'Query time' diaporamas de présentation

Spectral Partitioning for Metrics*

Spectral Partitioning for Metrics*. Alexandr Andoni (Columbia) Assaf Naor (Princeton) Aleksandar Nikolov (Toronto) Ilya Razenshteyn (MSR Redmond) Erik Waingarten (Columbia) * and nearest neighbor search too. Approximate Near Neighbors (ANN). Dataset: points in a metric space

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Leveraging Big Data: Lecture 12

http://www.cohenwang.com/edith/bigdataclass2013. Leveraging Big Data: Lecture 12. Edith Cohen Amos Fiat Haim Kaplan Tova Milo. Instructors:. Today. All-Distances Sketches Applications of All-Distance sketches Back to linear sketches (random linear transformations).

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John Kubiatowicz and Anthony D. Joseph Electrical Engineering and Computer Sciences

EECS 262a Advanced Topics in Computer Systems Lecture 17 Comparison of Parallel DB, CS, MR and Jockey October 30 th , 2013. John Kubiatowicz and Anthony D. Joseph Electrical Engineering and Computer Sciences University of California, Berkeley http://www.eecs.berkeley.edu/~kubitron/cs262.

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GRAIL: Scalable Reachability Index for Large Graphs VLDB2010

GRAIL: Scalable Reachability Index for Large Graphs VLDB2010. Vineet Chaoji Mohammed J. Zaki. 1. INTRODUCTION. Problem Definition

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C20.0046: Database Management Systems Lecture #5

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Computational Geometry

Computational Geometry. Chapter 6 Point Location. Problem Definition. Preprocess a planar map S. Given a query point p , report the face of S containing p. Goal: O( n )-size data structure that enables O(log n ) query time.

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Indexing similarity for efficient search in multimedia databases

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Collectively Representing Semi-Structured Data from the Web

Collectively Representing Semi-Structured Data from the Web. Bhavana Dalvi , William W. Cohen and Jamie Callan Language Technologies Institute Carnegie Mellon University Paper ID : 02 . This work is supported by Google and the Intelligence Advanced Research Projects Activity

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Sketching and Nearest Neighbor Search (2)

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Next Generation of Search

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Alex Andoni (MSR SVC)

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Fast Two-Sided Error-Tolerant Search

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Topic-sensitive PageRank

Topic-sensitive PageRank. Taher H. Haveliwala Stanford University, Stanford, CA WWW 2012 Jan 16, 2013 Hee -gook Jun. Outline. Introduction Topic-sensitive PageRank Experiments Conclusion. Simplified PageRank. Link-based ranking algorithm. 5. PageRank Value =. 10. A. 5.

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Lecture 10: Sketching S3: Nearest Neighbor Search

Lecture 10: Sketching S3: Nearest Neighbor Search. Plan. PS2 due yesterday, 7pm Sketching Nearest Neighbor Search Scriber?. Sketching. short bit-strings given and , should be able to estimate some function of and With success probability ( ) , norm: words

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Perfsonar LS scalability issues

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M. Sato, for ILDG Middleware WG ILDG 5 , Dec, 03, 2004

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BLAST addendum

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BLAST addendum

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2000

0.03. 0.025. Euclidean DTW. CBF Dataset. 0.02. 0.015. 0.01. 0.005. 0. Out-of-Sample Error Rate. 0.5. Two-Pat Dataset. 0.4. 0.3. 0.2. 0.1. 0. 0. 1000. 2000. 3000. 4000. 5000. 6000. Increasingly Large Training Sets.

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