Skyline query can compute the optimal solution which meets the multiple standards in large-scale dataset.It has been widely applied for multi-objective decisions.Dynamic skyline query

as an important variant of skyline

its result can be dynamically changed with choosing different query points

providing more flexibility when the users make some specified needs.However

dynamic skyline query can return a large number of query results and ignore the directionality of query point and data integrality

making difficult for users to choose.It is necessary to optimize the result set of dynamic skyline

improving the whole data integrality and filtering a large number of redundant data.Focusing on these problems

we propose the augmented dynamic skyline query method based on MapReduce.The algorithm partitions the original data according to dimensional information

parallel computes dynamic skyline points in multiple nodes

optimizes the result set of the traditional dynamic skyline and at the same time provides the more global optimal results for the user to choose.In addition

when the users provide the tolerance of some dimensions

we propose the augmented dynamic skyline query with user tolerance.The algorithm reduces the original dataset according to the user tolerance and reduces the comparison times of intermediate results with improving the accuracy of the result set.The experiment results show that the augmented dynamic skyline query method based on MapReduce is efficient

accurate and scalable.