# A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
## 4 Data Mining with Big Data Applications in the Electronics Industry
### Diagram of Data Content for Different Knowledge Areas  
From the view of electronics lifecycle, the main data for different knowledge areas can be divided into engineering data, enterprise resource and environment data, production plan and arrangement data, manufacturing result data, and transaction and interaction related data. Figure 9 illustrates the main content of each category and its application for different knowledge areas. The detailed description of each category is given as follows. 1 Engineering data: It includes product structure and function, manufacturing process plans, and quality requirements to define what is to be manufactured and how to manufacture. Relevant DM with Big Data applications have been conducted to improve product quality and customers' satisfaction or to optimize process parameters. This data can be stored in different systems such as PLM and computer-aided process planning system with structured (bill of materials), semi-structured (requirement reports), and unstructured (design model or drawing) styles. 2 Enterprise resource and environment data: Resource data relates to the workplace, equipment, and tools that specify where and what resource are required to manufacture the product, which also includes data on process statuses, collected in real time by smart sensors and the traced data based on RFID placed on transportation robots. In common, these data are structurally stored in ERP, MES, and DCS that can be used for the optimization of process control such as AEC/APC and production management. Taking an example from wafer fab, the equipment status data such as chamber pressure, gases flow, and chuck temperature are collected in real time by sensors placed on tools, and valuable data that are generated from clean room environment monitoring [101]. 3 Production plan and arrangement data: These data include the plan of the project, the hierarchy production plan, the inventory/material and procurement plan, and scheduling that defines when and how many products have to be manufactured. Different plans can be stored in ERP and MES with  
Figure 9: The data content for different knowledge areas. a structured style, which has been widely used for the optimization of PMO tasks such as production time prediction and scheduling optimization at the production stage. 4 Manufacturing result records: Result records define the quality and quantity of products at a certain time and workplace. They are always accumulated in MES, quality management system, ERP, and storage management system with a structured style. RFID has been widely used for product lifecycle management in recent years, and the traced data generated automatically at different stages through RFID placed on materials, semi-products, and finished products can also be taken as the data of manufacturing result. Taking an example from the data involved in the wafer fab, it is generated at various steps including inline through metrology steps that measure test wafers and product wafers such as parameters of critical dimension, film thicknesses, film resistances, and so forth. It also includes electrical test and final yield data. DM-based post hoc diagnosis, yield prediction, and parameters adjustment are used to ensure the future quality has been conducted based on different steps of the result. They can also be combined with enterprise resource and environment data for AEC/APC. 5 Interaction and transaction data: Owing to the fast development of online trading and electronic commerce in the past decades, a large amount of records related to transactions and online interactions between upper stream supplier, middle collaborator, downstream customer have been accumulated.