Research Of The Improved Data Stream Query Algorithm Based On The Skyline

With the continuous development and application of the information technology, the methods of the date collection become richer and richer, and mass memory become common. Based on these facts, a new operator called for skyline computing, which was used to find the points which aren't dominated by any other point in the data set, had been introduced into database field. The skyline computing can draw a picture of the object database, and help the users to find the interesting objects. Also, it can play an important part in multi-objective decision and data mining and data visualization, and so on.CCS algorithm is an effective skyline computing on the data stream, which keeps the SP set by the dominance tree. This algorithm can greatly reduce the skyline computing complexity.Also, CCS has some drawbacks. First, it may result the waste of the time and space when the data quantity is large. Second, it results the repeating computing. Third, it couldn't make full use of the SP set.In response to these questions, this thesis establishs a new data stream query model of the skyline. At the same time, this algorithm deals with the data ahead of time by the nearest neighbor algorithm. It will effectively reduce the resource through cutting off unnecessary objects. This algorithm meets the users' preferences by using the weighted euclidean distance method. Finally, the improved algorithm based on the skyline can reduce the time and space complexity and meet users' preferences by the experiments. The experiments show that the improved algorithm is viable. The improved algorithm is more effective than the algorithm which isn't improved. Also, the experiments test the effect of the different data types.