# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 2 Background  
In this section, we present the basic concepts in material data and multi-source data fusion, and particularly discuss the motivations of multi-source material data fusion.  
Material data are produced in a wide range of industrial and scientific environments. They are difficult to be described by a unified model, due to the complex features of material data. Material data fusion is closely related with data gathering and integration. The most fundamental challenge of multi-source heterogeneous data integration and fusion is that there are big challenges in every small process of processing material data, and every process cannot be processed separately. Currently, Oracle, Facebook, Google and some other big IT companies already provide technologies for data format conversion and data integration. However, they cannot support the fusion of material data, as material data involve many kinds of data, which are hard to be processed within one framework. Table 1 shows a classification of material data according to material data structure and format.  
Basically, the main characteristics of material data can be summarized as follows:  
* Multi-Source. A great amount of material data usually comes from different science institutions, material factories and manufacturing enterprises that make all kinds of materials.
* Heterogeneity. The heterogeneity of material data means that different kinds of material data are usually represented in different structures, e.g., relational tables, semi-structural formats, and unstructured texts.
* Unknowingness. For the time being, material science still has a bottleneck in determining what features can affect material performance. Material data are corresponding to many attributes; many materials still have unknown features.
* Irretrievability. Many materials that are important for manufacturing, the aerospace industry and transport undertakings are usually kept confidential. Material data generated from manufacturing and scientific research need to be transformed into formatted data, which is very time consuming.
* Complexity. There is a great variety of hidden correlations among material data, and it is hard to recognize data correlations accurately.
* Multi-Scale. Material data can be described in many scales: Electronic scale, atom-molecular scale, micro-scale, mesoscale and macroscale.  
Multi-source heterogeneous data fusion aims to combine material knowledge to reinforce the value of existing knowledge, and to extract the most valuable knowledge for material discovering, so it is necessary for people from material science and people from computer science to work together, which increases the difficulty of data fusion. The data conflicts in multi-source data are still a difficult problem to solve. The current mainstream method is to calculate the weight of data sources in terms of credibility, and then calculate the weighted average, but the details of the calculation method (e.g., the credibility of databases) are completely determined under specific situations, so it is very difficult to find a unified set of conflict resolution methods for material data fusion. Table 1 summarizes the major material data types and data formats.