## Literature Review
### Big Data Processing  
With the increasing number of IoT and sensing devices, data generated from manufacturing systems are expected to grow exponentially, producing so called \"big data\" [16]. Big data is often described in terms of 4 V's. The first V is volume in reference to the size of the data, the second V is variety in reference to the different types/formats of the data, the third V is velocity in reference to the speed of data generation, and the last V is veracity in reference to the reliability of the data [61]. The data generated during manufacturing is increasing daily with different types and formats (i.e., process logs, events, images, and sensor data), hence, the processing and storage of these data is becoming a challenging issue that needs to be addressed. There are several applications of big data analytics in the manufacturing industry. [PERSON] et al. proposed a big data framework for reducing energy consumption and emission in an energy-intensive manufacturing industry [17]. The proposed system consists of two components, data acquisition for gathering the energy data and data analytics for analyzing the energy usage. Based on a real-case implementation, the results showed that the proposed system was capable of eliminating three percent of the energy consumption and four percent of energy costs. [PERSON] et al. proposed a big data system for logistics discovery from RFID-enabled production data for mining knowledge [18]. An experiment was used to demonstrate the feasibility of the proposed system and the results showed that the knowledge gained from big data could be used for production scheduling and logistics planning. [PERSON] et al. studied the application of big data analytics for mitigating supply chain social risk [19]. A case study was used to elaborate the application of big data analytics in the supply chain. The results of the study revealed that big data analytics can help management predict various social problems and mitigate social risks. Finally, [PERSON] et al. proposed a big data framework for active sensing and processing of complex events in manufacturing processes [20]. To effectively process complex event big data, a relation model and unified XML-based manufacturing processes were developed. The Apriori frequent item mining algorithm was used to find a frequent pattern from the complex events data. The feasibility and effectiveness of the proposed system was confirmed with implementation in a local chili sauce manufacturing company. The proposed model is expected to provide practical guidance for management decision-making. Several big data technologies can be utilized in the manufacturing industry to process and store large volumes of data quickly, such as Apache Kafka, Apache Storm, and NoSQL MongoDB. Apache Kafka is a scalable messaging queue system used for building real-time applications [62]. It is fault-tolerant, high-throughput, and scalable. Several studies have shown significant benefits from using Kafka for healthcare, transportation, manufacturing, and IoT-generated sensor data. [PERSON] et al. proposed real-time data processing for monitoring diabetic patients [21]. Apache Kafka and MongoDB were utilized to handle and store sensor data from the patients. The proposed system was sufficiently efficient at monitoring diabetic patients. [PERSON] et al. proposed a cloud-based car parking system consisting of several technologies, including Apache Kafka [63]. The proposed system was capable of efficiently handling massive amounts of sensor data when the amount of data and the number of clients increased. [PERSON] et al. proposed a framework for handling real-time IoT event data [22]. The proposed framework utilized Apache Kafka as a message queue system and was efficient enough to process real-time IoT events data. [PERSON] et al. proposed a framework based on big data technologies and machine learning for online fault prediction for wind turbines [23].