Algorithms and lower bounds for sublinear processing of long sequences and large data

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As more and more applications are generating and storing massive amounts of data, the processing of large data is becoming crucial in many fields. In this proposal we are interested in processing large amounts of data in the presence of resource constraints. In particular, we would like to develop algorithms in the property testing, streaming, and sensor network aggregation models, allowing time and/or space sublinear in the size of the input. Clearly, such tight restrictions imply that the solutions provided by our algorithms cannot be fully accurate in many cases; one of our goals is to explore the tradeoff between accuracy and efficiency in these models. We will focus on problems related to properties of sequences, but will explore other areas as we develop more techniques.