'Query point' diaporamas de présentation

Algorithm Design Meets Big Data

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Point Location Problem in Computational Geometry

Point Location Problem in Computational Geometry. I4B01 ??? Ref: Computational Geometry, Preparata and Shamos, Chap. 2. Topics. Point Location Problem for Polygon Planar Subdivision. Point in Polygon. Single-shot approach Division wedges for the convex inclusion.

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MindReader: Querying databases through multiple examples

MindReader: Querying databases through multiple examples. Yoshiharu Ishikawa (Nara Institute of Science and Technology, Japan) Ravishankar Subramanya (Pittsburgh Supercomputing Center) Christos Faloutsos (Carnegie Mellon University). Outline. Background & Introduction Query by Example

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The Curse of Dimensionality

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Approximate Nearest Subspace Search with Applications to Pattern Recognition

Approximate Nearest Subspace Search with Applications to Pattern Recognition. Ronen Basri , Tal Hassner , Lihi Zelnik -Manor presented by Andrew Guillory and Ian Simon. The Problem. Given n linear subspaces S i :. The Problem. Given n linear subspaces S i : And a query point q :.

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Continuous Reverse Nearest Neighbor Monitoring

Continuous Reverse Nearest Neighbor Monitoring. Tian Xia, Donghui Zhang College of Computer and Information Science Northeastern University. Outline. Background and Definitions Related Work The Monitoring Region Query Processing Experimental Results Conclusions. Static NN and RNN Query.

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Efficient Algorithms for Finding Optimal Meeting Point on Road Networks

Efficient Algorithms for Finding Optimal Meeting Point on Road Networks. Da Yan, Zhou Zhao and Wilfred Ng The Hong Kong University of Science and Technology. Outline. Introduction Related Work Baseline Algorithm Convex-Hull-Based Pruning Fast Greedy Algorithm Experiments Conclusion.

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Reverse Furthest Neighbors in Spatial Databases

Bin Yao, Feifei Li, Piyush Kumar Presenter: Lian Liu. Reverse Furthest Neighbors in Spatial Databases. OUTLINE. Introduction Related Work Algorithms PFC (Progressive Furthest Cell) CHFC (Convex Hull Furthest Cell) Experiment Discussion. INTRODUCTION.

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An Efficient and Scalable Approach to CNN Queries in a Road Network

VLDB 2005. An Efficient and Scalable Approach to CNN Queries in a Road Network. Hyung-Ju Cho and Chin-Wan Chung Dept. of EECS, KAIST. Contents. Introduction Related Work Nearest Neighbor Search (NN Search) Continuous Nearest Neighbor Search (CNN Search) Performance Study Conclusions.

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Multidimensional Indexes [14]

Multidimensional Indexes [14]. Motivation. Examined indexes when search keys are in 1-D space Many interesting applications have search keys in multi-dimensional spaces Queries beyond exact match and range queries Partial match Nearest neighbor Where-am-I Drill-down and roll-up queries

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Supervised Learning Techniques over Twitter Data

Supervised Learning Techniques over Twitter Data. Kleisarchaki Sofia. Supervised Learning Algorithms - Process. Problem. Identification of required data. Data pre-processing. Def. of training set. Algorithm Selection. Parameter Tuning. Training. Evaluation with test set. ok?. no.

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Constant-Round Private Database Queries

Constant-Round Private Database Queries. Nenad Dedic and Payman Mohassel. Boston University. UC Davis. Outline. Introduction Element rank protocol Other protocols Equivalence to one-round PIR Open problems. q = Q(x). y. x. Server. Client. Dec(a) = f(x,y). a = A(q,y).

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Succinct Orthogonal Range Search Structures on a Grid with Applications to Text Indexing

Succinct Orthogonal Range Search Structures on a Grid with Applications to Text Indexing. Prosenjit Bose, Carleton University Meng He , Unversity of Waterloo Anil Maheshwari and Pat Morin, Carleton University. 2D Orthogonal Range Search. A fundamental geometric query problem

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Random projection trees and low dimensional manifolds

Yoav Freund, Sanjoy Dasgupta University of California, San Diego 2008. 2013. 01.07( ? ) Jeonbuk National Univ. DBLAB ???. Random projection trees and low dimensional manifolds. Contents. Introduction Detailed overview An RP-Tree-MAX adapts to assouad dimension.

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Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality

Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. Piotr Indyk, Rajeev Motwani. The 30 th annual ACM symposium on theory of computing 1998. Problems. Nearest neighbor (NN) problem:

232 views • 13 slides