Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Institut für Informatik

Gastvortrag: Prof. Dr. Domenico Sacca, Univ. of Calabria

  • Wann 19.07.2018 von 11:00 bis 23:59
  • Wo 12489 Berlin, Rudower Chaussee 25, Haus 3, R. 408
  • iCal

Am Donnerstag, den 19.7.2018, wird Prof. Dr. Domenico Sacca (Univ. of Calabria) bei uns zu Gast sein und um 11h00 s.t. im Raum 3.408 den Vortrag halten:

"Count Constraints: Definition, Complexity and Emerging Applications".

 

Alle Interessenten sind herzlich eingeladen!

 

Abstract:
A typical problem in database theory is to verify whether there exists a relation (or database) instance satisfying a number of given dependency constraints.
This problem has received a renewed deal of interest within the context of data exchange, but the issue of handling constraints on aggregate data has not been much investigated so far, notwithstanding the relevance of aggregate operations in exchange systems.
In this talk, we introduce count constraints that require the results of given count operations on a relation to be within a certain range. Count constraints are defined by a suitable extension of first order predicate calculus, based on set terms, and they are then used in a new decisional problem, the Inverse OLAP: given a star schema, does there exist a relation instance satisfying a set of given count constraints? The new problem turns out to be NEXP complete under various conditions: program complexity, data complexity and combined complexity.
Count constraints can be also used into a data exchange system context, where data from the source database are transferred to the target database using aggregate operations. In this framework, we show how to enrich source data  with additional background information and knowledge patterns on the application domain, derived by previous analysis of experiences from both experts and simple users. We shall therefore investigate the following challenge: devising a data exchange setting for enhancing the information content of source data. As an example, given raw data about restaurants and post reviews, we want to classify the restaurants according to the various “dimensions” (i.e., categorized properties or other features) that have been singled out and evaluated
in the reviews.