Big Data for Modern Industry: Challenges and Trends [Point of View]

By Shen Yin, Okyay Kaynak. Proceedings of the IEEE, 2015

551 citations recorded by the source corpus.

Subfields: Information Systems and Management, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/93b00fd9a9a5f3424d4f2aad7c821f6b33d1d064

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Jun Zhang
2. Lian Duan
3. Lida Xu

In Management Information Systems

1. Lian Duan
2. Lida Xu
3. Shancang Li
4. Fei Tao
5. Wu He
6. Qinglin Qi

In Management Science and Operations Research

1. Yew-Soon Ong
2. Zhi-hui Zhan
3. Okyay Kaynak
4. Jianfei Cai
5. Kay Chen Tan
6. Jun Zhang
7. Lian Duan
8. Haitao Liu

Citations in the corpus

• A survey on evolutionary computation for complex continuous optimization (2021, background): “For example, in the big data environments, the complex optimization problems, like many other big data problems, always have the so-called 4-V challenges as Volume, Velocity, Variety, and Value (Yin and Kaynak 2015), which respectively mean the amount of data, the speed of change, the range of data,”
• A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes (2021, background): “While we have seen evident increases in the availability of physical sensors, the collected yet underexploited raw data will eventually become data garbage without proper data analysis, information extraction and knowledge exploitation techniques [1]–[3].”
• Random Forests for Big Data (2015, background): “, the advisory company about information technology research (1), now often augmented with other Vs [6].”
• Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance (2017, background): “0 concept in Germany [5] and Cyber Physical Systems (CPS) in the US [6] are the most representative overall schemes that have aroused a new round of worldwide interest in advanced manufacturing [7].”
• Big data for cyber physical systems in industry 4.0: a survey (2019, background): “…Lee (2008), Khaitan and McCalley (2015), Lee (2015), Lee et al. (2015a), Lee, Bagheri, and Kao (2015b) are related to cyber physical systems in industry 4.0, and Lee et al. (2013), Lee, Kao, and Yang (2014), Lee et al. (2015a), and Yin and Kaynak (2015) are related to big data in industry 4.0.”
• A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (2019, unknown): “challenges to the industry [5].”
• Quo vadis artificial intelligence? (2022, background): “Third, the rise of AI is associated with a significant increase in computing power [42, 43].”
• When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, background): “Big data in the GP community mainly refers to one of the 5V challenges [13]: the volume that represents the huge amount of data points to be stored, processed, and analyzed, incurring high computational complexity for current GP paradigms.”
• An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (2016, unknown): “and fault diagnosis of machines [5].”