Anurag Khandelwal

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We present BlowFish, a distributed data store that admits a smooth tradeoff between storage and performance for point queries. What makes BlowFish unique is its ability to navigate along this tradeoff curve efficiently at finegrained time scales with low computational overhead. Achieving a smooth and dynamic storage-performance tradeoff enables a wide range(More)
Queries involving Regular Expressions (RegEx) have a wide range of applications including document stores, bioinformatics and information retrieval. However, efficiently executing RegEx queries over large datasets remains a challenging task. Data scans do not scale well with input size; however, existing techniques that avoid data scans — referred to as(More)
We present ZipG, a distributed memory-efficient graph store for serving interactive graph queries. ZipG achieves memory efficiency by storing the input graph data using a compressed representation. What differentiates ZipG from other graph stores is its ability to execute a wide range of graph queries directly on this compressed representation. ZipG can(More)
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