Summary
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.
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/804302 |
| Start date: | 01-12-2018 |
| End date: | 30-11-2024 |
| Total budget - Public funding: | 1 306 250,00 Euro - 1 306 250,00 Euro |
Cordis data
Original description
In the context of data-intensive systems, data provenance captures the way in which data is used, combinedand 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.
Status
SIGNEDCall topic
ERC-2018-STGUpdate Date
27-04-2024
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