Deepavali Bhagwat is a PhD candidate working with Prof. Darrell Long in the Archival Storage project. Her research focuses on scalability issues with respect to data de-duplication, indexing and retrieval for archival and backup storage systems. She is also interesting in developing solutions that apply the right level of redundancy to de-duplicated data for high availability. Along with her advisor she also works with researchers at Hewlett Packard Laboratories, Palo Alto.

Publications

2009

• Deepavali Bhagwat, Kave Eshghi, Darrell D. E. Long, Mark Lillibridge, Extreme Binning: Scalable, Parallel Deduplication for Chunk-based File Backup , Proceedings of the 17th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2009), September 2009. [Deduplication]
• Mark Lillibridge, Kave Eshghi, Deepavali Bhagwat, Vinay Deolalikar, Greg Trezise, Peter Camble, Sparse Indexing: Large Scale, Inline Deduplication Using Sampling and Locality, Proceedings of the 7th USENIX Conference on File and Storage Technologies (FAST '09), February 2009. [Deduplication]

2007

• Deepavali Bhagwat, Kave Eshghi, Pankaj Mehra, Content-based Document Routing and Index Partitioning for Scalable Similarity-based Searches in a Large Corpus, Proceedings of the 13th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD '07), August 2007, pages 105-112. [Archival Storage] [Scalable Metadata Management] [Deduplication]

2006

• Deepavali Bhagwat, Kristal Pollack, Darrell D. E. Long, Thomas Schwarz, Ethan L. Miller, Jehan-François Pâris, Providing High Reliability in a Minimum Redundancy Archival Storage System, Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS '06), September 2006, pages 413-421. [Archival Storage] [Deduplication]