18 research outputs found

    A Note on Randomized Streaming Space Bounds for the Longest Increasing Subsequence Problem

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    The deterministic space complexity of approximating the length of the longest increasing subsequence of a stream of N integers is known to be Theta~(sqrt N). However, the randomized complexity is wide open. We show that the technique used in earlier work to establish the Omega(sqrt N) deterministic lower bound fails strongly under randomization: specifically, we show that the communication problems on which the lower bound is based have very efficient randomized protocols. The purpose of this note is to guide and alert future researchers working on this very interesting problem

    Lower Bounds for Multi-Pass Processing of Multiple Data Streams

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    This paper gives a brief overview of computation models for data stream processing, and it introduces a new model for multi-pass processing of multiple streams, the so-called mp2s-automata. Two algorithms for solving the set disjointness problem wi th these automata are presented. The main technical contribution of this paper is the proof of a lower bound on the size of memory and the number of heads that are required for solvin g the set disjointness problem with mp2s-automata

    Lower Bounds on Streaming Algorithms for Approximating the Length of the Longest Increasing Subsequence

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    Communication Steps for Parallel Query Processing

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    We consider the problem of computing a relational query qq on a large input database of size nn, using a large number pp of servers. The computation is performed in rounds, and each server can receive only O(n/p1āˆ’Īµ)O(n/p^{1-\varepsilon}) bits of data, where Īµāˆˆ[0,1]\varepsilon \in [0,1] is a parameter that controls replication. We examine how many global communication steps are needed to compute qq. We establish both lower and upper bounds, in two settings. For a single round of communication, we give lower bounds in the strongest possible model, where arbitrary bits may be exchanged; we show that any algorithm requires Īµā‰„1āˆ’1/Ļ„āˆ—\varepsilon \geq 1-1/\tau^*, where Ļ„āˆ—\tau^* is the fractional vertex cover of the hypergraph of qq. We also give an algorithm that matches the lower bound for a specific class of databases. For multiple rounds of communication, we present lower bounds in a model where routing decisions for a tuple are tuple-based. We show that for the class of tree-like queries there exists a tradeoff between the number of rounds and the space exponent Īµ\varepsilon. The lower bounds for multiple rounds are the first of their kind. Our results also imply that transitive closure cannot be computed in O(1) rounds of communication

    Edit Distance: Sketching, Streaming and Document Exchange

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    We show that in the document exchange problem, where Alice holds xāˆˆ{0,1}nx \in \{0,1\}^n and Bob holds yāˆˆ{0,1}ny \in \{0,1\}^n, Alice can send Bob a message of size O(K(logā”2K+logā”n))O(K(\log^2 K+\log n)) bits such that Bob can recover xx using the message and his input yy if the edit distance between xx and yy is no more than KK, and output "error" otherwise. Both the encoding and decoding can be done in time O~(n+poly(K))\tilde{O}(n+\mathsf{poly}(K)). This result significantly improves the previous communication bounds under polynomial encoding/decoding time. We also show that in the referee model, where Alice and Bob hold xx and yy respectively, they can compute sketches of xx and yy of sizes poly(Klogā”n)\mathsf{poly}(K \log n) bits (the encoding), and send to the referee, who can then compute the edit distance between xx and yy together with all the edit operations if the edit distance is no more than KK, and output "error" otherwise (the decoding). To the best of our knowledge, this is the first result for sketching edit distance using poly(Klogā”n)\mathsf{poly}(K \log n) bits. Moreover, the encoding phase of our sketching algorithm can be performed by scanning the input string in one pass. Thus our sketching algorithm also implies the first streaming algorithm for computing edit distance and all the edits exactly using poly(Klogā”n)\mathsf{poly}(K \log n) bits of space.Comment: Full version of an article to be presented at the 57th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2016
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