Introduction
Multiple-criteria decision analysis encompasses various methods, with traditional queries often using SQL-like approaches. Top-K queries return the top K options, KNN (K-Nearest Neighbors) retrieves the K nearest neighbors to a query point, and Skyline Queries are employed to ensure that each result returned from a database is not inferior to others in at least one aspect. A classic example is hotel selection, where a user may seek affordable hotels close to the beach. When beachfront hotels tend to be more expensive, the Skyline operator ensures that, for any two hotels, each excels in either distance from the beach or price, assisting users in finding their preferred hotel.
There are various approaches to Skyline Queries, each designed to address different issues. The following outlines the Skyline Query methods currently undertaken in our laboratory:
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Probabilistic Skyline Query: This arises from data uncertainty, representing objects through probability density functions (PDF) rather than single values. It provides more precise and accurate Skyline analysis.
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Reverse Skyline Query: When dealing with dynamic rather than static data, this query involves adding specific data objects or designating them as query points. The results include a set formed by the dynamic Skyline points, incorporating the query point itself.
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Group Skyline Query: