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A Framework for Autonomic Computing for In Situ Imageomics

Publications associées (32)

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A machine learning approach for mapping the very shallow theoretical geothermal potential

Jean-Louis Scartezzini, Dan Assouline, Nahid Mohajeri Pour Rayeni

The very shallow geothermal potential (vSGP) is increasingly recognized as a viable resource for providing clean thermal energy in urban and rural areas. This is primarily due to its reliability, low-cost installation, easy maintenance, and little constrai ...

SPRINGEROPEN2019

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

Daniel Kuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Wolfram Wiesemann

A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these nominal distributions are themselves estimated from data, which makes them s ...

2019

With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, contemporary applications in science and engineering impose heavy computational and storage burdens on the optimization algorithms. As a result, there ...

EPFL2019

Whether it is for personal use or for medical application, wearable sensors are becoming more and more widespread. This is the industry answer to two parallel trends. First, the public show a wish to collect data about their own lifestyle. This rather new ...

EPFL2019

A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future

Gökan May, Sangje Cho, Roberto Pérez, Ioannis Tourkogiorgis

Advanced technologies based on Internet of Things (IOT) are blazing a trail to effective and efficient management of an overall plant. In this context, manufacturing companies require an innovative strategy to survive in a competitive business environment, ...

SPRINGER-VERLAG BERLIN2018

Toward timely, predictable and cost-effective data analytics

Renata Borovica-Gajic

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to ext ...

EPFL2016

Scalable splitting algorithms for big-data interferometric imaging in the SKA era

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In the context of next generation radio telescopes, like the Square Kilometre Array, the efficient processing of large-scale datasets is extremely important. Convex optimisation tasks under the compressive sensing framework have recently emerged and provid ...

Wiley-Blackwell2016

From Data Quality to Big Data Quality

Gianluigi Viscusi

This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordina ...

2015

CoShare: A Cost-effective Data Sharing System for Data Center Networks

Karl Aberer, Rameez Rahman, Hao Zhuang, Imen Filali

Numerous research groups and other organizations collect data from popular data sources such as online social networks. This leads to the problem of data islands, wherein all this data is isolated and lying idly, without any use to the community at large. ...

2015

Runtime Prediction for Scale-Out Data Analytics

Adrian Daniel Popescu

Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads ca ...

EPFL2015