74,674 research outputs found
Ringo: Interactive Graph Analytics on Big-Memory Machines
We present Ringo, a system for analysis of large graphs. Graphs provide a way
to represent and analyze systems of interacting objects (people, proteins,
webpages) with edges between the objects denoting interactions (friendships,
physical interactions, links). Mining graphs provides valuable insights about
individual objects as well as the relationships among them.
In building Ringo, we take advantage of the fact that machines with large
memory and many cores are widely available and also relatively affordable. This
allows us to build an easy-to-use interactive high-performance graph analytics
system. Graphs also need to be built from input data, which often resides in
the form of relational tables. Thus, Ringo provides rich functionality for
manipulating raw input data tables into various kinds of graphs. Furthermore,
Ringo also provides over 200 graph analytics functions that can then be applied
to constructed graphs.
We show that a single big-memory machine provides a very attractive platform
for performing analytics on all but the largest graphs as it offers excellent
performance and ease of use as compared to alternative approaches. With Ringo,
we also demonstrate how to integrate graph analytics with an iterative process
of trial-and-error data exploration and rapid experimentation, common in data
mining workloads.Comment: 6 pages, 2 figure
Carnap: an Open Framework for Formal Reasoning in the Browser
This paper presents an overview of Carnap, a free and open framework for the development of formal reasoning applications. Carnap’s design emphasizes flexibility, extensibility, and rapid prototyping. Carnap-based applications are written in Haskell, but can be compiled to JavaScript to run in standard web browsers. This combination of features makes Carnap ideally suited for educational applications, where ease-of-use is crucial for students and adaptability to different teaching strategies and classroom needs is crucial for instructors. The paper describes Carnap’s implementation, along with its current and projected pedagogical applications
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
An overview of decision table literature 1982-1995.
This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.
Virtual Reality Interactive Learning Environment
Open Building Manufacturing (ManuBuild) aims to promote the European construction industry beyond the state of the art. However, this requires the different stakeholders to be well informed of what ‘Open Building Manufacturing’ actually entails with respect to understanding the underlying concepts, benefits and risks. This is further challenged by the ‘traditional ways of learning’ which have been predominantly criticised for being entrenched in theories with little or no emphasis on practical issues.
Experiential learning has long been suggested to overcome the problems associated with the traditional ways of learning. In this respect, it has the dual benefit of appealing to adult learner's experience base, as well as increasing the likelihood of performance change through training. On-the-job-training (OJT) is usually sought to enable ‘experiential’ learning; and it is argued to be particularly effective in complex tasks, where a great deal of independence is granted to the task performer. However, OJT has been criticised for being expensive, limited, and devoid of the actual training context. Consequently, in order to address the problems encountered with OJT, virtual reality (VR) solutions have been proposed to provide a risk free environment for learning without the ‘do-or-die’
consequences often faced on real construction projects.
Since ManuBuild aims to promote the EU construction industry beyond the state of the art; training and education therefore needs also to go beyond the state of the art in order to meet future industry needs and expectations. Hence, a VR interactive learning environment was suggested for Open Building Manufacturing training to allow experiential learning to take place in a risk free environment, and consequently overcome the problems associated with OJT. This chapter discusses the development, testing, and validation of this prototype
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