The graph is used for analysis of “Ad Targeting Data”. Relevant data is presented at campaign level. Targeted keywords and URLs are related in the graph.
The challenge here is that sometimes the data points to be shown on the graph can be about 1,76,000 which makes it extremely slow. Also calculations for 1,76,000 points is extremely time consuming.
So the solution proposed was to perform calculations as a back-end process. The calculations could be refreshed at any time, by the click of a button. The data requirements were sufficing with bg processing.
To give an idea of the calculations involved, the time taken by the bg process to be completed was 8 hours, on a dedicated server with 4GB RAM. Configurations are implemented to allow long running bg processes.
The data on graph is restricted by number of minimum related nodes, which can be specified by administrator. For eg: only those data points will be shown which are related to atleast 5 other keywords. The top matching results can also be filtered.