Publications associées à Dimension (data warehouse)

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

EPFL2023

Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

Christoph Koch, Sachin Basil John, Zhekai Jiang, Peter Lindner

We present Sudokube, a novel system that supports interactive speed querying on high-dimensional data using partially materialized data cubes. Given a storage budget, it judiciously chooses what projections to precompute and materialize during cube constru ...

Association for Computing Machinery2023

High-dimensional Data Cubes

Christoph Koch, Sachin Basil John

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can ...

2022

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

Ketson Roberto Maximiano Dos Santos

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

2022

High-dimensional Data Cubes

Christoph Koch, Sachin Basil John

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can ...

ASSOC COMPUTING MACHINERY2022

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Pascal Fua, Pavan P Ramdya, Adám Gosztolai, Victor Lobato Rios, Helge Jochen Rhodin, Semih Günel, Daniel Eduardo Morales Garza, Marco Pietro Abrate

LiftPose3D infers three-dimensional poses from two-dimensional data or from limited three-dimensional data. The approach is illustrated for videos of behaving Drosophila, mice, rats and macaques. ...

NATURE PORTFOLIO2021

On some graph-based two-sample tests for high dimension, low sample size data

Soham Sarkar

Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, ...

SPRINGER2020

Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure

Michael Maximilian Steinlechner

In this thesis, we present a Riemannian framework for the solution of high-dimensional optimization problems with an underlying low-rank tensor structure. Here, the high-dimensionality refers to the size of the search space, while the cost function is scal ...

EPFL2016

Semi-supervised multiview embedding for hyperspectral data classification

Devis Tuia, Giona Matasci

In this paper, a method for semi-supervised multiview feature extraction based on the multiset regularized kernel canonical correlation analysis (kCCA) is proposed for the classification of hyperspectral images. The covariance matrix of this type of data i ...

Elsevier Science Bv2014

Reactive and Proactive Sharing Across Concurrent Analytical Queries

Anastasia Ailamaki, Manoussos Gavriil Athanassoulis, Iraklis Psaroudakis, Matthaios Alexandros Olma

Today an ever increasing amount of data is collected and analyzed by researchers, businesses, and scientists in data warehouses (DW). In addition to the data size, the number of users and applications querying data grows exponentially. The increasing concu ...

2014

An Adaptive Approach for Online Segmentation of Multi-Dimensional Mobile Data

Karl Aberer, Zhixian Yan, Tian Guo

With increasing availability of mobile sensing devices including smartphones, online mobile data segmentation becomes an important topic in reconstructing and understanding mobile data. Traditional approaches like online time series segmentation either use ...

2012

Introducing qualitative dimensions to analyze the usefulness of Web 2.0 platforms as PLEs

Denis Gillet, Stéphane Sire, Evgeny Bogdanov

The trends in web development of educational applications and of Web 2.0 or social applications are converging. Platforms are designed to host a variety of software components (called widgets or plugins), which can be organised or combined (mashed-up) at u ...

2011

Distributed Similarity Search in High Dimensions Using Locality Sensitive Hashing

Karl Aberer, Parisa Haghani, Sebastian Michel

In this paper we consider distributed K-Nearest Neighbor (KNN) search and range query processing in high dimensional data. Our approach is based on Locality Sensitive Hashing (LSH) which has proven very efficient in answering KNN queries in centralized set ...

2009

Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

Henry Markram, Sean Lewis Hill, Thomas Berger, Felix Schürmann, Idan Segev

Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such ...

2008

On Multidimensional Data and Modern Disks

Anastasia Ailamaki

With the deeply-ingrained notion that disks can efficiently access only one dimensional data, current approaches for mapping multidimensional data to disk blocks either allow efficient accesses in only one dimension, trading off the efficiency of accesses ...

USENIX Association2005