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Content archived on 2024-05-28

HeisenData - Towards a Next-Generation Uncertain-Data Management System

Objective

Several real-world applications need to manage and reason about large amounts of data that are inherently uncertain. For instance, pervasive computing applications must constantly reason about volumes of noisy sensory readings, e.g. for motion prediction and human behavior modeling; information-extraction tools can assign different possible labels with varying degrees of confidence to segments of text, due to the uncertainties and noise present in free-text data. Such probabilistic data analyses require sophisticated machine-learning tools that can effectively model the complex correlation patterns present in real-life data. Unfortunately, to date, approaches to Probabilistic Database Systems (PDBSs) have relied on somewhat simplistic models of uncertainty that can be easily mapped onto existing relational architectures: Probabilities are typically associated with individual data tuples, with little or no support for capturing data correlations. This research proposal aims to design and build a novel, extensible PDBS that supports a broad class of statistical models and probabilistic-reasoning tools as first-class system objects, alongside a traditional relational-table store. Our proposed architecture will employ statistical models to effectively encode data-correlation patterns, and promote probabilistic inference as part of the standard database operator repertoire to support efficient and sound query processing. This tight coupling of relational databases and statistical models represents a major departure from conventional database systems, and many of the core system components need to be revisited and fundamentally re-thought. The proposed research will attack several of the key challenges arising in this novel PDBS paradigm (including, query processing, query optimization, data summarization, extensibility, and model learning and evolution), build usable prototypes, and investigate key application domains (e.g. information extraction).

Fields of science (EuroSciVoc)

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Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

FP7-PEOPLE-2009-RG
See other projects for this call

Funding Scheme

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MC-IRG - International Re-integration Grants (IRG)

Coordinator

TECHNICAL UNIVERSITY OF CRETE
EU contribution
€ 100 000,00
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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