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New Algorithms for Large Datasets and Distributions
In this dissertation, we make progress on certain algorithmic problems broadly over two computational models: the streaming model for large datasets and the distribution testing model for large probability distributions. First we consider the streaming model, where a large sequence of data items arrives one by one. The computer needs to make one pass over this sequence, processing every item quickly, in a limited space. In Chapter 2 motivated by a bioinformatics application, we consider the problem of estimating the number of low-frequency items in a stream, which has received only a limited theoretical work so far. We give an efficient streaming algorithm for this problem and show its complexity is almost optimal. In Chapter 3 we consider a distributed variation of the streaming model, where each item of the data sequence arrives arbitrarily to one among a set of computers, who together need to compute certain functions over the entire stream. In such scenarios combining the data at a computer is infeasible due to large communication overhead. We give the first algorithm for k-median clustering in this model. Moreover, we give new algorithms for frequency moments and clustering functions in the distributed sliding window model, where the computation is limited to the most recent W items, as the items arrive in the stream. In Chapter 5, in our identity testing problem, given two distributions P (unknown, only samples are obtained) and Q (known) over a common sample space of exponential size, we need to distinguish P = Q (output ‘yes’) versus P is far from Q (output ‘no’). This problem requires an exponential number of samples. To circumvent this lower bound, this problem was recently studied with certain structural assumptions. In particular, optimally efficient testers were given assuming P and Q are product distributions. For such product distributions, we give the first tolerant testers, which not only output yes when P = Q but also when P is close to Q, in Chapter 5. Likewise, we study the tolerant closeness testing problem for such product distributions, where Q too is accessed only by samples.
Gayen, Sutanu, "New Algorithms for Large Datasets and Distributions" (2019). ETD collection for University of Nebraska - Lincoln. AAI22584493.