Delay‐bounded skyline computing for large‐scale real‐time online data analytics

Summary The proliferation of Internet applications, cloud systems, and mobile social networks results in unprecedented data set scale and high data generation rate. For us to be able to extract any meaningful information, it is important to achieve real‐time online data analytics. Skyline queries are important in many online data applications such as real‐time Web mining, multipreference analysis, and decision making. Most existing studies mainly focus on centralized systems, and distributed skyline query processing is still an emerging and challenging topic. In this paper, we propose SkyStorm, a delay‐bounded parallel skyline computing approach for large‐scale real‐time data analytics by dividing the search into multiple rounds and limiting the search in each round within a budget‐restricted range. The effectiveness of our proposals is demonstrated through analysis and simulations.