# Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System
## 1 Introduction  
In recent decades, technology has become a crucial element of human life, leading to various innovative and convenient advancements across numerous fields, including health [1, 2], entertainment [3, 4], social media [5, 6], physics [7, 8], and chemistry [9, 10]. While these innovations have positively impacted human life, they also demanded several technological requirements. These requirements, including computational power [11], Internet access [12], electricity [13], data [14], and other factors [15, 16], have become increasingly crucial in both academia and industry sectors. Data and data management, in particular, became the center node for solving these technological challenges, and their relevance has been further increased by the growth of machine learning methods and AI applications [14, 17, 18]. However, despite the need for huge data, research applications still lack suitable and effective data collection systems. Moreover, since many studies, especially those regarding a large part of the population, moved to mobile applications, real-time data became the strongest constraint to solve [19; 20]. These conditions have led researchers to focus on improving and creating novel data collection systems to facilitate the technological advances in the research activity. These data collection systems are widely used in many studies on topics as different as brain signals [21], earthquakes [22], weather conditions [23], etc. In this context, Representational State Transfer (REST), the most widely used web-based architecture in both the academic literature and industry, was introduced in 2000 as a Ph.D. thesis by [PERSON] [24] for leading the design and development of the architecture of an Internet-scale distributed hypermedia system. It facilitates the caching of components to reduce user-perceived latency, enforce security, and encapsulate legacy systems [24]. REST employs the Hyper Text Transfer Protocol (HTTP) to enable communication between clients and servers. Its structure provides several advantages, including modifiability and statelessness, which enhance interoperability [25]. These advantages bring substantial benefits to the management of real-time data. In addition to the solutions and limitations associated with real-time data management, it is widely recognized that the real-time management of diverse data types presents unique challenges due to their high density and rapid flow rates [26]. Eye-tracking data in particular present this complexity due to their highly dynamic and rapidly changing nature [27; 28; 29]. Furthermore, an eye-tracking pattern is an indirect measure of the complex biological system behind it, which requires high-cost computational methods for analysis with models, creating a major problem for the smooth real-time streaming of data. In this regard, eye tracking is a powerful research tool for studying various topics, such as marketing [30], attention [31], perception [32], psychopathology [33], computer vision [34], and decision making [35; 36]. Eye tracking provides insight into the neural mechanisms at the base of exploring strategies of visual stimuli [37]. The eye-tracking technology greatly advanced in recent years, achieving greater precision and accuracy, even in real-world environments [38; 39; 34]. The history of eye tracking can be traced back to the late 1800s, with improvements in terms of comfort, wearability, and performance for a long-time measure of eye movements [36; 40; 41]. Since then, there have been important advancements that have led to the development of increasingly sophisticated eye-tracking systems [42; 43; 44]. Although physical eye-tracking devices have improved and become easier to use, the real-world employment of these devices is still not widespread due to their high cost [42; 43; 44]. This practical issue has prompted researchers to find different solutions, and many eye-tracking models using webcams have been developed [34; 39; 45].