A Situation Analysis Method for Specific Domain Based on Multi-source Data Fusion
External Internet data, as a supplement of internal data, plays an important role to decision analysis of decision makers. However, the key point of this process is to solve the problem of inconsistency between multi-source heterogeneous data. In this paper, a situation analysis method based on multi-source data fusion is proposed to analyze the situation in a specific domain. The approach consists of three main steps. Firstly, Naive Bayes multi label classification algorithm is used in the process of text topic classification and quantization to overcome the structural inconsistency of multi-source data. Secondly, a time difference correlation analysis method is used to address the time inconsistency the two time series. Finally, Support vector machine regression algorithm (SVR) is used for situation assessment in related fields. In this study, the effectiveness of the model is verified by the free-trade zone (FTZ) platform shares data, Internet news text data, and Internet statistics. The experimental results show that the method has achieved good results in the situation estimation of the related indexes.
KeywordsMulti-source data fusion Inconsistency Naive Bayes multi label classification Causal lag analysis SVR Situation estimation
This work was supported by National Natural Science Foundation of China (No. 61472004, 61602109), Shanghai Science and Technology Innovation Action Plan Project (No. 16511100903), and by The Key Laboratory of Embedded System and Service Computing of Tongji University of Ministry Education (2015).
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