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CONCLUSIONS
9 Conclusions
In this section we present our final conclusions about visualization and interaction
with real-time data for the stock market, and answer the research questions for this
thesis.
In this thesis work we have explored the possibilities for visualizing and interacting
with large amounts of real-time data for the stock market with the aid of a visual
analytics tool. To be able to answer the primary research question of this thesis, we
will first answer the two additional research questions.
Who are the potential users of a real-time visual analytics tool within the
stock market?
The potential users within the stock market for a real-time visual analytics tool primarily consist of advisory and discretionary stock brokers and asset managers. It
became clear in the interviews conducted in the early stages of the thesis work that
both of these professional groups need to perform analysis of the large amounts of
real-time data which they handle daily to be able to make decisions on how to invest.
A potential secondary user group for real-time visual analytics tools has also been
identified; the analysts, who perform and supply the primary user group with more
thorough analysis of the real-time data.
What are the similarities and differences between visualizing and
interacting with static versus real-time data?
Visualizing and interacting with real-time data is in many ways similar to visualization and interaction with static data. However, when it comes to visualizing realtime data it becomes very significant to provide a context for the real-time data with
historic data, since you have no use of the real-time data if you have nothing to compare it with. When the prototypes were developed, it also became obvious that it was
very important to visually separate historic data from real-time data in a clear and
efficient way, so the users can differentiate between the different kinds of data.
Distinguishing the values that are updated from the rest of the values in the
visualization is vital. When you visualize large amounts of real-time data the interface
risks to become cluttered. It is therefore important to tone down the graphic design for
an interface handling real-time data.
When user tests were performed on the developed prototypes it was proven that the
same interaction techniques can be used for interaction with real-time data as for static
data. This is due to the fact that the movement in the plots is not that significant in
real-time, which was discovered early on in the development of the prototypes. Since
the primary user group does not look at data containing a shorter time span than 30
minutes, the movement of the data in the visualizations will not be so significant. It
however remains unclear if the interaction techniques used for static data also will fit
real-time data for other domains than the one we have studied.
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