## 1 Introduction  
With the rapid development of smart grid and sensing technology, China's power user side data showed high complexity and redundancy. Since 2011, the user side data volume of power distribution system in China has been booming, from GB to TB, even to PB level, and gradually forms a big data system. Facing the era of big data, power companies have not only improved traditional MySQL, Oracle, and other relational database systems, but also produced lots of new big data systems, such as HBase, GBase, and etc. All of these database systems mentioned above have already formed a multi-source heterogeneous big data system for intelligent power distribution and utilization (IPDU) [1; 2; 3; 4]. On the other hand, affected by local economic levels, the monitoring and testing conditions in local power companies and manufacturers for distribution network equipment are quite different, while parts of the complex monitoring and testing equipment is unreasonable and impossible to repeat purchase, leading to the further heterogeneity of IPDU big data. The IPDU big data contains operation data and external scene information of distribution network, and supports the further decision analysis for the planning, construction, operation, maintenance, and other business sectors of power distribution system. Through the deep mining of multi-source data, it would be easy to realize the accurate analysis of current situations and future trends of distribution networks, and operation control of intelligent power distribution network [5; 6]. However, the traditional data analysis and decision technologies of distribution networks require that all the data samples share the same monitoring and testing indexes, which makes the IPDU heterogeneous big data difficult to be effectively utilized, resulting in a huge waste of data resources. Because of the structure heterogeneity among the existing database systems and the limitation of data qualities from both objective and subjective factors, the IPDU big data system could not directly meet the requirements of traditional data analysis and decision technologies. At the same time, it also comes into a small sample environment for parts of samples in IPDU big data, and brings out great challenges for the data quality management of distribution networks, which makes it difficult to guarantee the accuracy of analysis and decisions [7; 8]. Therefore, the study on heterogeneous data integration technology has a significant influence on the future intelligent power distribution and utilization, which can also greatly improve the accuracy and efficiency of distribution network's operation and decision-making. So far, data integration researches in intelligent power distribution and utilization could be broadly divided into two categories, i.e., time-series data integration and index data integration. The research subjects of the former one mainly include load data, distributed power output, wind speed of wind turbine, and etc. These kinds of data integration technologies are relatively mature, and already form a relatively perfect research system, in which grey relational analysis, collaborative filtering, Markov chain, support vector machine and neural network are widely used as data analysis tools [9; 10; 11; 12]. The research subjects of index data integration mainly include the structured and semi-structured data, in addition to time-series data. Many experts and scholars have also carried out some inspiring works in this research field, such as: [PERSON] and et al. introduced low rank and sparsity theory for data integration to detect the false data injection in power grid [13]; [PERSON] and et al. proposed an XLPE power cable lifetime evaluation method by employing low-rank matrix completion technology [14]; [PERSON] and et al. used robust nonparametric regression via sparsity control to perform data cleaning and repair tasks [15].