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Data and information visualization (data viz/vis or info viz/vis) [2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount [3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.
A chart (sometimes known as a graph) is a graphical representation for data visualization, in which "the data is represented by symbols, such as bars in a bar chart, lines in a line chart, or slices in a pie chart". [1] A chart can represent tabular numeric data, functions or some kinds of quality structure and provides different info.
A graph or chart or diagram is a diagrammatical illustration of a set of data. If the graph is uploaded as an image file, it can be placed within articles just like any other image. Graphs must be accurate and convey information efficiently. They should be viewable at different computer screen resolutions.
In addition, the choice of appropriate statistical graphics can provide a convincing means of communicating the underlying message that is present in the data to others. [1] Graphical statistical methods have four objectives: [2] The exploration of the content of a data set; The use to find structure in data; Checking assumptions in statistical ...
Decision tables are a concise visual representation for specifying which actions to perform depending on given conditions. Decision table is the term used for a Control table or State-transition table in the field of Business process modeling; they are usually formatted as the transpose of the way they are formatted in Software engineering.
The use of visual representations to transfer knowledge between at least two persons aims to improve the transfer of knowledge by using computer and non-computer-based visualization methods complementarily. [8] Thus properly designed visualization is an important part of not only data analysis but knowledge transfer process, too. [9]
Graph databases portray the data as it is viewed conceptually. This is accomplished by transferring the data into nodes and its relationships into edges. A graph database is a database that is based on graph theory. It consists of a set of objects, which can be a node or an edge.
Figure 1: Unidentified model with latent variables (and ) shown explicitly Figure 2: Unidentified model with latent variables summarized. Figure 1 is a causal graph that represents this model specification. Each variable in the model has a corresponding node or vertex in the graph.