A Visual Stack Based Paradigm for Visualization Environments

We present a new visual paradigm for Visualization Systems, inspired by stack-based programming. Most current implementations of Visualization systems are based on directional graphs. However directional graphs as a visual representation of execution, though initially quite intuitive, quickly grow cumbersome and difficult to follow under complex examples. Our system presents the user with a simple and compact methodology of visually stacking actions directly on top of data objects as a way of creating filter scripts. We explore and address extensions to the basic paradigm to allow for: multiple data input or data output objects to and from execution action modules, execution thread jumps and loops, encapsulation, and overall execution control. We exploit the dynamic nature of current computer graphic interfaces by utilizing features such as drag-and-drop, color emphasis and object animation to indicate action, looping, message/parameter passing; to furnish an overall better understanding of the resulting laid out execution scripts

Big Data and Analysis of Data Transfers for International Research Networks Using NetSage

Modern science is increasingly data-driven and collaborative in nature. Many scientific disciplines, including genomics, high-energy physics, astronomy, and atmospheric science, produce petabytes of data that must be shared with collaborators all over the world. The National Science Foundation-supported International Research Network Connection (IRNC) links have been essential to enabling this collaboration, but as data sharing has increased, so has the amount of information being collected to understand network performance. New capabilities to measure and analyze the performance of international wide-area networks are essential to ensure end-users are able to take full advantage of such infrastructure for their big data applications. NetSage is a project to develop a unified, open, privacy-aware network measurement, and visualization service to address the needs of monitoring today's high-speed international research networks. NetSage collects data on both backbone links and exchange points, which can be as much as 1Tb per month. This puts a significant strain on hardware, not only in terms storage needs to hold multi-year historical data, but also in terms of processor and memory needs to analyze the data to understand network behaviors. This paper addresses the basic NetSage architecture, its current data collection and archiving approach, and details the constraints of dealing with this big data problem of handling vast amounts of monitoring data, while providing useful, extensible visualization to end users

A tool for metadata analysis

We describe a Web-based metadata quality tool that provides statistical descriptions and visualisations of Dublin Core metadata harvested via the OAI protocol. The lightweight nature of development allows it to be used to gather contextualized requirements and some initial user feedback is discussed