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The data which knowledge workers need to conduct their work is stored across an increasing number of  repositories and grows annually at a significant rate. It is therefore unreasonable to expect that knowledge  workers can efficiently search and identify what they need across a myriad of locations where upwards of  hundreds of thousands of items can be created daily. This paper describes a system which can observe user  activity and train models to predict which items a user will access in order to help knowledge workers discover  content. We specifically investigate network file systems and determine how well we can predict future access  to newly created or modified content. Utilizing file metadata to construct access prediction models, we show  how the performance of these models can be improved for shares demonstrating high collaboration among its  users. Experiments on eight enterprise shares reveal that models based on file metadata can achieve F scores  upwards of 99%. Furthermore, on an average, collaboration aware models can correctly predict nearly half of  new file accesses by users while ensuring a precision of 75%, thus validating that the proposed system can be  utilized to help knowledge workers discover new or modified content.

Access Prediction for Knowledge Workers in Enterprise Data Repositories