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In the field of science, astronomy has a very important status. As the observation technology and hardware equipment in recent years continue to improve, so that researchers in the field of astronomy can do more diversified analysis, and the amount of data observed by astronomical telescope continue to increase, and has gradually increased to Petabyte level.
In this paper, a suffix tree system based on distributed sturcture of Hadoop is proposed to assist astronomers to classify variable stars. The system is designed with MapReduce and Spark frameworks. In the stage of constructing suffix tree, the system converts a large amount of data, which is the sequence of star brightness changing over time, into a suffix tree structure, then stores the tree in the distributed file system; the system also supports appending following observation data. Using the characteristics of the suffix tree allows users to query efficiently. Moreover, the query stage of the system introduces the hierarchical concept, which can adjust the preciseness of the data in the tree, allows the system to not only find out the similar sequence generated by observation or calculation errors but also provide more diversified query in response to different classification methods. According to different needs, astronomical researchers can select the preciseness of data to classify stars, and quickly find the ID of same or similar characteristics of the star. | en_US |