QUAL : A Provenance-Aware Quality Model

The research described here is supported by the award made by the RCUK Digital Economy program to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Scalable And Secure Provenance Querying For Scientific Workflows And Its Application In Autism Study

In the era of big data, scientific workflows have become essential to automate scientific experiments and guarantee repeatability. As both data and workflow increase in their scale, requirements for having a data lineage management system commensurate with the complexity of the workflow also become necessary, calling for new scalable storage, query, and analytics infrastructure. This system that manages and preserves the derivation history and morphosis of data, known as provenance system, is essential for maintaining quality and trustworthiness of data products and ensuring reproducibility of scientific discoveries. With a flurry of research and increased adoption of scientific workflows in processing sensitive data, i.e., health and medication domain, securing information flow and instrumenting access privileges in the system have become a fundamental precursor to deploying large-scale scientific workflows. That has become more important now since today team of scientists around the world can collaborate on experiments using globally distributed sensitive data sources. Hence, it has become imperative to augment scientific workflow systems as well as the underlying provenance management systems with data security protocols. Provenance systems, void of data security protocol, are susceptible to vulnerability. In this dissertation research, we delineate how scientific workflows can improve therapeutic practices in autism spectrum disorders. The data-intensive computation inherent in these workflows and sensitive nature of the data, necessitate support for scalable, parallel and robust provenance queries and secured view of data. With that in perspective, we propose $OPQL^{Pig}$, a parallel, robust, reliable and scalable provenance query language and introduce the concept of access privilege inheritance in the provenance systems. We characterize desirable properties of role-based access control protocol in scientific workflows and demonstrate how the qualities are integrated into the workflow provenance systems as well. Finally, we describe how these concepts fit within the DATAVIEW workflow management system

Toward a common standard for data and specimen provenance in life sciences

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.</p