Data and knowledge mining with big data towards smart production

TL;DR: This paper aims to provide a survey-based tutorial on potential applications and supporting technologies of Industry 5.0 from the perspective of different industry practitioners and researchers.

TL;DR: Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and more efficient as discussed by the authors. But, the benefits of smart manufacturing are limited.

TL;DR: This novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models and can support petrochemical and other process manufacturing industries to dynamically adapt to the changing environment.

TL;DR: The security challenges of smart agriculture are analyzed and organized into two aspects: 1) agricultural production, and 2) information technology.

TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.

TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.

TL;DR: Three algorithms are presented to solve the problem of mining sequential patterns over databases of customer transactions, and empirically evaluating their performance using synthetic data shows that two of them have comparable performance.

TL;DR: This Second Edition of Building the Data Warehouse is revised and expanded to include new techniques and applications of data warehouse technology and update existing topics to reflect the latest thinking.