Clarifying Data Analytics Concepts for Industrial Engineering

Clarifying Data Analytics Concepts for Industrial Engineering

1. Data

   • Data analytics wants to analyse raw data in order to discover hidden patterns and relationships among different variables. The main aim of data analysis is to extract useful information from rough data and transfer it to effective knowledge to improve product and process understanding and to support decisions.
     • 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
       • Data analytics is therefore composed of: initial standard statistical techniques, such as visualization of data, selection of attributes, discarding outliers etc. in order to construct the initial example set; then, machine learning algorithms construct rules and statistical tests are used to validate them

2. Main concepts

   • Data mining
     • Data mining is the extraction of implicit, previously unknown and potentially useful information from data. The idea is to construct numerical algorithms that investigate datasets to extract hidden patterns and regularities. In data mining, the data is stored electronically and the search is automated through computers.
   • Machine learning
     • 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 mining and related machine learning techniques can be grouped into two different sets
         • 1.Supervised learning deals with predicting an outcome value based on the measures of other input variables
         • 2. Unsupervised learning deals with description of associations and patterns among a set of input data. In this case, we observe only the features and we are not able to define an output. The final aim is to find hidden patterns, to describe data and to find out how they may be organized or clustered
   • Artificial Intelligence
     • branch of computer science devoted to developing data processing systems that perform functions normally associated with human intelligence, such as reasoning, learning, and self-improvement

3. Big Data

   • “Big Data represents the Information assets characterized by such a High Volume, Variety and Velocity to require specific Technology and Analytical Methods for its transformation into Values
     • Big Data consists of extensive datasets -primarily in the characteristics of volume, variety, velocity, and/or variability- that require a scalable architecture for efficient storage, manipulation, and analysis

4. MACHINE LEARNING TECHNIQUES IN SMART MANUFACTURING

   • Supervised Learning
     • Linear Methods for Regression
       • A linear regression model assumes that the regression function is linear in the variable inputs X1, … Xp. Multivariate linear regression is a generalization of linear regression, by considering more than one dependent variable
     • Linear Method for Classification
       • The linear classifier uses the variable’s characteristics to identify which class (or group) the variable belongs to. Classification learning operates with a training example and an actual outcome, the class of the example
     • Decision Tree Based Method
       • A “divide-and-conquer” approach to the problem of learning from a set of independent instances leads naturally to a style of representation called a decision tree.
     • Neural Network
   • Unsupervised Learning
     • Principal Component Analysis
       • Principal Components Analysis (PCA) is a quite popular method to transform a high dimensional dataset into a lower dimension dataset.
     • Association Rules
       • Association rules emerged as a tool for mining commercial databases.
     • Cluster Analysis
       • Cluster analysis implements the grouping or segmentation of different objects into subsets or clusters as primary goal, meaning that objects in the same cluster are more similar than other objects assigned to other clusters.
     • Self-Organizing Maps (SOM)