| dc.description.abstract | Astronomical researchers have been manually registering and maintaining observation data for various analysis processes. But with the ongoing construction of observatories from Pan-Starrs projects, the size of observation data has exploded. Manually processing numerous of data each day becomes impractical. Responding to this challenge, we need to construct large scale information management system, as well as the efficient methodology for data analysis. We have the following goals to achieve in this project:
1. Constructing an automatic information preparation system:
Because of the movements of earth and astronomical objects, a complete set of observation records requires gathering data from world-wide observatories. Limited by factors such as hardware, weather, time, or temperature, we also need to calibrate and clarify heterogeneous data sources before data integration. Considering the rapidly growing data size, data preparation has to be processed automatically and efficiently. We will implement this preparation system with the accessibility of computer network and perform necessary calibration or transformation based on historical data features. The clarified data then can be integrated for further analysis and researches.
2. Develop astronomical time-series pattern mining and associated rule mining methodologies:
Discovering the similarities between astronomical objects, and accordingly classify those objects, is an important process for many astronomical researches. We then integrate concept hierarchy with weighted suffix tree, and made those similar variation trend objects gather in the same branch inside the tree structure. Furthermore, we also implement some functions to help user searching what they are interested in.
By using automatic program, the observation data can be simplified. Not only reduce the loading in data analysis, but also improve its efficiency and give those researchers a better solution to handle large data in the future.
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