Obvious: a meta-toolkit to encapsulate information visualization toolkits. One toolkit to bind them all

This article describes “Obvious”: a meta-toolkit that abstracts and encapsulates information visualization toolkits implemented in the Java language. It intends to unify their use and postpone the choice of which concrete toolkit(s) to use later-on in the development of visual analytics applications. We also report on the lessons we have learned when wrapping popular toolkits with Obvious, namely Prefuse, the InfoVis Toolkit, partly Improvise, JUNG and other data management libraries. We show several examples on the uses of Obvious, how the different toolkits can be combined, for instance sharing their data models. We also show how Weka and RapidMiner, two popular machine-learning toolkits, have been wrapped with Obvious and can be used directly with all the other wrapped toolkits. We expect Obvious to start a co-evolution process: Obvious is meant to evolve when more components of Information Visualization systems will become consensual. It is also designed to help information visualization systems adhere to the best practices to provide a higher level of interoperability and leverage the domain of visual analytics

Performance of Julia for High Energy Physics Analyses

We argue that the Julia programming language is a compelling alternative to implementations in Python and C++ for common data analysis workflows in high energy physics. We compare the speed of implementations of different workflows in Julia with those in Python and C++. Our studies show that the Julia implementations are competitive for tasks that are dominated by computational load rather than data access. For work that is dominated by data access, we demonstrate an application with concurrent file reading and parallel data processing.Comment: 16 pages, 4 pages, 1 table, 3 code listing

Resource Oriented Architecture and REST

In light of the emerging discussion on Resource Oriented Architectures (ROA) and REST technology platform as solutions for Spatial Data distributed Infrastructures, the aim of this document is capture Resource Oriented Architectures principles and assess the feasibility as well as the advantages of using such approach compared with Service Oriented Architectures (SOA). Although most of the comments are domain independent we will use the INSPIRE (and the OGC services) infrastructure that is currently based on a SOA as domain for the comparative analysis.JRC.H.6-Spatial data infrastructure