In the era of big data, multimedia, hyper-media and social networks are emerging, and the amount of information is growing rapidly. When people participate in the process of massive data processing, they will encounter data with different structures, so data has heterogeneity. How to acquire hidden and valuable knowledge from heterogeneous data and measure its uncertainty is an important problem in artificial intelligence. This paper investigates uncertainty measurement for heterogeneous data and gives its application in attribute reduction. The concept of a heterogeneous information system (HIS) is first proposed. Then, an equivalence relation on the object set is constructed. Next, uncertainty measurement for a HIS is investigated, a numerical experiment is given, and dispersion analysis, correlation analysis, and Friedman test and Bonferroni–Dunn test in statistics are conducted. Finally, as an application of the proposed measures, attribute reduction in a HIS is studied, and the corresponding algorithms and their analysis are proposed.

在大数据时代，多媒体，超媒体和社交网络正在兴起，信息量正在迅速增长。当人们参与海量数据处理过程时，他们会遇到具有不同结构的数据，因此数据具有异构性。如何从异构数据中获取隐藏和有价值的知识并测量其不确定性是人工智能中的重要问题。本文研究了异构数据的不确定性度量，并给出了其在属性约简中的应用。首先提出了异构信息系统（HIS）的概念。然后，在对象集上建立等价关系。接下来，研究了HIS的不确定度测量，给出了数值实验，并进行了色散分析，相关分析，在统计上进行了Friedman检验和Bonferroni-Dunn检验。最后，作为所提出措施的应用，研究了HIS中的属性约简，并提出了相应的算法及其分析。