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ProDIS SIGNED

Provenance for Data-Intensive Systems

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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Project "ProDIS" data sheet

The following table provides information about the project.

Coordinator
TEL AVIV UNIVERSITY 

Organization address
address: RAMAT AVIV
city: TEL AVIV
postcode: 69978
website: http://www.tau.ac.il/

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 Israel [IL]
 Total cost 1˙306˙250 €
 EC max contribution 1˙306˙250 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-12-01   to  2023-11-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TEL AVIV UNIVERSITY IL (TEL AVIV) coordinator 1˙306˙250.00

Map

 Project objective

In the context of data-intensive systems, data provenance captures the way in which data is used, combined and manipulated by the system. Provenance information can for instance be used to reveal whether data was illegitimately used, to reason about hypothetical data modifications, to assess the trustworthiness of a computation result, or to explain the rationale underlying the computation. As data-intensive systems constantly grow in use, in complexity and in the size of data they manipulate, provenance tracking becomes of paramount importance. In its absence, it is next to impossible to follow the flow of data through the system. This in turn is extremely harmful for the quality of results, for enforcing policies, and for the public trust in the systems. Despite important advancements in research on data provenance, and its possible revolutionary impact, it is unfortunately uncommon for practical data-intensive systems to support provenance tracking. The goal of the proposed research is to develop models, algorithms and tools that facilitate provenance tracking for a wide range of data-intensive systems, that can be applied to large-scale data analytics, allowing to explain and reason about the computation that took place. Towards this goal, we will address the following main objectives: (1) supporting provenance for modern data analytics frameworks such as data exploration and data science, (2) overcoming the computational overhead incurred by provenance tracking, (3) the development of user-friendly, provenance-based analysis tools and (4) experimental validation based on the development of prototype tools and benchmarks.

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

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