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

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Related publications (30)

MOSES: A Streaming Algorithm for Linear Dimensionality Reduction

This paper introduces Memory-limited Online Subspace Estimation Scheme (MOSES) for both estimating the principal components of streaming data and reducing its dimension. More specifically, in various applications such as sensor networks, the data vectors a ...

IEEE COMPUTER SOC2020

Adaptive partitioning and indexing for in situ query processing

Anastasia Ailamaki, Manoussos Gavriil Athanassoulis, Ioannis Alagiannis, Matthaios Alexandros Olma, Manolis Karpathiotakis

The constant flux of data and queries alike has been pushing the boundaries of data analysis systems. The increasing size of raw data files has made data loading an expensive operation that delays the data-to-insight time. To alleviate the loading cost, in ...

SPRINGER2020

Timely and cost-efficient data exploration through adaptive tuning

Matthaios Alexandros Olma

Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up. Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...

EPFL2019

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

Anastasia Ailamaki, Matthaios Alexandros Olma, Raja Appuswamy, 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

Time- and Space-Efficient Spatial Data Analytics

Mirjana Pavlovic

Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...

EPFL2019

Efficient Bundled Spatial Range Queries

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

Efficiently querying multiple spatial data sets is a growing challenge for scientists. Astronomers query data sets that contain different types of stars (e.g., dwarfs, giants, stragglers) while neuroscientists query different data sets that model different ...

ASSOC COMPUTING MACHINERY2019

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis

Kathryn Hess Bellwald, Cathrin Brisken, Jacques Rougemont, Rachel Marcone

Motivation: Unbiased clustering methods are needed to analyze growing numbers of complex data sets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small data sets. ...

2019

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

Arnab Bhattacharya, Akhil Arora, Piyush Kumar

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

Spectral MAB for Unknown Graph Processes

Pascal Frossard, Laura Toni

In this work, we study graph-based multi-arms bandit (MAB) problems aimed at optimizing actions on irregular and high-dimensional graphs. More formally, we consider a decision-maker that takes sequential actions over time and observes the experienced rewar ...

IEEE Computer Society2018

Graph-based Methods for Visualization and Clustering

Johann Paratte

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017

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