The generation of synthetic data is useful in multiple aspects, from testing applications to
benchmarking to privacy preservation. Generating thelinks between relations, subject …

Generating synthetic data is useful in multiple application areas (eg, database testing,
software testing). Nevertheless, existing synthetic data generators generally lack the …

This paper introduces a method for improving the association rules that will also help
improve the performance of recommender systems. Combining sampling and parallelism in …

Large amounts of data often require expensive and time-consuming analysis. Therefore,
highly scalable and efficient techniques are necessary to process, analyze and discover …

Generating synthetic data is useful in multiple application areas (eg, database testing,
software testing). Nevertheless, existing synthetic data generators are either limited to …

The abundance of data, often containing private and sensitive information, coupled with an
ever-growing interest in using the data for research and driving business value at scale has …

This paper [1] presents a method to evaluate Value Generalisation Hierarchies—tree-like
structures used in the data anonymisation process—with the aim of improving their …

Сэмплинг является популярным подходом к обработке сверхбольших баз данных в
широком спектре приложений, связанных с интеллектуальным анализом данных …