This paper introduces a novel visual analytics approach, DCPViz, to enable climate scientists to explore massive climate data interactively without requiring the upfront movement of massive data. Thus, climate scientists are afforded more effective approaches to support the identification of potential trends and patterns in climate projections and their subsequent impacts. We designed the DCPViz pipeline to fetch and extract NEX-DCP30 data with minimal data transfer from their public sources. We implemented DCPViz to demonstrate its scalability and scientific value and to evaluate its utility under three use cases based on different models and through domain expert feedback.

This paper proposes a visual analytics framework that addresses the complex user interactions required through a command-line interface to run analyses in distributed data analysis systems. The visual analytics framework facilitates the user to manage access to the distributed servers, incorporate data from the source, run data-driven analysis, monitor the progress, and explore the result using interactive visualizations. We provide a user interface embedded with generalized functionalities and access protocols and integrate it with a distributed analysis system. To demonstrate our proof of concept, we present two use cases from the earth science and Sustainable Human Building Ecosystem research domain.

Big data analytics traditionally involves download of massive amounts of datasets to common server/cluster for processing. Analytic process gets slower with increasing size of required data and network conditions. Data scientists also need explicit access to data locations to download required data. Explicit access to required data may not always be granted due to security reasons. To simplify and accelerate the analytics process on distributed big data with security considerations, we proposed the Virtual Information Fabric Infrastructure (VIFI) for data driven discoveries. Instead of moving large amounts of data to a common place of processing, VIFI allows automatic transfer of required analytics programs to the distributed data locations for in-place processing of relevant data. VIFI allows data scientists to conduct and coordinate complex analytics processes on distributed data repositories using containerization technology and open-source workflow design tools. VIFI alleviates users from having detailed knowledge of distributed data locations, as well as required dependencies, installation and configuration of analytical libraries. In this paper, we demonstrate our current and future work to improve the VIFI architecture using previous and additional uses cases, data management layer that simplifies search of relevant data sets through addition of metadata, integration with security policies at different institutions with the proposed VIFI security layer, and the use of a user-friendly web interface to carry different VIFI activities.

Big Data are revolutionizing nearly every aspect of the modern society. One area where this can have a profound positive societal impact is the field of Health Care Informatics (HCI), which faces many challenges. The key idea behind this study is: can we use some of the experience and technical and methodological solutions from the fields that have successfully adapted to the Big Data era, namely astronomy and space science, to help accelerate the progress of HCI? We illustrate this with examples from the Virtual Observatory framework, and the NCI EDRN project. An effective sharing and reuse of tools, methods, and experiences from different fields can save a lot of effort, time, and expense. HCI can thus benefit from the proven solutions to big data challenges from other domains.

In this paper, we describe an emergent tool called DAWN (short for "Distributed Analytics, Workflows and Numeric") which is a model for simulating, analyzing and optimizing system architectures for executing arbitrary data processing pipelines. As an example, we will apply DAWN to the investigation of a real-life Big Data use case in climate science: the evaluation of simulated rainfall characteristics using high-resolution observational data. We will show how DAWN can help in determining the optimal architecture, and science algorithms, to execute this case study analyzing distributed datasets, as a tradeoff between the overall time cost and the uncertainty of calculated metrics for model evaluation. We will also show how DAWN can guide architectural decisions for future research, specifically impacting how data should be generated and analyzed to cope with future projected data volumes.