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Collecting and representing manufacturing knowledge
Published in Justyna Patalas-Maliszewska, Managing Manufacturing Knowledge in Europe in the Era of Industry 4.0, 2023
An important element of BI systems is the process of online data analysis and processing, namely OLAP, which enables the multidimensional analysis of business data, achieved through integration and aggregation, and appropriately presenting and visualising various types of data (Surma, 2009). OLAP and can be divided into multidimensional OLAP (the multidimensional table provides the user with quick access to data but can cause performance problems with large amounts of dimensions, data redundancy, and data overflow), relational OLAP (the relational database model, which significantly retards the data searching process) and hybrid OLAP. Hybrid OLAP uses two database models – relational and multidimensional – thanks to which the data takes up less space than does multidimensional OLAP, but usually operates on a relational structure. This kind of OLAP system works well with large databases where fast access to data aggregation is required. Figure 4.2 presents the architecture that integrates knowledge collection and I4.0 technologies into a customer-oriented company.
Design pragmatic method to low-carbon economy visualisation in enterprise systems based on big data
Published in Enterprise Information Systems, 2021
Zhigui Guan, Yuanjun Zhao, Xingdong Wang
The star model is selected for data warehouse design. The star model is mainly composed of a fact table and multiple dimension tables, and each dimension table is only connected with a unique fact table (Xiong et al. 2020). The data organisation of the star model is intuitive, and it pre-processes the data of each dimension during the construction of the data mart, so it has a fast data access speed (Drzadzewski and Tompa 2016). The logical model of data warehouse is a multidimensional database. OLAP is also based on multidimensional analysis. In BI intelligent data analysis, data cubes are undertaken to achieve multidimensional analysis. The data cube is composed of two elements: dimension and measure. The dimension is the angle from which the user observes the data, and the measure is the actual data value. The data cube is pre-stored in the data warehouse. When the OLAP engine receives a data query request, it converts the MDX statement into an SQL statement, retrieves the target data set from the data warehouse, stores it in online analytical processing memory, and finally passes it to the view layer for reading by the foreground analysis tool.
Graininess characterization by multidimensional scaling
Published in Journal of Modern Optics, 2019
E. Perales, F. J. Burgos, M. Vilaseca, V. Viqueira, F. M. Martínez-Verdú
The multidimensional analysis provides statistical results about the dissimilarities or differences between samples, and a good correlation between the visual differences provided by the designed visual experiment and those obtained by the multidimensional analysis was verified.