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Authors: Chetan Verma 1 ; Michael Hart 2 ; Sandeep Bhatkar 2 ; Aleatha Parker-Wood 2 and Sujit Dey 1

Affiliations: 1 University of California San Diego, United States ; 2 Symantec Research Labs, United States

ISBN: 978-989-758-096-3

ISSN: 2184-4992

Keyword(s): Information Retrieval, Machine Learning, Enterprise, File Systems.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Collaborative and Social Interaction ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Health Information Systems ; Human-Computer Interaction ; Information Systems Analysis and Specification ; Knowledge Management ; Model Driven Architectures and Engineering ; Ontologies and the Semantic Web ; Sensor Networks ; Signal Processing ; Society, e-Business and e-Government ; Soft Computing ; Web Information Systems and Technologies

Abstract: 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. (More)

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Paper citation in several formats:
Verma, C.; Hart, M.; Bhatkar, S.; Parker-Wood, A. and Dey, S. (2015). Access Prediction for Knowledge Workers in Enterprise Data Repositories.In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-096-3, ISSN 2184-4992, pages 150-161. DOI: 10.5220/0005374901500161

author={Chetan Verma. and Michael Hart. and Sandeep Bhatkar. and Aleatha Parker{-}Wood. and Sujit Dey.},
title={Access Prediction for Knowledge Workers in Enterprise Data Repositories},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},


JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Access Prediction for Knowledge Workers in Enterprise Data Repositories
SN - 978-989-758-096-3
AU - Verma, C.
AU - Hart, M.
AU - Bhatkar, S.
AU - Parker-Wood, A.
AU - Dey, S.
PY - 2015
SP - 150
EP - 161
DO - 10.5220/0005374901500161

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