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Arbre kd

Publications associées (15)

Cluster-and-Conquer: When Randomness Meets Graph Locality

Anne-Marie Kermarrec

K-Nearest-Neighbors (KNN) graphs are central to many emblematic data mining and machine-learning applications. Some of the most efficient KNN graph algorithms are incremental and local: they start from a random graph, which they incrementally improve by tr ...

IEEE COMPUTER SOC2021

Smaller, Faster & Lighter KNN Graph Constructions

Rachid Guerraoui, Anne-Marie Kermarrec

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard’s index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and s ...

2020

Fingerprinting Big Data: The Case of KNN Graph Construction

Rachid Guerraoui, Anne-Marie Kermarrec

We propose fingerprinting, a new technique that consists in constructing compact, fast-to-compute and privacy-preserving binary representations of datasets. We illustrate the effectiveness of our approach on the emblematic big data problem of K-Nearest-Nei ...

2019

Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Anastasia Ailamaki, Raja Appuswamy, Matthaios Alexandros Olma, Odysseas Papapetrou

Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget ...

IEEE2019

HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces

Akhil Arora

Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due to the curse of dimensionality. A flavor of approximation is, therefore, necessary to practically solve the problem of nearest neighbor search. In this pap ...

ASSOC COMPUTING MACHINERY2018

Improving Spatial Data Processing by Clipping Minimum Bounding Boxes

Anastasia Ailamaki, Eleni Tzirita Zacharatou, Darius Sidlauskas

The majority of spatial processing techniques rely heavily on the idea of approximating each group of spatial objects by their minimum bounding box (MBB). As each MBB is compact to store (requiring only two multi-dimensional points) and intersection tests ...

IEEE2018

Algorithmic Resource Verification

Ravichandhran Kandhadai Madhavan

Static estimation of resource utilisation of programs is a challenging and important problem with numerous applications. In this thesis, I present new algorithms that enable users to specify and verify their desired bounds on resource usage of functional p ...

EPFL2017

A maximum-likelihood approach for building cell-type trees by lifting

Bernard Moret, Philipp Bucher, Yu Lin, Mingfu Shao, Nishanth Ulhas Nair

Background: In cell differentiation, a less specialized cell differentiates into a more specialized one, even though all cells in one organism have (almost) the same genome. Epigenetic factors such as histone modifications are known to play a significant r ...

2016

RUBIK: Efficient Threshold Queries on Massive Time Series

Anastasia Ailamaki, Thomas Heinis, Farhan Tauheed, Eleni Tzirita Zacharatou

An increasing number of applications from finance, meteorology, science and others are producing time series as output. The analysis of the vast amount of time series is key to understand the phenomena studied, particularly in the simulation sciences, wher ...

2015

A 65k pixel, 150k frames-per-second camera with global gating and micro-lenses suitable for fluorescence lifetime imaging

Edoardo Charbon, Claudio Bruschini, Samuel Burri, Francesco Regazzoni, François Eric Powolny

This paper presents our work on a 65k pixel single-photon avalanche diode (SPAD) based imaging sensor realized in a 0.35 mu m standard CMOS process. At a resolution of 512 by 128 pixels the sensor is read out in 6.4ps to deliver over 150k monochrome frames ...

Spie-Int Soc Optical Engineering2014

Accelerating Spatial Range Queries

Anastasia Ailamaki, Thomas Heinis, Farhan Tauheed

It is increasingly common for domain scientists to use computational tools to build and simulate spatial models of the phenomena they are studying. The spatial models they build are more and more detailed as well as dense and are consequently difficult to ...

2013

Accelerating Range Queries For Brain Simulations

Henry Markram, Anastasia Ailamaki, Felix Schürmann, Thomas Heinis, Farhan Tauheed

Neuroscientists increasingly use computational tools to build and simulate models of the brain. The amounts of data involved in these simulations are immense and the importance of their efficient management is primordial. One particular problem in analyzin ...

2012

Distributed processing of continuous sliding-window k-NN queries for data stream filtering

Karl Aberer

A sliding-window k-NN query (k-NN/w query) continuously monitors incoming data stream objects within a sliding window to identify k closest objects to a query. It enables effective filtering of data objects streaming in at high rates from potentially distr ...

Springer Verlag2011

Delineating Trees in Noisy 2D Images and 3D Image Stacks

Pascal Fua, François Fleuret, Engin Türetken, Germán González Serrano

We present a novel approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, our method builds a set of candidate trees over many different subsets of point ...

2010

Graph coloring with cardinality constraints on the neighborhoods

Dominique de Werra, Bernard Ries

Extensions and variations of the basic problem of graph coloring are introduced. The problem consists essentially in finding in a graph G a k-coloring, i.e., a partition V-1,...,V-k of the vertex set of G such that, for some specified neighborhood (N) over ...

2009