OLAP Techniques for Approximation and Mining Query Answering

Efficient Query Answering Through Approximation

In this poster, we propose to recast the idea of knowledge compilation into approximating OWL DL ontologies with DL-Lite ontologies, against which query answering has only polynomial data complexity. We identify a useful category of queries for which our approach also guarantees completeness. Furthermore, we paper report on the implementation of our approach in the ONTOSEARCH2 system.

Techniques of OLAP and Association Rule Mining

OLAP is a multidimensional view of complete data in the data store used for multidimensional analysis. It is the most practical approach used in the data warehouse for analytical process of large data and provides tools for analytical and statistical analysis of data. While Association rule learning is a popular and researched method for discovering interesting relations between variables in ve...

Mining Preferences from OLAP Query Logs for Proactive Personalization

The goal of personalization is to deliver information that is relevant to an individual or a group of individuals in the most appropriate format and layout. In the OLAP context personalization is quite beneficial, because queries can be very complex and they may return huge amounts of data. Aimed at making the user’s experience with OLAP as plain as possible, in this paper we propose a proactiv...

Intelligent Query Answering by Knowledge Discovery Techniques

Knowledge discovery in databases facilitates querying database knowledge, cooperative query answering and semantic query optimization in database systems. In this paper, we investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovere...

Intelligent Query Answering by Knowledge Discovery Techniques