Weighing the techniques for data optimization in a database

TL;DR

This paper compares different data optimization techniques in databases, focusing on skyline and ranking methods, through experimental evaluation to assess their effectiveness in multi-criteria data retrieval.

Contribution

It provides an empirical comparison of dominance-based skyline and utility-based ranking methods for data selection in large databases.

Findings

Skyline approach effectively identifies interesting data points.

Ranking queries efficiently prioritize data based on scoring functions.

Experimental results highlight the strengths and limitations of each method.

Abstract

A set of preferred records can be obtained from a large database in a multi-criteria setting using various computational methods which either depend on the concept of dominance or on the concept of utility or scoring function based on the attributes of the database record. A skyline approach relies on the dominance relationship between different data points to discover interesting data from a huge database. On the other hand, ranking queries make use of specific scoring functions to rank tuples in a database. An experimental evaluation of datasets can provides us with information on the effectiveness of each of these methods.