Publications related to Data virtualization

Oligolithic Cross-task Optimizations across Isolated Workloads

Anastasia Ailamaki, Eleni Zapridou, Panagiotis Sioulas

Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely differen ...

2024

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Henry Markram, Sean Lewis Hill, Samuel Claude Kerrien, Carolina Johanna Elisabeth Lindqvist, Alejandra Garcia Rojas Martinez, Huanxiang Lu, Mohameth François Sy, Anna-Kristin Kaufmann, Jonathan Raël Lurie, Henry Genet, Pierre-Alexandre Fonta, Alexander Désiré Ulbrich, Michaël Fernand Paul Dupont, Silvia Rosario Jimenez Tejeda, Bogdan Roman, Ian Lavriushev, Didac Montero Mendez, Wojciech Adam Wajerowicz, Kenneth William Pirman, Julien Antonin Machon, Dhanesh Neela Mana, Natalia Stafeeva

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simul ...

2023

Columnar Storage Optimization and Caching for Data Lakes

Haoqiong Bian

As a unified data repository, data lake plays a vital role in enterprise data management and analysis. It composes the raw files into tables that are processed in-situ by various computation engines and applications. Therefore, the read performance of the ...

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 ...

2022

Pixels: An Efficient Column Store for Cloud Data Lakes

Anastasia Ailamaki, Haoqiong Bian

To benefit from the cloud’s higher elasticity and price-efficiency, most modern data-lake engines support S3-like cloud object storage (COS) services as their optional or preferred underlying storage. Meanwhile, the widespread column stores, such as Parque ...

IEEE2022

Frugal Decentralized Learning

Anne-Marie Kermarrec

Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally a ...

IEEE2022

Storage Management in Smart Data Lake

Anastasia Ailamaki, Haoqiong Bian, Bikash Chandra, Ioannis Mytilinis

Data lakes are complex ecosystems where heterogeneity prevails. Raw data of diverse formats are stored and processed, while long and expensive ETL processes are avoided. Apart from data heterogeneity, data lakes also entail hardware heterogeneity. Typical ...

2021

Data transformer apparatus

Babak Falsafi, Christoph Koch, Siddharth Gupta, Mario Paulo Drumond Lages De Oliveira, Mark Johnathon Sutherland, Arash Pourhabibi Zarandi, Zilu Tian, Hussein Kassir

Data transformer apparatus comprising at least a dispatcher module (D), a reader module (R), a converter module (C) and a writer module (W). The dispatcher module (D) is configured to: receive a data transformation request (DTR) including: a first informat ...

2021

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Martin Mathias Müller

The quantification of population-level health behaviors is crucial for guiding public health policy. However, traditional methods for measuring such health behaviors have several short- comings. In recent years social media data has been successfully used ...

EPFL2021

Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption

David Jules Froelicher

Analyzing and processing data that are siloed and dispersed among multiple distrustful stakeholders is difficult and can even become impossible when the data are sensitive or confidential. Current data-protection and privacy regulations highly restrict the ...

EPFL2021

JIT works: decide when all data is known (VLDB Women in Database Research Award talk)

Anastasia Ailamaki

When the time comes to make a critical decision, it is of paramount importance to prepare enough so that all the information necessary is available at decision time. Under-preparation leads to uninformed decisions; over-preparation, however, may lead to co ...

ASSOC COMPUTING MACHINERY2020

Ontology for Strategies and Predictive Maintenance models

Dimitrios Kyritsis, Gökan May, Sangje Cho, Marlène Hildebrand-Ehrhardt

As of today, to cope with traditional maintenance policies such as reactive and preventive maintenance, the manufacturing companies need the deployment of adaptive and responsive maintenance strategies. Meanwhile, the advent of Industry 4.0 leads the maint ...

ELSEVIER2020

QUASII: QUery-Aware Spatial Incremental Index

Anastasia Ailamaki, Darius Sidlauskas, Thomas Heinis, Mirjana Pavlovic

With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can ...

2018

Just-in-time Analytics Over Heterogeneous Data and Hardware

Manolis Karpathiotakis

Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of datasets to gain insights. At the same time, data variety increases continuously across multiple axes. First, data comes in mu ...

EPFL2017

How to Aggregate Lesson Observation Data into Learning Analytics Datasets?

Maria Jesus Rodriguez Triana

The technological environment that supports the learning process tends to be the main data source for Learning Analytics. However, this trend leaves out those parts of the learning process that are not computer-mediated. To overcome this problem, involving ...

CEUR2017

CleanM: An Optimizable Query Language for Unified Scale-Out Data Cleaning

Anastasia Ailamaki, Benjamin Cyrille Damien Gaidioz, Manolis Karpathiotakis, Styliani Asimina Giannakopoulou

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. At the same time, the existing tools that att ...

Assoc Computing Machinery2017

No data left behind: real-time insights from a complex data ecosystem

Anastasia Ailamaki, Manolis Karpathiotakis

The typical enterprise data architecture consists of several actively updated data sources (e.g., NoSQL systems, data warehouses), and a central data lake such as HDFS, in which all the data is periodically loaded through ETL processes. To simplify query p ...

2017

Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health

Marcel Salathé

The digital revolution has contributed to very large data sets (ie, big data) relevant for public health. The two major data sources are electronic health records from traditional health systems and patient-generated data. As the two data sources have comp ...

Oxford University Press (OUP)2016

A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

Karl Aberer, Michael Ignaz Schumacher, Matteo Vasirani, Alevtina Dubovitskaya

In this paper, we address the problem of building an anonymized medical database from multiple sources. Our proposed solution defines how to achieve data integration in a heterogeneous network of many clinical institutions, while preserving data utility an ...

Springer International Publishing2015

Combine and Conquer : Mining Social Systems for Prediction

Vincent Etter

In this thesis, we explore the application of data mining and machine learning techniques to several practical problems. These problems have roots in various fields such as social science, economics, and political science. We show that computer science tec ...

EPFL2015