In the previous blog post we looked at the processing of multiple data streams and using the resulting sets of data (referred as data ponds) as subjects of data analysis. This is often an effective approach to help with understanding specific phenomena, as a big-picture created from a number of series can reveal trends and patterns. Such a big-picture can however serve additional purposes, as it also can be used to establish a relevant context for the processing of an individual stream (that may, but doesn’t have to, be part of the data used to create this context). The results from analysis templates implementing such an approach can be effectively visualized, with focus on the individual series as a clearly distinguished foreground and context from multiple series presented as a background.

In the examples below we again use Dow Jones components, this time with the 5-year history of their daily close prices. Figure 1 includes data series for all stocks in the scope of Dow Jones data pond, without any transformations applied and with focus on Microsoft (MSFT).

Figure 1. Five-year history of Dow Jones components with focus on Microsoft stock daily close prices (SVG)

This chart is not very useful, since the value range of MSFT price is small compared to the value range of the chart (determined by all series) and thus the foreground series seems rather flat. This problem can be addressed by transforming all the series in the data pond, as illustrated in Figure 2, where series’ value ranges were normalized to [0, 1] (we used this transformation also in the first post of the series).

Figure 2. Dow Jones background with value ranges normalized to [0,1] and Microsoft stock as the foreground (SVG)

Another type of transformation, often applied in practice, is based on calculating change from a previous value, or one at a selected point in time. Figure 3 includes results of such an experiment with the change (percentage) calculated against the first data point in the time frame (5 years earlier). In addition to MSFT stock, this chart also covers IBM, so that their performance can be easily compared.

Figure 3. Price changes (%) of Microsoft and IBM stock over 5-year interval with Dow Jones components background (SVG)

In the examples above we focused on the visualization of individual series against the context built from multiple series. But obviously, the foreground-vs-background pattern can also be used for analysis, as the focus series can be analyzed in the context of all the others. Such analysis doesn’t have to be limited to a single series, but can focus on a subset, e.g. patients meeting specified criteria. The context build from multiple series may also be of different types - it can be personal (e.g. latest workout metrics vs results collected over time), local (e.g. sales from a specific location vs company aggregation) or even global (e.g. our performance in the competitive landscape). We’ll get to such scenarios, in different application domains, in the future posts.