| As the archaeological excavation technology continues to improve, the type and quantity of information obtained in archaeological excavations increase rapidly. Currently the archaeological research relied heavily on the acquisition and application of data. The massive archaeological excavations data can effectively promote archaeological research. Thus, it has great value to do research on the processing and analysis of archaeological excavations data.Firstly, based on the data processing requirements, we build the archaeological excavations data model to represent all kinds of information achieved in archaeological site. From the perspective of time and space, this model describes the data need to be collected in the archaeological excavations. Then for the structured attribute data, a relational database model is built. And for the vast amounts of file data, we build a distributed storage solution based on the Hadoop Distributed File System.Secondly, we present a new association rule mining method which combined subjective and objective interestingness measure. This method can effectively filter out some of the boring candidate frequent item sets and rules, and to improve the performance and effectiveness of the association rule mining. Through experiments on Tom Brijs datasets, we verify the effectiveness of this method. Also we have experiments on the actual data collected from LiangZhu cultural site. We get a series of association rules and most of them can get a good explanation. The experimental results show that the association rule mining can help archaeological researchers find new things amount the vast amounts of archaeological excavations data.Lastly we describe the design and implementation of the archaeological excavations of data resource management platform, on which the data acquisition module, data classification display modules and archaeological excavation report generation module is described in detail. |
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