Opendata, web and dolomites

ViDaR

ViDaR: R-enabled large-scale data analytics in ViDa

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "ViDaR" data sheet

The following table provides information about the project.

Coordinator
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE 

Organization address
address: BATIMENT CE 3316 STATION 1
city: LAUSANNE
postcode: 1015
website: www.epfl.ch

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 150˙000 €
 EC max contribution 150˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-PoC
 Funding Scheme ERC-POC
 Starting year 2018
 Duration (year-month-day) from 2018-01-01   to  2019-06-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE CH (LAUSANNE) coordinator 150˙000.00

Map

 Project objective

In this project we will build ViDaR, an interface for integrating R with ViDa. R is among the leading data analytics environments (the leading open-source), and is heavily used by data and domain scientists and data analysts in their daily routine. ViDa, developed by an ERC grant, is the state-of-the-art query engine for raw data. It relies on data virtualization, i.e., abstracting data out of its form and manipulating it regardless of the way it is stored or structured, to enable efficient, scalable, querying and manipulation of data in-situ, at their raw format and shape. Integration of ViDa with R will have a positive impact on both systems. For ViDa, it will provide capabilities for data exploration, visualization, mining and analytics, as well as powerful libraries for numerical and statistical computing, thereby substantially growing its user base. For R, it will increase its scale and performance, and reduce the time and effort spent by data scientists on tedious data management tasks. The resulting solution will serve as a proof-of-concept of ViDa’s performance, capabilities, and flexibility for integration with any third-party software that needs to manage vast amounts of raw data.

 Publications

year authors and title journal last update
List of publications.
2019 Aunn Raza, Periklis Chrysogelos, Panagiotis Sioulas, Vladimir Indjic, Angelos Anadiotis; Anastasia Ailamaki
(under submission) GPU-accelerated data management under the test of time
published pages: , ISSN: , DOI:
2019-11-14
2019 Periklis Chrysogelos, Manolis Karpathiotakis, Raja Appuswamy, Anastasia Ailamaki
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines.
published pages: , ISSN: , DOI:
2019-11-14
2019 Panagiotis Sioulas, Periklis Chrysogelos, Manolis Karpathiotakis, Raja Appuswamy, Anastasia Ailamaki
Hardware-conscious Hash-Joins on GPUs
published pages: , ISSN: , DOI:
2019-11-14
2019 Periklis Chrysogelos, Panagiotis Sioulas, Anastasia Ailamaki
Hardware-conscious Query Processing in GPU-accelerated Analytical Engines
published pages: , ISSN: , DOI:
2019-11-14

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "VIDAR" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "VIDAR" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.1.)

Growth regulation (2019)

The wide-spread bacterial toxin delivery systems and their role in multicellularity

Read More  

EffectiveTG (2018)

Effective Methods in Tame Geometry and Applications in Arithmetic and Dynamics

Read More  

inhibiTOR (2020)

Novel selective mTORC1 inhibitors

Read More