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Query evaluation techniques for large databases
 ACM COMPUTING SURVEYS
, 1993
"... Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of objectoriented and extensible database systems will not solve this problem. On ..."
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Cited by 767 (11 self)
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relational database systems, including iterative execution of complex query evaluation plans, the duality of sort and hashbased set matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.
Parallel database systems: the future of high performance database systems
 COMMUNICATIONS OF THE ACM
, 1992
"... Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional sharednothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews t ..."
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Cited by 641 (13 self)
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Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional sharednothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews
Kademlia: A Peertopeer Information System Based on the XOR Metric
, 2002
"... We describe a peertopeer system which has provable consistency and performance in a faultprone environment. Our system routes queries and locates nodes using a novel XORbased metric topology that simplifies the algorithm and facilitates our proof. The topology has the property that every message ..."
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Cited by 834 (3 self)
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message exchanged conveys or reinforces useful contact information. The system exploits this information to send parallel, asynchronous query messages that tolerate node failures without imposing timeout delays on users.
Improving generalization with active learning
 Machine Learning
, 1994
"... Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples ..."
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Cited by 544 (1 self)
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neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers "useful. " We test our implementation, called an SGnetwork, on three domains and observe significant improvement
A Framework for Dynamic Graph Drawing
 CONGRESSUS NUMERANTIUM
, 1992
"... Drawing graphs is an important problem that combines flavors of computational geometry and graph theory. Applications can be found in a variety of areas including circuit layout, network management, software engineering, and graphics. The main contributions of this paper can be summarized as follows ..."
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Cited by 628 (44 self)
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as follows: ffl We devise a model for dynamic graph algorithms, based on performing queries and updates on an implicit representation of the drawing, and we show its applications. ffl We present several efficient dynamic drawing algorithms for trees, seriesparallel digraphs, planar stdigraphs, and planar
NonMalleable Cryptography
 SIAM Journal on Computing
, 2000
"... The notion of nonmalleable cryptography, an extension of semantically secure cryptography, is defined. Informally, in the context of encryption the additional requirement is that given the ciphertext it is impossible to generate a different ciphertext so that the respective plaintexts are related. ..."
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Cited by 480 (20 self)
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system users. Our cryptosystem is the first proven to be secure against a strong type of chosen ciphertext attack proposed by Rackoff and Simon, in which the attacker knows the ciphertext she wishes to break and can query the decryption oracle on any ciphertext other than the target.
SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR
 SUBMITTED TO THE ANNALS OF STATISTICS
, 2007
"... We exhibit an approximate equivalence between the Lasso estimator and Dantzig selector. For both methods we derive parallel oracle inequalities for the prediction risk in the general nonparametric regression model, as well as bounds on the ℓp estimation loss for 1 ≤ p ≤ 2 in the linear model when th ..."
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Cited by 472 (11 self)
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We exhibit an approximate equivalence between the Lasso estimator and Dantzig selector. For both methods we derive parallel oracle inequalities for the prediction risk in the general nonparametric regression model, as well as bounds on the ℓp estimation loss for 1 ≤ p ≤ 2 in the linear model when
Efficient noisetolerant learning from statistical queries
 JOURNAL OF THE ACM
, 1998
"... In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” learning algorithms in the most general way, we formalize a new but related model of learning from stat ..."
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Cited by 353 (5 self)
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statistical queries. Intuitively, in this model, a learning algorithm is forbidden to examine individual examples of the unknown target function, but is given access to an oracle providing estimates of probabilities over the sample space of random examples. One of our main results shows that any class
Oracle
"... The Blink project’s ambitious goal is to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. Blink is a new DBMS aimed primarily at readmostly BI query processing that exploits scaleout of commodity multico ..."
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scans (portions of) the data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two IBM accelerator products generally available since March 2011. We are now
Approximate distance oracles
, 2004
"... Let G = (V, E) be an undirected weighted graph with V  = n and E  = m. Let k ≥ 1 be an integer. We show that G = (V, E) can be preprocessed in O(kmn 1/k) expected time, constructing a data structure of size O(kn 1+1/k), such that any subsequent distance query can be answered, approximately, in ..."
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Cited by 273 (9 self)
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strictly smaller than 2k + 1. The space requirement of our algorithm is, therefore, essentially optimal. The most impressive feature of our data structure is its constant query time, hence the name “oracle”. Previously, data structures that used only O(n 1+1/k) space had a query time of Ω(n 1/k). Our
Results 1  10
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