AF: Small: Efficient Algorithms for Querying Noisy Distributed/Streaming Datasets

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This project aims to study the design of efficient query algorithms for noisy datasets in distributed and streaming applications. Noisy data is universal in today's world. Imprecise and varying references to the same real-world entities are ubiquitous in scientific and commercial databases. This noise poses significant obstructions to accurate data analytics. As an example of "noisy data," consider YouTube videos. YouTube tracks the views of individual videos. However, there are frequently many similar versions of the same event and answering a basic question such as "How many people viewed this event?" is challenging using current techniques. This project will provide new techniques and insights to combat the noisy nature of large datasets, and hence will enhance our ability to process the ever-increasing quantity of business and scientific data. The products of this project will be integrated into a trilogy of graduate and undergraduate courses on algorithms, databases, and data mining. The PI will disseminate research outcomes by giving talks at conferences/workshops, universities, industrial labs, as well as online media.More technically, this project tries to answer the following question: can we run distributed and streaming algorithms directly on the noisy datasets, resolve the noise "on the fly", and retain communication and space efficiency compared with the noise-free setting? The PI plans to study statistical, relational and graph problems. This project has the potential to impact a wide range of active research areas in theoretical computer science, including distributed and streaming algorithms, group testing, compressed sensing, communication complexity, clustering, and locality sensitive hashing.