China 
Africa 
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The three-axis approach to analytics projects
Published in Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný, Data Analytics Initiatives, 2022
Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný
Source data are stored in the source systems (transactional ones). If we want to leverage them, we need access to the source system directly, which could be problematic for many reasons. One of them is the performance of the source system itself – it is not usually designed for analytical tasks. So, if you run a complex query against the transactional system, the system itself may encounter performance issues (as it is designed for transactional tasks, but not for analytical ones). That is why the data from the source system is usually moved to new data storage for analytical purposes.
Quantitative Modeling of Electricity Consumption
Published in Yi Chen, Yun Li, Computational Intelligence Assisted Design, 2018
The fitness function represents the approximation to the national electricity consumption. As shown in Figure 21.1, four steps are involved in defining the fitness function. Step 1 collects raw data from specific data sources, including yearbooks and research reports. Step 2 screens, filters, and pre‐processes the source data. Step 3 estimates the national electricity consumption, as described in Section 21.2. Step 4 creates the fitness function given by Equation (21.2), where the fitness function is defined as the root mean square (RMS) errors of EÊC and EC0. Here, mmAP is taken as an index for the quantitative analysis driven by the CIAD approach, and EÊC is stated in Equation (21.1). The goal of this paper is to find the optimal combination of C0, Θ, and Ω that simultaneously maximizes the electricity consumption EÊC based on historical data EC0. [F=Maximize:{mmAP(RMS(EE^C(C0,Θ,Ω)-EC0))}] $$ [F = Maximize:{\rm{ }}\{ mmAP(RMS(E\hat EC({C_0},~\Theta ,~\Omega ) - E{C_0}))\} ] $$
Collaborative Workflow in an HBIM Project for the Restoration and Conservation of Cultural Heritage
Published in International Journal of Architectural Heritage, 2022
Juan Enrique Nieto-Julián, Javier Farratell, Manuel Bouzas Cavada, Juan Moyano
The LO(D) describes the graphical representation of the model and follows existing standards, from the symbolic placeholder to the detailed model (LOD 0-1-2-3-4). The choice is determined by the available reference material and the purpose of the model. The LO(I) is the embedded information contained in the BIM elements. This may include information on the materials, manufacturer, and source data. Unlike in new construction, HBIM relies on existing plans, specification documents, and on-site observations to determine the structure, material, and more. The type of embedded information is not limited to traditional categories such as materiality or size of structural components but may contain comments from the specialists (architect/archaeologist/historian/restorer) of the element analysed or if additional verification is needed.
An Enhanced Entity Model for Converting Relational to Non-Relational Documents in Hospital Management System Based on Cloud Computing
Published in IETE Technical Review, 2022
A. Samydurai, K. Revathi, L. Karthikeyan, B. Vanathi, K. Devi
An automated relational database meant for the zebrafish colony management system was proposed by Gutierrez et al. [19]. Here, a FishNET was presented in order to relate information regarding the open-source, data management by means of a relational database with the data based on the maintenance of a zebraFish. Thus, the experimental analysis was made and it revealed that this approach is more flexible, highly effective, and highly robust. Also, this approach failed to implement the inventory method to satisfy the zebrafish community requirements.