BIG DATA VISUALIZATION METHODS - Part I

• TreeMap

This method is based on space-filling visualization of hierarchical data. And as follows from the definition, there is a strict requirement applied to data — data objects which must be hierarchically linked.

The Treemap is represented by a root rectangle, divided into groups, also represented by the smaller rectangles, which correspond to data objects from a set.

Free space on hard drive visualization, profitability from different organizations, and its affiliates are examples of this method. Treemap can be applied to large data volumes and its iteratively representing data layers for each level of the hierarchy. The analyst always can move forward to the next block to continue his/her research into more detailed data on the lower level of hierarchy in device resolution exciding. So, the large data volume criterion is satisfied. Because treemap based on shapes volume estimation, calculated from one or more data factors. In addition, every change in data is followed by a total repaint of the whole image for the currently visible level of the hierarchy. The image repainting doesn’t acquire by the changes of a higher level because the data it contains is not visible for an analyst. This method can only show two data factors so, I acquired the visualization. The factor used for a shape volume calculation is the first factor and the second factor is color which used for grouping shapes. Those shapes used for volume estimation which are must be presented by computable data types, so the criterion data variety is not met.

And the last criterion also cannot be satisfied, because Treemap only shows data representation at one moment in time.

Method advantages:

(i) Hierarchical grouping clearly shows data relations.

(ii) Extreme outliers are immediately visible using special color.

Method disadvantages:

(i) Data must be hierarchical even more, this method is for analyzing data sets where there is at least one important quantitative dimension with wide variations.

(ii) Not suitable for examining historical trends and time patterns.

• Circle Packing.

This method is a direct alternative to a treemap, besides the fact that as primitive shape it uses circles, which also can be included in circles from a higher hierarchy level. The main profit of this method is that possibly we can place and percept larger a number of objects, by using classical Treemap [3]. Because the circle packing method is based on the Treemap method, it has the same properties. So, we can assume that only large data volumes criterion is met by this method.

Method advantages:

(i) Space-efficient visualization method compared to Treemap.

Method disadvantages:

(ii) The same disadvantages as for Treemap Method.

• Sunburst.

This method is also an alternative to Treemap. But it uses Treemap visualization and converted to a polar coordinate system. In this method variable parameters are radius and arc length so; it is the main difference between these methods because in other methods variable parameters are width and height. Because of this, it allows us not to repaint the whole diagram upon data change. But changing its radius reason for only one sector containing new data. Therefore, using animation which can add dynamics to data, sunbursts can be adapted to show data dynamics, manipulating only with sunburst ray’s radius, so, it can be said, that data dynamics criterion is met.

Method advantages:

(i) Easily perceptible by most humans.

Method disadvantages:

(ii) The same disadvantages as for Treemap Method.

In my next article, I hope to discuss more methods that are used for big data visualization.