Structural Feature Based Anomaly Detection for Packed Executable Identification - Computational Intelligence in Security for Information Systems - page 248

again. In grid environment, such architecture can be used for all applications from
different science spheres which have the character of a parametric study.
Actually, the research community needs not only “traditional” batch computations
of huge bunches of data but also the ability to perform complex data processing; this
requires capabilities like on-line access to databases, interactivity, fine real-time job
control, sophisticated visualization and data management tools (also in real time),
remote control and monitoring. The user can completely control the job during
execution and change the input parameters, while the execution is still running. Both
tools, the tool for submission designed before and continued sequential visualization
tool, provide complete solution of the specific main problem in HPC environment.
The position of the visualization tool as a visual control process is shown in figure 1.
Astrophysics scientists are able to run scientific simulations, data analysis, and
visualization through web browsers.
Through Earth and astronomical science gateway scientists are able to import they
sophisticated scripts by which the VT can be activated as well, as the output from
workflow executions without writing any web related code [5].
2.1 VT as a New Discovery for Presenting Academic Research Results
Advance in sciences and engineering results in high demand of tools for high-
performance large-scale visual data exploration and analysis. For example,
astronomical scientists can now study evolution of all Solar systems on numerous
astronomical simulations. These simulations can generate large amount of data,
possibly with high resolution (in three-dimensional space), and long time series.
Single-system visualization software running on commodity machines cannot scale up
to the large amount of data generated by these simulations. To address this problem, a
lot of different grid-based visualization frameworks have been developed for time-
critical, interactively controlled file-set transfer for visual browsing of spatially and
temporally large datasets in a grid environment. To address the problem, many
frameworks for grid and cloud based visualization are used. We can go through
evolution of sophisticated grid-based visualization frameworks with actualized
functionality, for example, Reality Grid, UniGrid and TerraGrid.
All of the frameworks have been included in the visualization . Frameworks were
created during grid-based projects and create new features for presentations of the
academic research results in visualization. Visualization resources enabled by the
astronomical science gateway the top of research experiences.
Multiple visualizations generated from a common model will improve the process
of creation, reviewing and understanding of requirements. Visual representations,
when effective, provide cognitive support by highlighting the most relevant
interactions and aspects of a specification for a particular use. The goal of scientific
visualization is to help scientists view and better understand their data. This data can
come from experiments or from numerical simulations. Often the size and complexity
of the data makes them difficult to understand by direct inspection. Also, the data may
be generated at several times during an experiment or simulation and understanding
how the data varies with time may be difficult. Scientific visualization can help with
these difficulties by representing the data so that it may be viewed in its entirety. In
the case of time data varying in time, animations can be created that show this
variation in a natural way. Using virtual reality techniques, the data can be viewed