35,951 research outputs found
A Unified Approach To Collaborative Data Visualization
Much efforts have lately been concentrated on increasing the precision of recommendations following the Netflix Prize competition. Recently, many researchers and industries have noted that other factors like adequate presentation of the results can add more utility to a recommender system than slight improvement in the precision. In this paper, we suggest a methodology for user-friendly representation of recommendations to the end users. Our scheme unifies the two objectives of prediction and visualization in the core of a unique approach. Users and items are first embedded into a high dimensional latent feature space according to a predictor function, particularly designated to meet visualization requirements. The data is then projected into a -dimensional space by Curvilinear Component Analysis (CCA). CCA draws personalized Item Maps (PIMs) representing a small subset of items to the active user. The intra-item semantic correlations are preserved in PIMs which is inherited from the clustering property of the high-dimensional embedding space. Our prediction function and the projection method are both non-linear to increase the clarity of the maps and to limit the effect of projection error. The algorithms are tested on three versions of the MovieLens dataset and the Netflix dataset to show they combine good accuracy with satisfactory visual properties
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
User Preferences of Spatio-Temporal Referencing Approaches For Immersive 3D Radar Charts
The use of head-mounted display technologies for virtual reality experiences
is inherently single-user-centred, allowing for the visual immersion of its
user in the computer-generated environment. This isolates them from their
physical surroundings, effectively preventing external visual information cues,
such as the pointing and referral to an artifact by another user. However, such
input is important and desired in collaborative scenarios when exploring and
analyzing data in virtual environments together with a peer. In this article,
we investigate different designs for making spatio-temporal references, i.e.,
visually highlighting virtual data artifacts, within the context of
Collaborative Immersive Analytics. The ability to make references to data is
foundational for collaboration, affecting aspects such as awareness, attention,
and common ground. Based on three design options, we implemented a variety of
approaches to make spatial and temporal references in an immersive virtual
reality environment that featured abstract visualization of spatio-temporal
data as 3D Radar Charts. We conducted a user study (n=12) to empirically
evaluate aspects such as aesthetic appeal, legibility, and general user
preference. The results indicate a unified favour for the presented location
approach as a spatial reference while revealing trends towards a preference of
mixed temporal reference approaches dependent on the task configuration:
pointer for elementary, and outline for synoptic references. Based on immersive
data visualization complexity as well as task reference configuration, we argue
that it can be beneficial to explore multiple reference approaches as
collaborative information cues, as opposed to following a rather uniform user
interface design.Comment: 29 pages, 9 figures, 1 tabl
Virtue integrated platform : holistic support for distributed ship hydrodynamic design
Ship hydrodynamic design today is often still done in a sequential approach. Tools used for the different aspects of CFD (Computational Fluid Dynamics) simulation (e.g. wave resistance, cavitation, seakeeping, and manoeuvring), and even for the different levels of detail within a single aspect, are often poorly integrated. VIRTUE (the VIRtual Tank Utility in Europe) project has the objective to develop a platform that will enable various distributed CFD and design applications to be integrated so that they may operate in a unified and holistic manner. This paper presents an overview of the VIRTUE Integrated Platform (VIP), e.g. research background, objectives, current work, user requirements, system architecture, its implementation, evaluation, and current development and future work
ImageJ2: ImageJ for the next generation of scientific image data
ImageJ is an image analysis program extensively used in the biological
sciences and beyond. Due to its ease of use, recordable macro language, and
extensible plug-in architecture, ImageJ enjoys contributions from
non-programmers, amateur programmers, and professional developers alike.
Enabling such a diversity of contributors has resulted in a large community
that spans the biological and physical sciences. However, a rapidly growing
user base, diverging plugin suites, and technical limitations have revealed a
clear need for a concerted software engineering effort to support emerging
imaging paradigms, to ensure the software's ability to handle the requirements
of modern science. Due to these new and emerging challenges in scientific
imaging, ImageJ is at a critical development crossroads.
We present ImageJ2, a total redesign of ImageJ offering a host of new
functionality. It separates concerns, fully decoupling the data model from the
user interface. It emphasizes integration with external applications to
maximize interoperability. Its robust new plugin framework allows everything
from image formats, to scripting languages, to visualization to be extended by
the community. The redesigned data model supports arbitrarily large,
N-dimensional datasets, which are increasingly common in modern image
acquisition. Despite the scope of these changes, backwards compatibility is
maintained such that this new functionality can be seamlessly integrated with
the classic ImageJ interface, allowing users and developers to migrate to these
new methods at their own pace. ImageJ2 provides a framework engineered for
flexibility, intended to support these requirements as well as accommodate
future needs
Collaborative e-science architecture for Reaction Kinetics research community
This paper presents a novel collaborative e-science architecture (CeSA) to address two challenging issues in e-science that arise from the management of heterogeneous distributed environments: (i) how to provide individual scientists an integrated environment to collaborate with each other in distributed, loosely coupled research communities where each member might be using a disparate range of tools; and (ii) how to provide easy access to a range of computationally intensive resources from a desktop. The Reaction Kinetics research community was used to capture the requirements and in the evaluation of the proposed architecture. The result demonstrated the feasibility of the approach and the potential benefits of the CeSA
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