
Compressed Counting
Counting is among the most fundamental operations in computing. For exam...
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Improved 3pass Algorithm for Counting 4cycles in Arbitrary Order Streaming
The problem of counting small subgraphs, and specifically cycles, in the...
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ONCE and ONCE+: Counting the Frequency of Timeconstrained Serial Episodes in a Streaming Sequence
As a representative sequential pattern mining problem, counting the freq...
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Flattened Exponential Histogram for Sliding Window Queries over Data Streams
The Basic Counting problem [1] is one of the most fundamental and critic...
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A decentralized algorithm for network node counting
Node counting on a graph is subject to some fundamental theoretical limi...
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Taming Discrete Integration via the Boon of Dimensionality
Discrete integration is a fundamental problem in computer science that c...
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SetSketch: Filling the Gap between MinHash and HyperLogLog
MinHash and HyperLogLog are sketching algorithms that have become indisp...
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Model Counting meets F0 Estimation
Constraint satisfaction problems (CSP's) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSP's and computation of zeroth frequency moments (F_0) for data streams. Our investigations lead us to observe striking similarity in the core techniques employed in the algorithmic frameworks that have evolved separately for model counting and F_0 computation. We design a recipe for translation of algorithms developed for F_0 estimation to that of model counting, resulting in new algorithms for model counting. We then observe that algorithms in the context of distributed streaming can be transformed to distributed algorithms for model counting. We next turn our attention to viewing streaming from the lens of counting and show that framing F_0 estimation as a special case of #DNF counting allows us to obtain a general recipe for a rich class of streaming problems, which had been subjected to casespecific analysis in prior works. In particular, our view yields a stateofthe art algorithm for multidimensional range efficient F_0 estimation with a simpler analysis.
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