Efficient astronomical data classification on large-scale distributed systems

Classification of different kinds of space objects plays an important role in many astronomy areas. Nowadays the classification process can possibly involve a huge amount of data. It could take a long time for processing and demand many resources for computation and storage. In addition, it may also take much effort to train a qualified expert who needs to have both the astronomy domain knowledge and the capability to manipulate the data. This research intends to provide an efficient, scalable classification system for astronomy research. We implement a dynamic classification framework and system using support vector machines (SVMs). The proposed system is based on a large-scale, distributed storage environment, on which scientists can design their analysis processes in a more abstract manner, instead of an awkward and time-consuming approach which searches and collects related subset of data from the huge data set. The experimental results confirm that our system is scalable and efficient.