1,532 research outputs found

    Quotient Complexity of Regular Languages

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    The past research on the state complexity of operations on regular languages is examined, and a new approach based on an old method (derivatives of regular expressions) is presented. Since state complexity is a property of a language, it is appropriate to define it in formal-language terms as the number of distinct quotients of the language, and to call it "quotient complexity". The problem of finding the quotient complexity of a language f(K,L) is considered, where K and L are regular languages and f is a regular operation, for example, union or concatenation. Since quotients can be represented by derivatives, one can find a formula for the typical quotient of f(K,L) in terms of the quotients of K and L. To obtain an upper bound on the number of quotients of f(K,L) all one has to do is count how many such quotients are possible, and this makes automaton constructions unnecessary. The advantages of this point of view are illustrated by many examples. Moreover, new general observations are presented to help in the estimation of the upper bounds on quotient complexity of regular operations

    Distributed Minimum Cut Approximation

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    We study the problem of computing approximate minimum edge cuts by distributed algorithms. We use a standard synchronous message passing model where in each round, O(logn)O(\log n) bits can be transmitted over each edge (a.k.a. the CONGEST model). We present a distributed algorithm that, for any weighted graph and any ϵ(0,1)\epsilon \in (0, 1), with high probability finds a cut of size at most O(ϵ1λ)O(\epsilon^{-1}\lambda) in O(D)+O~(n1/2+ϵ)O(D) + \tilde{O}(n^{1/2 + \epsilon}) rounds, where λ\lambda is the size of the minimum cut. This algorithm is based on a simple approach for analyzing random edge sampling, which we call the random layering technique. In addition, we also present another distributed algorithm, which is based on a centralized algorithm due to Matula [SODA '93], that with high probability computes a cut of size at most (2+ϵ)λ(2+\epsilon)\lambda in O~((D+n)/ϵ5)\tilde{O}((D+\sqrt{n})/\epsilon^5) rounds for any ϵ>0\epsilon>0. The time complexities of both of these algorithms almost match the Ω~(D+n)\tilde{\Omega}(D + \sqrt{n}) lower bound of Das Sarma et al. [STOC '11], thus leading to an answer to an open question raised by Elkin [SIGACT-News '04] and Das Sarma et al. [STOC '11]. Furthermore, we also strengthen the lower bound of Das Sarma et al. by extending it to unweighted graphs. We show that the same lower bound also holds for unweighted multigraphs (or equivalently for weighted graphs in which O(wlogn)O(w\log n) bits can be transmitted in each round over an edge of weight ww), even if the diameter is D=O(logn)D=O(\log n). For unweighted simple graphs, we show that even for networks of diameter O~(1λnαλ)\tilde{O}(\frac{1}{\lambda}\cdot \sqrt{\frac{n}{\alpha\lambda}}), finding an α\alpha-approximate minimum cut in networks of edge connectivity λ\lambda or computing an α\alpha-approximation of the edge connectivity requires Ω~(D+nαλ)\tilde{\Omega}(D + \sqrt{\frac{n}{\alpha\lambda}}) rounds

    A Call to Arms: Revisiting Database Design

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    Good database design is crucial to obtain a sound, consistent database, and - in turn - good database design methodologies are the best way to achieve the right design. These methodologies are taught to most Computer Science undergraduates, as part of any Introduction to Database class. They can be considered part of the "canon", and indeed, the overall approach to database design has been unchanged for years. Moreover, none of the major database research assessments identify database design as a strategic research direction. Should we conclude that database design is a solved problem? Our thesis is that database design remains a critical unsolved problem. Hence, it should be the subject of more research. Our starting point is the observation that traditional database design is not used in practice - and if it were used it would result in designs that are not well adapted to current environments. In short, database design has failed to keep up with the times. In this paper, we put forth arguments to support our viewpoint, analyze the root causes of this situation and suggest some avenues of research.Comment: Removed spurious column break. Nothing else was change
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