Year: 2011
Type: conference-paper
Abstract: Scientific research has moved from an isolated environment into a collaborated culture due to the data explosion and the experiment complexity. Several scientific workflow systems have been developed to support scientists to conduct the scientific analysis and create knowledge. They provide the ability to automatically record provenance, the process that led to a particular data, which can be used by \nother scientists for better understanding, re-using and verifying a data product to generate new knowledge. However, most of the existing scientific workflow systems \nprovide poor query capability for users to effectively find relevant provenance information. It is not a trivial task due to the complexity of the workflow and the size of the provenance graphs. In this work, we propose a system architecture for provenance exploration in which querying, navigation and visualization methods are combined together. It enables effective and efficient knowledge discovery \namong various provenance information generated by different workflow runs.
Authors: Liu, Qing, Zhang, Ji, James, Forbes, Xu, Kai, Dinesh, Nair
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7010900011