PARALLEL DATA LAB

Contact
In this section

Attribute-Based Naming

As storage capacity continues to increase, users find it increasingly difficult to manage their files using traditional directory heirarchies. Attribute-based naming systems can help, but rely on users and content analysis to provide attributes, with limited success. To make attribute-based naming more effective, we are developing techniques to gather attributes based on context analysis. These techniques use the system context of a user accessing files to gather attributes and create relationships between files. By combining these techniques with user and content-based assignment, we can increase both the quantity and quality of attributes available.

People

FACULTY

Greg Ganger

STUDENTS

Craig Soules

Publications

  • Using Provenance to Aid in Personal File Search. Sam Shah, Craig A. N. Soules, Gregory R. Ganger, Brian D. Noble. USENIX '07 Annual Technical Conference, Santa Clara, CA, June 17–22, 2007.
    Abstract / PDF [225K]

  • Using Context to Assist in Personal File Retrieval. Craig A. N. Soules. Carnegie Mellon University School of Computer Science Ph.D. Dissertation CMU-CS-06-147, August 25, 2006.
    Abstract / PDF [ 681K]

  • Connections: Using Context to Enhance File Search. Craig A. N. Soules, Gregory R. Ganger. SOSP'05, October 23–26, 2005, Brighton, United Kingdom. Supercedes Carnegie Mellon University Parallel Data Lab Technical Report CMU-PDL-05-105, April 2005.
    Abstract / PDF [300K]

  • Toward Automatic Context-based Attribute Assignment for Semantic File Systems. Craig A. N. Soules, Gregory R. Ganger. Carnegie Mellon University Parallel Data Laboratory Technical Report CMU-PDL-04-105. June 2004.
    Abstract / PDF [290K]

  • Why Can’t I Find My Files? New methods for automating attribute assignment. Craig A.N. Soules, Greg Ganger. Proceedings of the Ninth Workshop on Hot Topics in Operating systems, USENIX Association, May 2003. Also available as Carnegie Mellon University Technical Report CMU-CS-03-116, February 2003.
    Abstract / PDF [75K]

Acknowledgements

We thank the members and companies of the PDL Consortium: Bloomberg LP, Everpure, Google, Jane Street, LayerZero Labs, Meta, Microsoft Research, Oracle Corporation, Salesforce, Uber, and Western Digital for their interest, insights, feedback, and support.