# Information and Communication Technology Solutions for the Circular Economy
## 2 Information and Communication Technology Solutions for the Circular Economy
### Data Analysis and Artificial Intelligence Algorithms  
Solutions revolving around data analysis and artificial intelligence (AI) algorithms were extensively examined in the context of this paper. A wide range of technologies and methods employed in the corresponding solutions can be found and have been surveyed, including: big data analytics, case-based reasoning, data and model integration, data visualization, dynamic game theory, dynamic programming, evaluation models, fuzzy logic, heuristic algorithms, machine learning, recommender systems, semantic processing, and others. The use of these technologies for advancing CE is further presented in the paragraphs below. Big data is one of the most popular CE enablers. The term big data refers to large-volume, complex and ever-growing (at an extremely fast pace) datasets, coming from various sources. Traditional definitions of big data refer not only to volume but also to variety and velocity as key aspects, usually referred to as \"3 Vs\" [39]. 4V or 5V models are also popular, including data veracity and data value as the fourth and/or fifth \"V\". [PERSON] et al. integrate aspects of the ReSOLVE model of CE with the 4 Vs of big data management. In addition to this, they spotlight the importance for the CE of novel big data-based business models [40]. A significant aspect in the area of big data is data visualization. Data visualization refers to the representation of a huge amount of data in ways that enhance situation awareness, e.g., into pictorial or diagrammatic representation. Two very popular technologies using data visualization are augmented reality (AR) and virtual reality (VR). In AR, virtual objects are linked to the physical objects. AR can be implemented by devices ranging from micro-size to big screens. [PERSON] et al. propose an AR product customization application, enabling original equipment manufacturers (OEMs) to design their manufacturing networks in a more efficient and cost-effective manner [41]. Among other AR solutions, [PERSON] et al. describe the potential of an AR-based system for excavators, enabling workers to detect and visualize buried utilities, thus helping prevent accidental utility (water/wastewater pipes, conduits, cables, etc.) damage and their subsequent severe economic and environmental impacts [42]. In VR, a user is submerged into a full 3D experience, in which the physical objects are linked to the virtual world [43]. A case of VR application for optimizing the WEEE disassembly process [35] is presented in Section 2.3. Another popular technology in the CE domain is fuzzy logic. Fuzzy logic can be described as a type of many-valued logic which models vagueness and uses natural language to represent and handle partial truth in practical applications in a consistent manner [44]. Based on the main philosophy of fuzzy logic, [PERSON] et al. demonstrate an analysis model of ecological suitability of land, using fuzzy comprehensive evaluation and geographic information system (GIS) data. The proposed solution can serve as a scientific basis for sustainable construction and development of cities [45]. [PERSON] et al. describe a waste analytics system which achieves accurate waste prediction and offers construction waste (CW) minimization opportunities. This system is based on the adaptive neuro-fuzzy inference system (ANFIS) technology [46]. Machine learning represents a major pillar in many popular applications nowadays, and the CE domain is no exception. Machine learning includes computer methods and algorithms, which can adapt, learn and improve through experience [47]. [PERSON] et al. explain how machine learning and data-driven approaches can improve corrosion management, enabling material efficiency and asset preservation [48]. [PERSON] et al. stress the contribution of monitoring centers for water, energy and waste management, using machine learning techniques in optimizing resource consumption of municipal, commercial and industrial clients [49]. [PERSON] et al.