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Computational Geometry

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.

By vega
(253 views)

Sketching and Nearest Neighbor Search (2)

Sketching and Nearest Neighbor Search (2)

Sketching and Nearest Neighbor Search (2). Alex Andoni (Columbia University). Sketching. short bit-strings given and , should be able to estimate some function of and , norm: words Decision version: given in advance… , norm: bits. 010110. 010101. Estimate.

By brinly
(324 views)

Spectral Partitioning for Metrics*

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

By monisha
(130 views)

Lecture 10: Sketching S3: Nearest Neighbor Search

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

By duman
(70 views)

Indexing similarity for efficient search in multimedia databases

Indexing similarity for efficient search in multimedia databases

Indexing similarity for efficient search in multimedia databases . Tom áš Skopal Dept . of Software Engineering , MFF UK SIRET research group, http://siret.ms.mff.cuni.cz. Similarity search. subject : content - based retrieval

By kacia
(157 views)

Collectively Representing Semi-Structured Data from the Web

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

By camdyn
(150 views)

BLAST addendum

BLAST addendum

BLAST addendum. Sushmita Roy BMI/CS 576. How does BLAST scan the database?. Two indices Preprocess to divide the database into words Query word list. NP NS NT NW NY. MF NY T…. NP GAT…. QECFT…. Database sequence. At Query time.

By jess
(86 views)

Leveraging Big Data: Lecture 12

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).

By gerik
(110 views)

Fast Two-Sided Error-Tolerant Search

Fast Two-Sided Error-Tolerant Search

Fast Two-Sided Error-Tolerant Search. Hannah Bast, Marjan Celikik University of Freiburg, Germany KEYS 2010. Motivation. Handling uncertainty in text search is important. Query side – users make mistakes typing the query Either due to mistyping

By cleta
(92 views)

Next Generation of Search

Next Generation of Search

Next Generation of Search. Fran Álvarez. Agenda. Background Zmart Search Demo Conclusions. Background. Content Management is everywhere Search of contents is key (Almost) All CMS and ECM provide Full Text Search but … Is it any good ? Let’s focus on Alfresco. Alfresco.

By tokala
(76 views)

Alex Andoni (MSR SVC)

Alex Andoni (MSR SVC)

Sketching, Sampling and other Sublinear Algorithms: Euclidean space: dimension reduction and NNS. Alex Andoni (MSR SVC). A Sketching Problem. Sketching: :objects short bit-strings given and should be able to deduce if and are “similar” Why?

By garvey
(151 views)

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

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.

By hien
(68 views)

Topic-sensitive PageRank

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.

By lesley
(191 views)

BLAST addendum

BLAST addendum

BLAST addendum. Sushmita Roy BMI/CS 576. How does BLAST scan the database?. Two indices Preprocess to divide the database into words Query word list. NP NS NT NW NY. MF NY T…. NP GAT…. QECFT…. Database sequence. At Query time.

By galena
(64 views)

Perfsonar LS scalability issues

Perfsonar LS scalability issues

Perfsonar LS scalability issues. Common LS queries. The most common LS query will likely be: “find me the service_accesspoint for X” Samples: Find me the topology server for ESnet Find me all pSB MAs for community “LHC” Find me all pSB MAs with throughput data for host X

By airlia
(87 views)

2000

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.

By shirin
(110 views)

Overview of RISOT: Retrieval of Indic Script OCR’d Text

Overview of RISOT: Retrieval of Indic Script OCR’d Text

Overview of RISOT: Retrieval of Indic Script OCR’d Text. Utpal Garain Indian Statistical Institute, Kolkata Tamaltaru Pal Indian Statistical Institute, Kolkata Jiaul Paik Indian Statistical Institute, Kolkata Kripa Ghosh Indian Statistical Institute, Kolkata

By hamish
(199 views)

C20.0046: Database Management Systems Lecture #5

C20.0046: Database Management Systems Lecture #5

C20.0046: Database Management Systems Lecture #5. Matthew P. Johnson Stern School of Business, NYU Spring, 2005. Crew. StudioName. Crew_ID. Miramax. C1. Miramax. C2. Disney. C1. Converting weak ESs – differences. Atts of Crew Rel are: attributes of Crew

By phila
(100 views)

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

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

Lattice QCD Data Grid Middleware: Meta Data Catalog (MDC) and Simple Replica Catalog (RC) Demo. M. Sato, for ILDG Middleware WG ILDG 5 , Dec, 03, 2004. Grid-of-Grids on MDC. Clients can access multiple MDC’s at different sites

By betty
(104 views)

GRAIL: Scalable Reachability Index for Large Graphs 	VLDB2010

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

By otylia
(105 views)

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