## 1 Introduction  
The technical solutions and processes that are specific to the water industry present a very heterogeneous characteristic, being simultaneously highly dispersed from a geographical standpoint, thus contributing to defining a landscape currently prevailed by legacy systems. Although viable solutions at the time of their implementation, the large majority of legacy systems that can be found today in the water industry are inefficient by current standards, thus transforming the water industry into the perfect environment for receiving quality, availability, and efficiency improvements. The Industrial Internet of Things (IIoT) concept [1; 2; 3; 4] groups a wide spectrum of industry-oriented principles [5; 6], aimed at different objectives, most of which can be achieved by developing intelligent communication between different technical entities. This aspect places the superior and enhanced connectivity, interoperability, and information exchange at the core of the IIoT paradigm, which transposes from a practical standpoint to a continuous effort to connect industrial computers, sensors, and actuators to the internet [7]. The improved communication as a result of the IIoT implementation opens the way for developing more intelligent and sophisticated future systems, capable of improving the working characteristics of technical systems (such as availability, cost reduction, safety, productivity), by applying autonomous optimization techniques to the systems [8]. The importance of developing the interoperability between industrial systems is emphasized in [9], where the authors propose a communication framework that brings both efficiency and interoperability improvements for service-based architecture. Their work prepares the appropriate context for implementing future distributed monitoring and control applications. Such advances in industrial communications have recently led to the emergence of the ideas around bringing both fog computing and Big Data concepts closer to the industry, in accordance with the IIoT guidelines, because of the data accumulation phenomenon that occurs. In this direction, [PERSON] et al. conclude in [10] that a middleware support is required between the industrial environment and the cloud services. This middleware can be represented by the fog computing [11], which is capable of providing local computing support. On the other hand, the large amount of generated data requires Big Data technologies in the industry, and the paper from [12] presents a survey of the Big Data solutions that are already integrated into the industry, while [13] focuses on identifying the high performing Big Data techniques that can be applied to industrial data. Despite classic Historian applications [14; 15; 16] that store the large amounts of generated data into industrial environments dominated by the IIoT principles are widely available and do not represent a novelty anymore, the most recent trends in this area are to develop solutions that can make use of the already-gathered data. The future Historian application will be intelligent and proactive, using the stored data to identify dependencies and relations between the characteristics of the monitored system, which, in turn, will be used to predict the future evolution of the technical system, as a starting point for optimizing the monitored system, in an autonomous, non-invasive, unassisted-by-human manner, being placed in the fog computing area close to the monitored system. Although such an Historian application cannot be identified to date, progress is being made toward this development direction. The study from [17] proposes efficiency improvements to a classic Historian application, while a distributed and configurable data analytics infrastructure is presented in [18]. Another distributed, wireless monitoring system is presented in [19], the problem of predictive maintenance is tackled in [20], and lightweight, Raspberry Pi-based, classic Historian solutions are presented in both [21] and [22].