# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
### Data Fusion for Data Analysis
#### 6.4.1 Data Types  
At present, the amount of unstructured and semi-structured information is becoming increasingly large (according to statistics, semi-structured and unstructured data account for more than 80% of all information). A feature of semi-structured data is that they have a certain structure, but the semantics are not quite certain (for example, tags in web pages can indicate some incomplete structured information). Unstructured information (such as large-scale text information, corpus, picture, video, and audio) mostly refers to the information that users are interested in, which is not organized and is scattered in various parts of the text. Users must review and understand the information to mine the hidden meaning of the information. Generally, unstructured information cannot be clearly expressed with a unified structure, and thus it is difficult to conduct information structuring and information extraction following a unified pattern, which is fundamentally different from the information organization and management based on structured data. Therefore, in the research on data analysis and mining, the data type greatly influences the choice of the fusion technology. The fusion and analysis based on different data types are divided into the following three aspects in this paper:  
* _Fusion and analysis for structured data:_ Structured data refers to the data logically expressed and realized by a two-dimensional table structure. They strictly follow the data format and length specification and are mainly stored and managed by a relational database. The general feature is that the data are with a row as a unit. A row of data represents the information of an entity, and the attribute of each row of data is the same. Application scenarios of fusion and analysis for structured data include Enterprise resource planning (ERP), financial system, medical HIS database, education all-in-one card, administrative approval of the government, and other core databases. Structured data are mainly divided into numerical value data and type data. The fusion of structured data mainly relies on statistical methods and tree-based algorithms (such as the decision tree, Bayes, support vector machine, and random forest). This is because there is no need for tree-based algorithms to assume that type variables are continuous, and there is no need to make any assumption at the level of  
Figure 7: Data type, fusion technology, fusion target and fusion type of data analysis. type variables. Branching can be conducted with them as required to find out each state. There are also some studies exploring how to separately handle the numerical value variable and the type variable. The use of neural networks is suggested to learn these features. * _Fusion and analysis for semi-structured data:_ Semi-structured data are a form of structured data, but they do not comply with a relational database or the structure of the data model associated with the form of other data tables. However, they contain relevant tags that are used to separate semantic elements and to conduct the layering for records and fields. Therefore, it is also called a self-describing structure. In semi-structured data, entities belonging to the same type can have different attributes, and the sequence of these attributes is not important after data fusion. Common semi-structured data include XML, JSON, mail, HTML, statements, resource library, and so on. Typical scenarios of fusion and analysis for semi-structured data include mail systems, WEB cluster, teaching resource library, data-mining systems, and archive system. The fusion for semi-structured data mainly depends on data-integration technology, model-extraction technology, and the data-description pattern (tree-based description, graph-based description, relation-based description, logic-based description, and object-based description).