Efficient skyline computation over distributed interval data

The increasing volume of uncertain data has resulted in a dire need for supporting efficient uncertain data management. The skyline query as an important aspect of data management has received considerable attention in recent years, because of its importance in making intelligent decisions over complex data. Moreover, data collection and storage have become increasingly distributed, which makes the central assembly of data for storage and query infeasible and inefficient. Although many research efforts have been conducted to address the skyline query problem in various distributed scenarios, we still lack algorithms to address the queries over interval data, which is a special kind of attribute-level uncertain data that widely exists in many applications. In this paper, we extensively study the skyline query over distributed interval data. We model the skyline query problem and define the distributed skyline query over interval data. Particularly, 2 efficient algorithms are proposed to retrieve the skylines progressively from distributed local sites with a highly optimized feedback framework. Moreover, we exploit 2 strategies for further improving the queries. Extensive experiments on synthetic and real datasets with real deployment are conducted to validate the effectiveness and efficiency of our proposals.