The Internet of Things （IoT） implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource,...The Internet of Things （IoT） implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions （attributes）, i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.展开更多
基金the National High-Tech Research and Development (863) Program of China (No. 2011AA010101)the National Natural Science Foundation of China (Nos. 61103197, 61073009, and 61240029)+5 种基金the Science and Technology Key Project of Jilin Province (No. 2011ZDGG007)the Youth Foundation of Jilin Province of China (No. 201101035)the Fundamental Research Funds for the Central Universities of China (No. 200903179)China Postdoctoral Science Foundation (No. 2011M500611)the 2011 Industrial Technology Research and Development Special Project of Jilin Province (No. 2011006-9)the 2012 National College Students' Innovative Training Program of China, and European Union Framework Program: MONICA Project under the Grant Agreement Number PIRSES-GA-2011-295222
文摘The Internet of Things （IoT） implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions （attributes）, i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.
