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Markus Schneider

EQUID: Efficient Querying of Inconsistent Data

Markus Schneider

 

Obtaining meaningful answers from inconsistent databases is vital to a host of applications. In the era of mass data production this poses a challenge due to the inherent properties of Big Data. Although the field of consistent query answering has attracted considerable attention, no practical solution exists to date. Most research has concentrated on finding tractable scenarios, which unfortunately have limited real world applicability. A recently proposed framework introduced a new concept which allows for efficient approximation algorithms. Promising preliminary results have shown the potential of this approach, which beg for further investigation. This is why we want to examine the boundaries of the new approach as well as its practical applicability in this proposed research.

Supervisors: Andreas Pieris & Marco Calautti