We were discussing our recommender software yesterday and the day before.The recommender might get geographical location of the user, the time of the day and search terms from the owner. These are helpful to predict the activity the owner may take. The recommender does not need to rebuild the activity log for the owner but it can perform correlations for the window it operates on. If it helps to build the activity log for year to date, then the correlation can become easier by translating to queries and data mining over the activity log.
The activity log has a natural snowflake schema and works well for warehouse purposes as well. Addition to the activity log may be very granular or coarse or both and these may be defined and labeled as per information found in the past or input from user. the activity log has a progressive order as in time series database and allows standard query operator in date ranges. By virtue of allowing user to accrue this log from anywhere and on any device, this database is well suited to be in the cloud. public cloud databases or virtual data warehouse are well suited for this purpose. when the recommender performs correlations for the owner, it discovers activities by the owner. These activities are recorded in this database. if the recommender needs to search for date ranges it can quickly use the activity log it helped build. Activity log for the owner gives the most information about the owner and consequently helps with recommendations.
Since the recommender queries many data sources, it is not limited to the Activity Log but it eventually grows the Activity Log to be the source of truth about the owner.
#codingexercise

Segregate and Sort even and odd integers: 

List <int> SegregateAndSort (List <int> input) 

{ 

var odd = input.Select ( x => x % 2 == 1 ).ToList(); 

var even = input.Select ( x => x % 2 == 0).ToList (); 

return even.Sort ().Union (odd.Sort()).ToList (); 

} 
and test for our k-means classifier: https://github.com/ravibeta/cexamples/blob/master/classifiertest.c