Abstract: Skyline queries are one of the most commonly used query operators for locating query results that only return data items whose dimension vector is not dominated by any other data item in the database. Skyline queries have been included into various sorts of databases, including complete, incomplete, and uncertain, due to their utility and ubiquity. In the early phases of a knowledge-discovery process, the skyline query and its variant queries are useful functions. The skyline query and its variants identify a collection of important items that outperform the dataset's other common objects. Such knowledge-discovery queries must be computed in parallel distributed systems in order to manage huge data. We will use typical datasets in this research to parallelize skyline computations utilising various strategies such as parallel data pre-processing, parallel skyline computation using multithreading, and multiprocessing. The ultimate goal is to reduce response time using parallel computation. We shall be able to give the approach for efficient, parallel skyline calculation near the end of the paper.
Keywords: Skyline query, Preference queries, Skylines, SQL, Algorithms, Database, Multithreading, multiprocessing.
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DOI:
10.17148/IJARCCE.2022.11691
