Abstract
The explosion of big data emphasizes the need for scalable data diversification, especially for applications based on web, scientific, and business databases. However, achieving effective diversification in a multi-user environment is a rather challenging task due to the inherent high processing costs of current data diversification techniques. In this paper, we address the concurrent diversification of multiple search results using various approximation techniques that provide orders of magnitude reductions in processing cost, while maintaining comparable quality of diversification as compared to sequential methods. Our extensive experimental evaluation shows the scalability exhibited by our proposed methods under various workload settings.
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