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The Architecture of PIER: an InternetScale Query Processor
 In CIDR
, 2005
"... This paper presents the architecture of PIER , an Internetscale query engine we have been building over the last three years. PIER is the first generalpurpose relational query processor targeted at a peertopeer (p2p) architecture of thousands or millions of participating nodes on the Internet. ..."
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Cited by 88 (8 self)
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This paper presents the architecture of PIER , an Internetscale query engine we have been building over the last three years. PIER is the first generalpurpose relational query processor targeted at a peertopeer (p2p) architecture of thousands or millions of participating nodes on the Internet. It supports massively distributed, databasestyle dataflows for snapshot and continuous queries. It is intended to serve as a building block for a diverse set of Internetscale informationcentric applications, particularly those that tap into the standardized data readily available on networked machines, including packet headers, system logs, and file names
SIA: secure information aggregation in sensor networks
 Proc. of of ACM SenSys 2003
, 2003
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Estimating the sortedness of a data stream
 In Proceedings of the ACMSIAM Symposium on Discrete Algorithms
, 2007
"... The distance to monotonicity of a sequence is the minimum number of edit operations required to transform the sequence into an increasing order; this measure is complementary to the length of the longest increasing subsequence (LIS). We address the question of estimating these quantities in the one ..."
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Cited by 24 (2 self)
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The distance to monotonicity of a sequence is the minimum number of edit operations required to transform the sequence into an increasing order; this measure is complementary to the length of the longest increasing subsequence (LIS). We address the question of estimating these quantities in the onepass data stream model and present the first sublinear space algorithms for both problems. We first present O ( √ n)space deterministic algorithms that approximate the distance to monotonicity and the LIS to within a factor that is arbitrarily close to 1. We also show a lower bound of Ω(n) on the space required by any randomized algorithm to compute the LIS (or alternatively the distance from monotonicity) exactly, demonstrating that approximation is necessary for sublinear space computation; this bound improves upon the existing lower bound of Ω ( √ n) [LNVZ06]. Our main result is a randomized algorithm that uses only O(log 2 n) space and approximates the distance to monotonicity to within a factor that is arbitrarily close to 4. In contrast, we believe that any significant reduction in the space complexity for approximating the length of the LIS is considerably hard. We conjecture that any deterministic (1 + ɛ) approximation algorithm for LIS requires Ω ( √ n) space, and as a step towards this conjecture, prove a space lower bound of Ω ( √ n) for a restricted yet natural class of deterministic algorithms. 1
Smoothed Analysis: Motivation and Discrete Models
 Proc. of WADS 2003
, 2003
"... Abstract. In smoothed analysis, one measures the complexity of algorithms assuming that their inputs are subject to small amounts of random noise. In an earlier work (Spielman and Teng, 2001), we introduced this analysis to explain the good practical behavior of the simplex algorithm. In this paper, ..."
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Cited by 6 (0 self)
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Abstract. In smoothed analysis, one measures the complexity of algorithms assuming that their inputs are subject to small amounts of random noise. In an earlier work (Spielman and Teng, 2001), we introduced this analysis to explain the good practical behavior of the simplex algorithm. In this paper, we provide further motivation for the smoothed analysis of algorithms, and develop models of noise suitable for analyzing the behavior of discrete algorithms. We then consider the smoothed complexities of testing some simple graph properties in these models. 1
Verifying Server Computation
"... Abstract. In many scenarios, clients receive the results of computation which has been performed by a remote server. An example of such a setting is the thirdparty publishing model, in which a server answers arbitrary database queries posed by clients with data originally provided by a third party, ..."
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Cited by 1 (0 self)
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Abstract. In many scenarios, clients receive the results of computation which has been performed by a remote server. An example of such a setting is the thirdparty publishing model, in which a server answers arbitrary database queries posed by clients with data originally provided by a third party, who does not participate in the protocol. Previous work in this scenario has addressed range queries, but there has not previously existed a general solution for detecting cheating on the part of the server for arbitrary queries. We propose different two techniques for approximate proof construction to detect server misbehavior: spot checking and transformationbased checks. We develop proof constructions, using the spotchecking paradigm, for arbitrary database queries. We also offer an illustrative example of a use of the transformationbased check technique. 1