The notion of collaborative scientific workflow is coined to address the increasing need for collaborative data analytics. In collaborative environments, access control policies are necessary for controlling the sharing of workflows, data products, and provenance information among collaborating parties. In particular, the protection of workflow provenance is critical because it often encodes the detailed protocol of a scientific experiment and carries the intellectual property of the respective stakeholders. In addition, since scientific workflows often evolve quickly, the corresponding access control policies for workflow provenance have to evolve as well. It is important to ensure that the evolution of workflow provenance access control policies maintain certain properties, in order to guarantee the correctness and performance of the corresponding policy enforcement. In this paper, we 1) propose a role-based access control model for scientific workflow provenance; 2) define three quality requirements for scientific workflow provenance access control policies - consistency, completeness, and conciseness; 3) develop a mechanism mapping from specifications of workflows to their counterparts in a provenance that preserves such quality properties, and 4) conduct a case study on a scientific workflow for autism behavioral data analysis that demonstrates the feasibility of our proposed analysis algorithms.

Early intervention in autism, although deemed as essential, has high variance in the outcome attained, partially due to complex interaction between multitude of factors and variables involved, and the lack of systematic study to untangle their influences in the outcome. Therefore, pairing set of interventions with an individual children to cater for their need remains highly challenging. From the perspective of parents, unknown factors emanate from their unfamiliarity with what interventions are out there and why. From the perspective of caregivers, it is critical to understand unique attributes of the individual children develop over time. There is a scarcity of exploration of interactions between attributes specific to a child, family characteristics and therapeutic, medical and educational services. In this research, we aim to bridge the gap. In this study, we identify predictive features pertaining to each individual child and how they interact responding to different interventions and services. We have studied temporal data and model improvement/regression outcomes at different timestamped milestones and overlayed a model to aid parents and caregivers in coming up with pragmatic intervention plan. We propose a scientific workflow to automate the modeling process and rely on DATAVIEW to guarantee computational reproducibility and data fidelity. We use data collected by SFARI dataset for evaluation. To the best of our knowledge, this is first-time amalgamation between the Autism Health informatics community and the Workflow community; and this is the first-time study that combines prediction methods applied on Autism Spectrum Disorder (ASD) Phenotype data to provide guidance to parents and caregivers.