Deepavali Bhagwat
| Email: |
 |
I received my PhD in September 2010. I now work for Hewlett Packard's StorageWorks
Division.
My PhD thesis focussed on scalable deduplication methods. I worked in the SSRC group in the Archival Storage project. My
advisor was Prof. Darrell Long.
- Deepavali Bhagwat, Kave Eshghi, Darrell D. E. Long, and Mark Lillibridge,
Extreme Binning: Scalable, Parallel Deduplication for Chunk-based File Backup,
In Proceedings of the 17th International Symposium
on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
(MASCOTS) 2009, London (pdf)
- Mark Lillibridge, Kave Eshghi, Deepavali Bhagwat, Vinay
Deolalikar, Greg Trezise, and Peter Campbell,
Sparse Indexing, Large Scale, Inline Deduplication Using
Sampling and Locality, In Proceedings of the Seventh USENIX
Conference on File and Storage Technologies (FAST) 2009, San Francisco, CA (pdf)
- Deepavali Bhagwat, Kave Eshghi, and Pankaj Mehra,
Content-based Document Routing and Index Partitioning for Scalable
Similarity-based Searches in a Large Corpus, In Proceedings of the Thirteenth ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining
(KDD) 2007, San Jose, CA (pdf)
- Deepavali Bhagwat, Kristal Pollack, Darrell D. E. Long, Thomas Schwarz S.J.,
Ethan L. Miller, and Jehan-François Pâris, Providing High Reliability in a Minimum
Redundancy Archival System, In Proceedings of the 14th International Symposium
on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
(MASCOTS) 2006, Monterey, CA (pdf)
- Deepavali Bhagwat and Neoklis Polyzotis, Searching a File System using Inferred Semantic Links
, ACM Hypertext 2005, Salzburg, Austria.(pdf)
- Deepavali Bhagwat, Laura Chiticariu, Gaurav Vijayvargiya and Wang-Chiew Tan,
An Annotation Management System for Relational Databases, VLDB
Journal Special Issue (Best papers of 2004).
(pdf)
- Deepavali Bhagwat, Laura Chiticariu, Gaurav Vijayvargiya and Wang-Chiew Tan,
An Annotation Management System for Relational Databases, International
Conference on Very Large Databases (VLDB) 2004, Toronto, Canada.
(pdf)