The objective of this competition is to build a model, learned using historical data, that will determine an employee's access needs such that manual access transactions (grants and revokes) are minimized as the employee's attributes change over time. [This is a clustering/collaborative filtering exercise..this needs a little more context]. The model will take an employee attribute record and a resource code and will return true if the employee should be given access this resource and false if the employee should not be given access to this resource.
The measure of success is to minimize the cost of add/remove actions for all employees for a given time perdiod.
This data is non-confidential and suitable for public consumption.
Data Set #1: amzn-anon-access-samples-user-profile-history.zip
contains the set of attributes associated with each user; each column corresponds to a single attribute (the rows have a time dimension).
Data Set #2: amzn-anon-access-transaction-history.zip
contains the access transaction history (the rows have a time dimension).
Data Set #3: amzn-anon-samples-user-access-snapshot.zip
contains the user access snapshot at the beginning and the end of the transaction history (the rows have a time dimension; either 2011-11-01 or 2010-11-01).
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This competition is also coordinated with 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP),
September 23-26, 2012, Santander, Spain