17,024 research outputs found
A virtual workspace for hybrid multidimensional scaling algorithms
In visualising multidimensional data, it is well known that different types of algorithms to process them. Data sets might be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualization. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This suggests that hybrid combinations of appropriate algorithms might also successfully address other characteristics of data. This paper presents a system and framework in which a user can easily explore hybrid algorithms and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views
A visual workspace for constructing hybrid MDS algorithms and coordinating multiple views
Data can be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualisation. This has led to an abundance of often disparate algorithmic techniques. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This paper presents a system and framework in which a user can easily explore algorithms as well as their hybrid conjunctions and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views of algorithmic and visualisation components. We propose that our approach has two main benefits: significant improvements in run times of MDS algorithms can be achieved, and intermediate views of the data and the visualisation program structure can provide greater insight and control over the visualisation process
Somoclu: An Efficient Parallel Library for Self-Organizing Maps
Somoclu is a massively parallel tool for training self-organizing maps on
large data sets written in C++. It builds on OpenMP for multicore execution,
and on MPI for distributing the workload across the nodes in a cluster. It is
also able to boost training by using CUDA if graphics processing units are
available. A sparse kernel is included, which is useful for high-dimensional
but sparse data, such as the vector spaces common in text mining workflows.
Python, R and MATLAB interfaces facilitate interactive use. Apart from fast
execution, memory use is highly optimized, enabling training large emergent
maps even on a single computer.Comment: 26 pages, 9 figures. The code is available at
https://peterwittek.github.io/somoclu
Viewpoints: A high-performance high-dimensional exploratory data analysis tool
Scientific data sets continue to increase in both size and complexity. In the
past, dedicated graphics systems at supercomputing centers were required to
visualize large data sets, but as the price of commodity graphics hardware has
dropped and its capability has increased, it is now possible, in principle, to
view large complex data sets on a single workstation. To do this in practice,
an investigator will need software that is written to take advantage of the
relevant graphics hardware. The Viewpoints visualization package described
herein is an example of such software. Viewpoints is an interactive tool for
exploratory visual analysis of large, high-dimensional (multivariate) data. It
leverages the capabilities of modern graphics boards (GPUs) to run on a single
workstation or laptop. Viewpoints is minimalist: it attempts to do a small set
of useful things very well (or at least very quickly) in comparison with
similar packages today. Its basic feature set includes linked scatter plots
with brushing, dynamic histograms, normalization and outlier detection/removal.
Viewpoints was originally designed for astrophysicists, but it has since been
used in a variety of fields that range from astronomy, quantum chemistry, fluid
dynamics, machine learning, bioinformatics, and finance to information
technology server log mining. In this article, we describe the Viewpoints
package and show examples of its usage.Comment: 18 pages, 3 figures, PASP in press, this version corresponds more
closely to that to be publishe
Trajectory Clustering and an Application to Airspace Monitoring
This paper presents a framework aimed at monitoring the behavior of aircraft
in a given airspace. Nominal trajectories are determined and learned using data
driven methods. Standard procedures are used by air traffic controllers (ATC)
to guide aircraft, ensure the safety of the airspace, and to maximize the
runway occupancy. Even though standard procedures are used by ATC, the control
of the aircraft remains with the pilots, leading to a large variability in the
flight patterns observed. Two methods to identify typical operations and their
variability from recorded radar tracks are presented. This knowledge base is
then used to monitor the conformance of current operations against operations
previously identified as standard. A tool called AirTrajectoryMiner is
presented, aiming at monitoring the instantaneous health of the airspace, in
real time. The airspace is "healthy" when all aircraft are flying according to
the nominal procedures. A measure of complexity is introduced, measuring the
conformance of current flight to nominal flight patterns. When an aircraft does
not conform, the complexity increases as more attention from ATC is required to
ensure a safe separation between aircraft.Comment: 15 pages, 20 figure
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