# K-Dominant Skyline Join Queries: Extending the Join Paradigm to K-Dominant Skylines

## TL;DR

This paper introduces KSJQ, a new approach for k-dominant skyline queries on joined relations, improving efficiency and scalability in multi-criteria, multi-attribute decision-making scenarios involving complex joins.

## Contribution

It extends k-dominant skyline queries to the join paradigm, proposing algorithms for efficient query processing and k-value selection, with practical handling of aggregated attributes.

## Key findings

- Pre-processing reduces query response time.
- Algorithms effectively select k for desired skyline size.
- Experiments show high scalability and efficiency.

## Abstract

Skyline queries enable multi-criteria optimization by filtering objects that are worse in all the attributes of interest than another object. To handle the large answer set of skyline queries in high-dimensional datasets, the concept of k-dominance was proposed where an object is said to dominate another object if it is better (or equal) in at least k attributes. This relaxes the full domination criterion of normal skyline queries and, therefore, produces lesser number of skyline objects. This is called the k-dominant skyline set. Many practical applications, however, require that the preferences are applied on a joined relation. Common examples include flights having one or multiple stops, a combination of product price and shipping costs, etc. In this paper, we extend the k-dominant skyline queries to the join paradigm by enabling such queries to be asked on joined relations. We call such queries KSJQ (k-dominant skyline join queries). The number of skyline attributes, k, that an object must dominate is from the combined set of skyline attributes of the joined relation. We show how pre-processing the base relations helps in reducing the time of answering such queries over the naive method of joining the relations first and then running the k-dominant skyline computation. We also extend the query to handle cases where the skyline preference is on aggregated values in the joined relation (such as total cost of the multiple legs of the flight) which are available only after the join is performed. In addition to these problems, we devise efficient algorithms to choose the value of k based on the desired cardinality of the final skyline set. Experiments on both real and synthetic datasets demonstrate the efficiency, scalability and practicality of our algorithms.