Data diversification provides users with a concise and meaningful view of the results returned by search queries. In addition to taming the information overload, data diversification also provides the benefits of reducing data communication costs as well as enabling data exploration. The explosion of big data emphasizes the need for data diversification in modern data management platforms, especially for applications based on web, scientific, and business databases. Achieving effective diversification, however, is rather a challenging task due to the inherent high processing costs of current data diversification techniques. This challenge is further accentuated in a multi-user environment, in which multiple search queries are to be executed and diversified concurrently. In this paper, we propose the DoS scheme, which addresses the problem of scalable diversification of multiple search results. Our experimental evaluation shows the scalability exhibited by DoS under various workload settings, and the significant benefits it provides compared to sequential methods.