# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 2 Literature Review
### Big Data  
Big Data plays a crucial role in the fourth industrial generation, as highlighted in various studies. In [17], its influence spans areas such as improving fault tolerance and predicting machine states. The authors of [11] emphasized the significance of Big Data in rapid failure detection for smart manufacturing, essential for enhancing industrial maintenance practices. The authors also noted its importance in additive manufacturing with 3D-printing technologies [25] and machine design. Moreover, the inclusion of bottleneck detection is considered as part of its broader application in critical analysis.  
In [10], the author explored challenges in information processing, focusing on computational complexity and the difficulty of extracting valuable insights from vast amounts of raw data. The discussion included challenges in monitoring the health status of factory machines using cloud computing and the need for high-performance local machines for effective local computing. The development of new distributed systems for data analysis is considered a challenge, requiring substantial computing power. Additionally, there is a debate on whether processing such massive amounts of data is necessary, with some authors emphasizing the importance of maintaining quality information over quantity.  
In [11], the researchers outlined key analytical methods for industrial Big Data. Descriptive analytics involves interpreting historical business-oriented information, while predictive analytics enables the detection of future events based on historical data, particularly useful in applications such as predictive maintenance. When combined with statistical models and AI, predictive analytics allows for predicting machine trends and behavior. Lastly, prescriptive analysis contributes to better decision-making, closely linked with predictive analysis to define actions based on prediction results.  
The conclusions we drew from the authors in relation to the context of this work are:* It is necessary to select which data to use to balance the cost and complexity of processing large amounts of information.
* Data quality: use cases where bottlenecks are reproduced should be captured and a specific dataset with key variables should be generated for analysis.
* Data analysis: predictive analysis has been proven by the authors to be the most effective way to analyze historical information and predict future events.  
The Figure 1 displays a series of sets with nodes that act as data sources. The concept it represents is the grouping of these data sources into subsets that are crucial for specific analyses, such as aiding in the identification of bottlenecks in production lines.  
Supervised SVM Algorithm to Build a Prediction Model in Big Data  
In the realm of Machine Learning [26], supervised learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (KNN), are utilized for developing predictive models based on input and output data, particularly in the context of predictive maintenance, according to [27]. Additionally, the authors of [28] demonstrated the use of SVM classification for weld quality prediction and its potential in real-time monitoring systems, emphasizing the importance of an appropriate feature selection method for achieving higher accuracy.  
Therefore, since our system monitors sensors in real time, we will leverage this synergy to utilize the algorithm. By adjusting it, we will use it for data training, consequently making future predictions on sensitive datasets for bottleneck detection. On this same SVM algorithm, the authors of [29] cited it as recursive for predicting a relationship between product shape elements and product images. Although these authors likewise considered the k-Nearest Neighbors (KNN) algorithm in their analysis. This algorithm will also be used to make comparisons with SVM and will have an open-contrast base.