Top-k Skyline: A Unified Approach

The WWW has become a huge repository of information. For almost any knowledge domain there may exist thousands of available sources and billions of data instances. Many of these sources may publish irrelevant data. User-preference approaches have been defined to retrieve relevant data based on similarity, relevance or preference criteria specified by the user. Although many declarative languages can express user-preferences, considering this information during query optimization and evaluation remains as open problem. SQLf, Top-k and Skyline are three extensions of SQL to specify user-preferences. The first two filter irrelevant answers following a score-based paradigm. On the other hand, the latter produces relevant non-dominated answers using an order-based paradigm. The main objective of our work is to propose a unified approach that combines paradigms based on order and score. We propose physical operators for SQLf considering Skyline and Top-k features. Properties of those will be considered during query optimization and evaluation. We describe a Hybrid-Naive operator for producing only answers in the Pareto Curve with best score values. We have conducted initial experimental studies to compare the Hybrid operator, Skyline and SQLf.