Introduce el texto aquí

Introduce el texto aquí

1. Data come from different areas, such as product and process design, assembly, material planning, quality control, scheduling, maintenance, fault detection and cover all the product life cycle phases.

2. Industry 4.0

   • Germany proposed the concept of Industry 4.0 , whose the main goal is to develop smart factories for higher competitiveness and flexibility
     • In this context “smart” is strictly connected with “information”, since the ability to extract information from data is needed in order to make the manufacturing process more intelligent

3. Data analytics wants to analyse raw data in order to discover hidden patterns and relationships among different variables

   • -to understand how to capture data along the production process. • to understand how to analyse data in order to extract useful knowledge about product, production process and product life cycle; • to understand how to share information among all the different stakeholders, along all the product life cycle.

4. clasification of data mining

   • Data mining is the extraction of implicit, previously unknown and potentially useful information from data.
   • Machine learning is the field that developed most of the algorithms to find and describe structural patterns in data. So, we can say that machine learning represents the technology instruments of data mining.
   • Data analytics, or equivalently data analysis, is a trend word, since it is strictly connected with the buzzword “Big Data”. Actually we can say that data analytics is a more comprehensive term respect to data mining, since incorporates a great deal of statistical thinking.
   • Big Data has now become a ubiquitous term in many sectors in industry and academia. Recent works invested time and effort in the proposition and the acceptance of a standard definition of Big Data

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