Article Preview
TopIntroduction
The cloud model has been applied in various domains, including decision-making, pattern recognition, data mining, and expert systems. It allows for the modeling and reasoning of uncertain and imprecise information, enabling more accurate and robust analysis of complex problems. Many concepts in real-world problems need to be described by multiple metrics, i.e., multi-attribute, multidimensional problems. The traditional cloud model normally suffers from an evaluation process, thus as the size of the data set increases, its operation efficiency decreases; there also exists a dilemma that biased evaluation results may yield when there is a large difference in the scales of each evaluation level interval. To solve such problems, a multidimensional cloud model can be considered (Li & Du, 2017).
Further, CiteSpace visualization is used here to present the structure, and distributional characteristics for the research of a multidimensional cloud model. CiteSpace information visualization software can present the new dynamics of a certain scientific field in future developments (Chen, 2006) and draw a visual analysis chart of literature author collaboration, research institution collaboration, and literature keyword co-occurrence. By analyzing the size and number of nodes in the graph, as well as the density of connecting lines between nodes, the current research hotspots and future research trends in this field are analyzed.
Keywords are a cluster of natural language words with substantial meaning that express the thematic characteristics of the content of the article. Reading the literature first, locating the keyword section can yield the article’s theme, research object, research methodology, etc. Similarly, search keywords can realize the paper's information to find and summarize. Thus, the node information is set as keywords in CiteSpace and visualized as a graph; secondly, a series of intuitive knowledge graphs are used to show the hot keywords of the multidimensional cloud model and their evolution direction in foreign research. Keyword co-occurrence analysis graph in Web of Science (WoS) regarding multidimensional cloud modeling (see Figure 1), where intricate solid lines come together to form dots (nodes) that indicate how many keywords appear in the literature. The larger the dot, the higher the frequency of the keyword, and the thickness of the solid line connecting the dots indicates the strength of the link between the keywords; the thicker the solid line, indicating that the keywords appear in the same article, the greater the intensity (Chen, 2016).
Figure 1. Multidimensional cloud model (WoS) keyword co-occurrence graph
Figure 1 demonstrates the co-occurrence graph of the terms cloud model, cloud computing, multidimensional, multidimensional cloud, and data mining. Other keywords are centered on the cloud model, spreading out in all directions to form a mesh, and each of the nodes is connected through the nodes, and then extended to the multidimensional model, multidimensional cloud, and the degree of affiliation composed of the other groups by the nodes, and the connection between the nodes to form a whole with a certain relationship. Further, on the prospect of nodes, Figure 1 shows that the dots for keywords, including cloud model, field framework, big data, cloud computing, and algorithm are significantly larger than the dots of other keywords, which indicates that the number of times these keywords appear is relatively frequent. In addition, the network connecting the words is intricate and complex, which means that the closer the connection is, which leads to the conclusion that these words belong to the hot vocabulary in the current research field of cloud modeling. Next, the keyword clustering analysis of the literature obtained from the WoS database can visualize the aggregation of the multidimensional cloud model. Figure 2 shows that the keywords mined from the WoS database are clustered into multiple word clouds, each of which describes the main research directions of the multidimensional cloud model from 2005 to 2022.