## 2 Literature Overview
### Big Data
They note that industrial Big Data introduction is a big challenge for companies, as it includes both hardware and software changes. The authors address this question in accordance with the lifecycle of a product, making their solution different from the traditional Business Process system [19]. [PERSON] and [PERSON], in their study (2021), use two research methods--systematic search in bibliographic repositories and bibliographic verification of the obtained results--to discuss a research gap in Big Data mining for customer insight. Their goal is to provide a theoretical ground to the discussion by organizing knowledge on the role of Big Data mining in understanding customers' behavior [20]. Prediction of prices is also an important factor in various fields, including energetics, as presented in the study by [PERSON], [PERSON], and [PERSON] (2018). They note that participants in electric energy market have been dependent on price predictions already for a long time. The authors compare individual methods and technologies, including Big Data and Deep Learning, assigned for prognosing [21]. Outside industry, Big Data is used in many fields. [PERSON], [PERSON], [PERSON], and [PERSON] examine (2021) applications of Big Data in human resource management (HRM). They started with conducting a bibliometric review to quantitatively assess and scientifically map the evolution of current literature on HDM. They then proceeded with using a clustering algorithm based on Big Data research paradigm. Based on the discoveries, they proposed a general theoretical framework of Inductive vs. Deductive, where Inductive represents prediction paradigm of data mining and theory building, and Deductive means the explanation paradigm of theory testing [22]. Along with the fourth industrial revolution, also the fourth agricultural revolution, or Agriculture 4.0, takes place. It has many common features with Industry 4.0, using the same methods and technologies--Big Data, biotechnology, artificial intelligence, IoT, etc. [PERSON], [PERSON], and [PERSON], in their study (2021), discuss the possible implications of application of Agriculture 4.0 in the Sub-Saharan Africa. They examine the readiness of the area based on a number of metrics, such as scale, finance, technology, governance, and education. They find out that, while there is a potential for implementation, there are currently certain gaps in knowledge, skills, finance, and infrastructure [23]. Technologies and concepts to alleviate the manipulation with HDV keep being developed for the purpose of many fields, including medicine. One of these technologies is Robotic Process Automation (RPA), the aim of which is to unburden people from daily, repetitive tasks. [PERSON] in their study (2019) investigates, how RPA can help with processing the medical data (called also Medical Big Data in the context) from various sources, from medical care to the pharmacy industry. The introduction of Machine Learning (ML) into the field can make numerous operations, including designing new medications, their quality, availability, and distribution speed, easier. The author also proposed a concept based on RPA/ML, which could be helpful in standardization of application of traditional Indian medicine, Ajurveda [24]. Data acquired from patients can provide a significant help already in the diagnostic phase, and therefore help preventing various crucial health problems. This applies to both the physical and mental health of patients. [PERSON], [PERSON], and [PERSON] (2019) deal with the digital phenotyping of mental health (DPMH). The authors identify key factors connected to the mental health, and then they use data from various sensors, social media, and medical centers to estimate mental health of the patient. They focus mostly on detection of anomalies in behavior, detecting affective disorders, cognitive analytics, social analytics, and biomarker analysis intervening with psychiatric context [25].