The document outlines the various stages of data management from collection to analysis and how visualization can aid at each stage. It discusses using visualizations like trees, graphs and networks to plan the data management process and understand relationships between datasets during collection. During collection, visualization can also help understand results from data mining algorithms and the structure of files. At the assurance stage, graphical summaries produced from statistical operations on datasets can be used for security and quality checks. Visualizations like multi-dimensional maps and volumes can then be used for process monitoring and quality assurance. Visualization is also discussed as a way to understand and analyze large web archives by examining file hierarchies and types during preservation. Finally, different visualizations can be applied during analysis depending on user needs

3. PLANNING
Creation of a road map that involve all the process need to
be done for proper Data Management from Collection to
Analyses of the data. It can be represented by chronological
hierarchical visualization like Tree or radial or composition
of both
In this visualization, each process is subdivided into different
task and sub task. Thus, making the flow of work clear and
understandable

4. COLLECTION
For the large collection of datasets, visualization can aid in:
To provide a global and visual map that would represent the overall
organization of this collection with the possibility to obtain local details about
its similarities and other features. An example of such can be force directed
layouts or graphs. Each graph is a dataset and each edge is a link on the
dataset basis on its features like proximity graphs. Linked data makes it
easy to understand the relationship between different datasets
It can aid in understanding the results of various data mining algorithms like
clustering and machine learning techniques like classifications used for
processing the data. Once the data is properly classified or categorized, a
quick overview can be done by RadViz .
Once, we have these datasets, visualization can be used for understanding
the structure of these files, finding potential formatting and structural
incompatibilities. My current work is on this theme(thus can’t send the link
publically) and also, we can use machine learning for the same purpose

5. Get pictures from intern proposal

6. ASSURANCE
Once, we have these datasets, visualization can be used for
understanding the structure of these files, finding potential formatting
and structural incompatibilities. My current work is on this theme(thus
can’t send the link publically) and also, we can use machine learning
for the same purpose

7. DESCRIPTION
Do statistical operations on datasets and produce the graphical summaries. This
summary actually can be used for security and quality checks in preservation of data.
At this stage, multi-dimension visualizations can be implemented for process
monitoring and quality assurance. Example multi-variate visualizations like 1D- time, 2-
D maps and 3D-volumes.

8. PRESERVING
Visualization can help in understanding and analysing the large
collection of web archives, by examining directory hierarchy and file
type distribution. http://bl.ocks.org/mbostock/raw/4063582/

9. ANALYSIS
It is a very user intuitive and interactive process. Depending
on the need of user, different visualization can be implied.
Visual Analytics on the different facts of data can be
employed, which helps in compressing the knowledge and
only focus on what is important for them.
http://www.cotrino.com/lifespan/