On Wednesday, April 30, 2014 11:30:41 AM UTC-5, Douglas Bates wrote: > > It is sometimes difficult to obtain realistic "Big" data sets. A > Revolution Analytics blog post yesterday > > > http://blog.revolutionanalytics.com/2014/04/predict-which-shoppers-will-become-repeat-buyers.html > > mentioned the competition > > http://www.kaggle.com/c/acquire-valued-shoppers-challenge > > with a very large data set, which may be useful in looking at performance > bottlenecks. > > You do need to sign up to be able to download the data and you must agree > only to use the data for the purposes of the competition and to remove the > data once the competition is over. >
I did download the largest of the data files which consists of about 350 million records on 11 variables in CSV format. The compressed file is around 2.6 GB, uncompressed it would be over 22GB. Fortunately, the GZip package allows for working with the compressed file for sequential access. Most of the variables are what I would call categorical (stored as integer values) and could be represented as a pooled data vector. One variable is a date and one is a price which could be stored as an integer value (number of cents) or as a Float32. So the first task would be parsing all those integers and creating a binary representation. This could be done using a Relational DataBase but I think that might be overkill for a static table like this. I have been thinking of storing each column as a memory-mapped array in a format like pooled data. That is, store only the indices into a table of values so that the indices can be represented as whatever size of unsigned int is large enough for the table size. To work out the storage format I should first determine the number of distinct values for each categorical variable. I was planning on using split(readline(gzfilehandle,",")) applying int() to the appropriate fields and storing the values in a Set or perhaps an IntSet. Does this seem like a reasonable way to start?
