Integration Schemas in Service Lakes. | 1 | 0.35 | 2017 |
| SHARP: Harmonizing Cross-workflow Provenance. | 0 | 0.34 | 2017 |
| Multi-level clustering for extracting process-related information from email logs | 0 | 0.34 | 2017 |
| Static analysis of Taverna workflows to predict provenance patterns. | 3 | 0.37 | 2017 |
| A framework for mining process models from emails logs. | 0 | 0.34 | 2016 |
| Pay-as-you-go Data Integration: Experiences and Recurring Themes. | 4 | 0.42 | 2016 |
| Automatic vs Manual Provenance Abstractions: Mind the Gap. | 0 | 0.34 | 2016 |
| Yin & Yang: Demonstrating Complementary Provenance from noWorkflow & YesWorkflow. | 7 | 0.53 | 2016 |
| Efficient Feedback Collection for Pay-as-you-go Source Selection. | 6 | 0.47 | 2016 |
| Data Exchange with MapReduce: A First Cut. | 0 | 0.34 | 2016 |
| Protecting Privacy in the Cloud: Current Practices, Future Directions. | 6 | 0.41 | 2016 |
| Converting scripts into reproducible workflow research objects | 1 | 0.35 | 2016 |
| Linking prospective and retrospective provenance in scripts | 0 | 0.34 | 2015 |
| Modeling Evidence-Based Medicine Applications with Provenance Data in Pathways. | 0 | 0.34 | 2015 |
| Using a suite of ontologies for preserving workflow-centric research objects | 29 | 1.07 | 2015 |
| Retrospective Provenance Without a Runtime Provenance Recorder | 4 | 0.60 | 2015 |
| Enabling community-driven information integration through clustering | 3 | 0.38 | 2015 |
| Mining Workflow Repositories for Improving Fragments Reuse. | 2 | 0.36 | 2015 |
| The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web. | 7 | 0.56 | 2014 |
| Research Objects for Audio Processing: Capturing Semantics for Reproducibility | 1 | 0.38 | 2014 |
| Verification of Semantic Web Service Annotations Using Ontology-Based Partitioning | 6 | 0.47 | 2014 |
| UP & DOWN: Improving Provenance Precision by Combining Workflow- and Trace-Level Information. | 1 | 0.35 | 2014 |
| Common motifs in scientific workflows: An empirical analysis | 31 | 1.56 | 2014 |
| Annotating the Behavior of Scientific Modules Using Data Examples: A Practical Approach. | 1 | 0.36 | 2014 |
| Incrementally improving dataspaces based on user feedback | 18 | 0.68 | 2013 |
| PAV ontology: provenance, authoring and versioning. | 28 | 1.57 | 2013 |
| Enhancing and abstracting scientific workflow provenance for data publishing | 8 | 0.46 | 2013 |
| D-PROV: extending the PROV provenance model with workflow structure | 5 | 0.47 | 2013 |
