# Visual Communication Design and Wireless Data Transmission Technology for Blockchain Big Data Information Presentation

Authors: Guan-Chen Liu; Chih-Hsiang Ko
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The present work is aimed at solving the difficulty of BC big data information analysis and the defects of traditional BC platform visual interface (VI), such as nonstandard layout, unreasonable color use, unclear guidance, and increased user learning cost. Firstly, this paper expounds on BC technology, the related theory of information visualization (IV), and the IV design method of BC-generated big data. Secondly, by formulating the user experience design strategy, a big data visual information sharing platform (ISP) based on behavior experience (BE) is designed. Finally, the system performance is tested. The results show that (i) the proposed BE-based big data visual ISP has the basic functions of information query and module jump. The overall interface of the platform is simple and tidy, the information layout is reasonable, the presentation method is more intuitive, and the visual effect is better. (ii) The host throughput of each system module when processing business is greater than 100 times/s, and the success rate (SR) of event handling is greater than 99%. The average response time (RT) of terminal processing is less than 0.3 s, and the average RT of the terminal side is less than 0.4 s. The system’s central processing unit (CPU) occupancy rate (OR) shall be controlled below 30%. The memory OR shall be below 30%, both of which are lower than the standard value, and the system performance meets the standard. To sum up, the proposed ISP has basic functions and ensures good operation performance. It is suitable for the IV of BC-generated big data. The purpose is to provide important technical support for the IV of BC-generated big data and improve the efficiency of users’ data information acquisition and analysis.


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## Body

## 1. Introduction

In recent years, blockchain (BC) technology, with its advantages of high security, stability, and information transparency, has become another focus after big data, artificial intelligence (AI) technology, and augmented reality (AR) [1]. With the rapid development of BC and the advent of the big data era, the volume of BC-generated data information rises exponentially, making it more difficult for users to query and understand the required information [2]. To this end, information visualization (IV) brings new ideas. In particular, IV mainly uses graphics and charts to present the disordered data information to people in a more intuitive, vivid, and interesting way to quickly obtain the required information, thereby reducing the learning cost while improving efficiency [3].Wang and Lu reasoned that big data brought great value and challenges to all social fields. The most effective way to extract information from massive complex data and present complex information more clearly was to use IV technology [4]. Turkkan found that typesetting elements greatly impacted the design effect in the process of IV design. The design considering typesetting was visually more attractive and preferable than other designs [5]. Yang et al. designed a big data analytics (BDA) and multidimensional IV platform astronomical data analysis (DA), which provided strong technical support for astronomical research topics, such as astronomical image processing (IP), moving target extraction, space target orbit calculation, numerical cosmology, cosmological simulation, and galaxy fluid simulation [6]. Hao et al. proposed an information storage platform combining BC and visualization technology against the shortcomings of the traditional food traceability system (FTS): data tampering and ineffectiveness to analyze risk causes intuitively. Then, the effectiveness of the proposed method was verified by actual case analysis [7]. Peral et al. constructed a health care- (HC-) oriented BC application over BC architecture and visualization technology, which provided an information sharing platform (ISP) for patients and doctors and was conducive to patient HC management (HCM) [8]. Yap et al. used exploratory graphic analysis (EGA) to analyze the transaction (TXNS) data of the Ethereum network, which was realized through network visualization, mathematical calculation, and statistical modeling of network data [9]. Shahzad et al. tried to solve the high server, equipment, and energy consumption cost of the cloud-based big data IV framework and proposed a green big data IV solution based on BC. They used hyper-ledger sawtooth to optimize the utilization of organizational resources to support the current distributed IV platform and ensure security and data availability with lower storage costs [10].The existing research results indicate that IV and BC technologies have been popularized and used in many fields. IV can provide convenience for people to analyze data quickly and effectively. Applying it to the analysis and research of BC big data will also greatly improve the analysis quality and efficiency. But currently, people pay more attention to the practicability of the above two technologies, not yet fully considering the level of user experience. The traditional BC platform visual interface (VI) generally has defects, such as nonstandard layout, unreasonable color use, and unclear guidance, resulting in increased user learning costs. Based on these problems, this paper first expounds on BC technology, the IV-related theory, and the IV design method of BC big data. Secondly, this paper fully considers the problem of user experience and creatively proposes a big data-oriented visual ISP based on behavior experience (BE). The proposed platform comprises four modules: home page, IV tool, communication community, and personal center. Finally, the system performance is tested. The purpose is to provide important technical support for the IV of BC-generated big data and improve the efficiency of users’ acquisition and analysis of data information.


## 2. Key Technologies and Research Methods

### 2.1. BC Technology and IV Analysis

#### 2.1.1. BC Technology

Shortly, BC is a block-constructed time series data (TSD) chain. More elaborately, it is a distributed database based on time axis [11], which mainly adopts blocks and chain data structure (DS) to verify and store data information. Meanwhile, the data generation and update are realized by distributed node consensus algorithm, whereas cryptography is used to ensure the security of data transmission and access. Finally, automatic script code is used for programming and operation (OP) [12–14]. Figure 1 depicts the features of BC.Figure 1
Features of BC.BC features anonymousness, high transparency (HT), collective maintenance, high data security, and decentralization [15]. Decentralization enables all users to participate in the BC maintenance and data storage, rather than being managed by specified users or the system itself [16, 17]. The difference between centralization and decentralization is drawn in Figure 2.Figure 2
Difference between centralization and decentralization. (a) Centralization example. (b) Decentralization example.
(a)(b)As manifested in Figure2, centralization enables users’ TXNS and OP to be carried out through the central organization, where all their information can be stored. Although centralization is very effective against data fraud in the TXNS process, there are certain uncertainties in data management (DM) and security, such as data loss and tampering. Comparatively, the decentralization is a more effective data loss prevention (DLP) model to avoid these problems, in which all BC users are given a complete account book to supervise their own information and that of others to verify the accuracy of data, thereby reducing the risk of arbitrary data tampering.


#### 2.1.2. Big Data-Oriented IV

With the advent of the big data era, the BC-generated data volume is seeing a high increase. Since the human brain’s text processing ability is limited in speed and efficiency, the huge amount of information undoubtedly increases users’ difficulty in acquiring and analyzing data [18]. Hence, specific IV approaches can help users quickly retrieve the required information in big data. IV is an interdisciplinary field that mainly studies large-scale IV through the presentation of graphics or images; it is a technology that maps nonspatial abstract information into an effective visual form to help people quickly understand and analyze data information [19, 20]. Specifically, IV is realized as follows: after collecting, sorting, and analyzing the data information, the designer combines the theories of graphic semiotics, IP, DA, and data statistics (DSTA) to symbolize and graphically design the information and present it on the information carrier [21]. IV has four characteristics: intuitiveness, instantaneousness, interestingness, and interactiveness [22], as depicted in Figure 3.Figure 3
IV features.IV can be divided into two-dimensional (2D) plane class and three-dimensional (3D) space class according to DIMension (DIM). Examples of two types of IV are plotted in Figure4.Figure 4
IV classification. (a) 2D plane IV. (b) 3D spatial IV.
(a)(b)At present, the 2D plane IV is the most commonly used, including graphics, charts, plane maps, and data maps; it is often used in magazines and books. The 2D IV method organically combines pictures, symbols, and text information to obtain a more vivid, straightforward, and interesting presentation. By contrast, 3D spatial IV presents information on the carrier more intuitively through the design of 3D graphics and spatial layout. Compared with the 3D plane IV, the 3D spatial IV can display better effects and images that are more in line with people’s normal way of thinking. However, it has high requirements for the designers’ professional skills and aesthetic sense, so people are more inclined to choose 2D plane IV methods [23, 24].


### 2.2. Design of BC-Generated Big Data-Oriented IV

#### 2.2.1. Design Scheme of IV

IV is mainly the process of representing the data in the information space in graphics and charts and communicating it to users. Therefore, the IV design is the key link [25]. The specific design process is outlined in Figure 5.Figure 5
IV design process.BC-generated big data are usually high-dimensional, pluralistic, and fragmented. Before IV design, it is necessary to sort out the hierarchical and parallel relationship of information, determine the dimension of information, layer the information dimension, and classify the information elements. Reasonable and effective information architecture is the basis of IV design. In the information architecture stage, it is necessary to sort out the information and architecture of big data to be characterized and determine the information level, as charted in Figure6.Figure 6
Schematic diagram of information architecture.Figure6 describes a schematic diagram of information architecture. Through the information architecture, designers can sort out the hierarchical and parallel relationship between information and separate dimensions to provide the basis for presenting the corresponding dimension information on the page according to user actions in the later stage and achieving cognitive dimensionality reduction through the selective presentation of dimensions.


#### 2.2.2. BC-Generated Big Data Information-Based Entity Relationship (ER)

It is necessary to determine the ER among the sorted structural information according to the internal relationship of the DS, which is the basis of big data-oriented IV. The data chart, including histogram, line chart, and sector chart, is the basic REPresentation (REP) of ER, in which each data entity contains a certain information DIM [26]. The ER based on Cartesian coordinate system (CCS) is the most widely used at present; in particular, CCS is the general name of right angle and oblique angle coordinate system, in which an x-axis and a y-axis intersecting at the origin form an xy plane, also known as the Cartesian plane. The most basic CCS-based ER includes polyline, histograms, scatter, and area diagrams. The rendering effect of the four data charts is drawn in Figure 7.Figure 7
Example of CCS-based ER. (a) Polyline diagram. (b) Histogram. (c) Scatter diagram. (d) Area diagram.
(a)(b)(c)(d)As presented in Figure7, the line chart focuses on the trend that the dimension of y-axis data changes with the dimension of the x-axis. The histogram emphasizes the dimensional change of the y-axis in a certain x-axis dimension interval, and a scatter chart can reflect the distribution law of data. The area chart emphasizes the changing trend of the total value. Based on the above basic graphic relationship, various graphic relationship layouts can be evolved according to the relationship between big data information dimension and representation dimensions, such as the arc diagram and matrix diagram demonstrated in Figure 8. Of these, the arc diagram can reflect the pairwise correlation between nodes at the same level, whereas the matrix graph can express the corresponding relationship between node rows and columns.Figure 8
Expanded ER. (a) Arc diagram. (b) Matrix diagram.
(a)(b)


#### 2.2.3. Visual Coding Design

Next, it is necessary to carry out a visual coding design for the BC-generated big data information and its graphic ER. It mainly codes the semantic integration and visual harmony in the low-level sense to form the dimensional mapping [27]. The data DIMs are divided into quantitative information DIM and qualitative information DIM, in which quantitative information DIM refers to the attributes of information that can be quantified. In contrast, the qualitative information DIM represents the classification attribute of information. Therefore, this paper will design visual coding from quantitative and qualitative DIM.(1) Quantitative Coding DIM. Quantitative coding DIM is mainly determined through IV to obtain the magnitude sequence distribution information in a DIM quickly; generally, quantitative coding DIM enables users to quickly understand the location, size, width, length, and angle of elements. Common quantitative coding DIMs are presented in Figure 9.Figure 9
Quantitative coding DIM.Each node’s plan position in the DIM can reflect its spatial relationship. That is, users can judge its value and size by observing the relative position of a node in the coordinate axis plane compared with other nodes. The similarity between nodes can also be determined by the distribution of each node in the plane and grouped to form the visual effect of “cluster.” Color contrast refers to the color difference between the REP color of the node and the background (BG) color, as signified in Figure10.Figure 10
Color contrast.Figure10 proves that the higher the contrast between the REP color and the BG color, the stronger the visual saliency (VS) is, and the more likely it will be noticed by the user; this feature can be used to distinguish quantitative values of DIMs. The REP DIMs of line length and width, angle and slope, and size and volume are revealed in Figure 11.Figure 11
Comparison of different length and width lines. (a) Length/width of line. (b) Angle/slope. (c) Size. (d) Volume.
(a)(b)(c)(d)The visual effects of lines will differ according to their length and width. People use longer or wider lines to represent large values, but the effective perception level of line width is less than its length. The angle and slope in REP DIM must be combined with the plane position or node color for comprehensive REP. The quantitative comparison will be more difficult when the relative positional angle is greater than 90°. The quantitative comparison will be more difficult, so acute angles are more suitable for quantitative comparison. Finally, common quantitative REP DIMs also include size and volume. Still, humans’ ability to recognize size hierarchy is limited, so size and volume are generally only used for relative comparison between nodes.(2) Qualitative Coding DIM. The qualitative coding DIM mainly uses different hues and graphic shapes to represent the attribute categories in the same DIM to distinguish different attribute categories quickly. The qualitative REP DIM of hue is illustrated in Figure 12.Figure 12
Qualitative REP DIM of hue. (a) Two hue REP methods. (b) Four hue REP methods.
(a)(b)Hues represent different attribute categories, and the more colors there are, the better the REP effect is. Figure12 manifests the visual effect of distinguishing attributes with two and four hues. Obviously, the recognizability of two-hue attributes is significantly better than that of the four-hue attribute. Therefore, in the design of IV, the number of hue types should be minimized within a reasonable range so that people can quickly obtain the required information. Different graphic shapes can also distinguish different attribute categories. Graphics (shapes) are divided into semantic featureless geometric graphics and featured semantic graphics, as depicted in Figure 13.Figure 13
Qualitative REP DIM of shapes. (a) Semantic featureless geometric graphics. (b) Semantic featured graphics.
(a)(b)Semantic featureless shapes can refer to any attribute and be marked accordingly; it contains a relatively simple structure with a more concise and comfortable VI. By comparison, given the semantic featured shapes, people can quickly obtain the corresponding information through observation without labeling; however, such shapes usually contain many nodes, resulting in a more chaotic and complex VI, which is not conducive to information access. Therefore, the two shape-based qualitative REP DIMs have their own advantages and disadvantages, which must be selected according to the actual needs during IV design.(3) VI Component Design. VI component design mainly expresses the REP semantics in a specific format. Users can gradually approach their target object by interacting with the VI components. VI components include the hyperlink, radio box, range slider, drop-down box, and the input box, as drawn in Figure 14.Figure 14
Examples of VI components. (a) Hyperlinks, radio boxes, and range sliders. (b) Drop-down boxes and input boxes.
(a)(b)As shown in Figure14, the hyperlink is a jump mechanism, in which users can be connected to the corresponding interface through a click on the hyperlink; radio boxes and drop-down boxes are usually single choices, which are used to determine the attribute of a specific DIM; the range of DIMs can be directly selected by dragging the slider, but it cannot be accurately positioned; at this time, the user can directly enter the queried information terms in the input box for accurate positioning.


### 2.3. BE-Based Big Data-Oriented ISP

The traditional BC data ISP interface generally has a complex information layout and is difficult to use, or the interface is too concise, lacks corresponding guide icons, and is less user-friendly [28]. Under such VI, learning cost is amplified, thus producing negative UE, which is not conducive to product promotion. To that end, the BC big data-oriented IV design guarantees a more convenient and quick information retrieval (IR) for users. Therefore, the user-product interaction should be fully considered in the IV design process. The VI should be concise, understandable, and smooth, thereby reducing learning costs and improving users’ satisfaction. Finally, this paper proposes a BE-based visual BC data-oriented ISP. Based on the IV design theory, the ISP is designed by fully considering the UB psychology.


#### 2.3.1. ISP Design Strategy

The design of the ISP mainly considers the BE and emotional experience (EE), as reflected in Figure15.Figure 15
The design strategy of visual ISP.Usability awareness means that every icon, format, and language expression used in the ISP VI should be designed in a way acceptable to most people; in other words, the designs can be perceived and used correctly by users, which is the basis of ISP design. Thereupon, it is also necessary to adopt appropriate methods to guide users to manipulate or approach the target object; it can be realized by layering the information from simple to deep or providing novice guidance to new users. At the same time, the ISP needs to give corresponding feedback timely to remind the user to confirm their OP while emphasizing the importance of specific content through feedback methods, such as sound or touch. The timeliness feedback is an important system performance indicator for users.Further, users’ psychological state and emotional state in product use can be collectively called the EE. Based on BE design, users’ deep emotion and consciousness should be fully considered to help to design a more humanized and interesting ISP, thereby further improving UX. Firstly, the icons and information in the VI should be accompanied by detailed prompts to help users get started quickly; these prompts and descriptions can be a pop-up window, floating layers, toast, and other modes. Besides, the unimportant information in the VI should be removed, whereas the more important information should be highlighted by font size or color contrast, namely, information noise reduction (NR); in this way, the VI will become more concise and clearer, with a better visual effect.


#### 2.3.2. Design of Visual ISP

Based on the above strategies, the visual ISP will be designed. The ISP consists of four modules: home page, IV tool, communication community, and personal center. The platform architecture is detailed in Figure16.Figure 16
Visual ISP architecture.Concretely, the home page of the visual ISP mainly focuses on information query, navigation, and information display. Users can select the query information by entering keywords or clicking the guide component in the corresponding query area. Relevant tools are displayed in the VI visualized navigation bars. By clicking the corresponding REP graphics, BC-generated data information can be visualized in different forms, facilitating user visual perception (VP). Apparently, a sound and user-friendly interaction design (ID) can attract more browsing and exploration users. The community mainly provides an ISP for users to exchange and share relevant experiences. Users can view, manage, and share account information, login information, and favorites in the personal center.


### 2.4. System Performance Test Method

#### 2.4.1. System Operation Environment and Configuration

The software environment, hardware environment, network environment, and performance test tools of the system are listed in Table1.Table 1
System operation environment and configuration table.
Operating environment typeProjectConfigurationSoftware environmentOperation system (OS)Linux OSWeb application serverTomcat6DatabaseMicrosoft SQL Server 2008BrowserGoogle ChromeHardware environmentHard disk capacity50 GMemory4 GCentral processing unit (CPU)Intel XeonEnvironmental maintenancePerformance test toolWeb Application Stress Tool (WAST)


#### 2.4.2. Performance Test Index

(1) Event Handling Success Rate (SR). The SR of the system handling events is calculated by the following equation:
(1)SR=PsPs+Pf,where Ps is the total number of successfully handled events by the system and Pf is the total number of times the unsuccessfully handled events.(2) Throughput. Throughput (denoted by transaction per second (TPS)) refers to the unit-time user request (USRQ) processed by the system. TPS is one of the key indexes in network maintenance and fault identification (FI). In particular, the throughput index mainly reflects the ability of the server to withstand pressure and its load capacity. The calculation of TPS reads the following:
(2)TPS=CNART,where CN is the concurrent number and ART is the average system response time (RT).(3) CPU Occupancy Rate (OR). CPU OR refers to the percentage of CPU resources occupied by real-time running programs in the machine, indicating the machine’s program concurrency ability at a specific point in time (PIT). The CPU OR during system OP is calculated by the following equation:
(3)OR=TcTf=CPI×ICTf,where IC is the number of instructions used during program execution, Tf is the clock frequency, Tc is the number of clock cycles, and CPI is the average number of clock cycles required to execute each instruction. CPI calculation is displayed in the following equation:
(4)CPI=∑i=1nCPIi×Pi=∑i=1nCPIi×ICiIC,where Pi is the frequency of use of class i instructions, CPIi is the clock cycles required to execute the class i instruction, n is the number of classes of all instructions, and ICi is the number of the class i instructions.This section will use the WAST to test the system’s performance. During the test, the concurrent user is 100, and the server is operated simultaneously. The system CPU OR and memory utilization rate can be controlled within 50%, the event handling SR is higher than 90%, and the average RT is less than 0.5 s, which means that the system meets the standard and can be popularized.


## 2.1. BC Technology and IV Analysis

### 2.1.1. BC Technology

Shortly, BC is a block-constructed time series data (TSD) chain. More elaborately, it is a distributed database based on time axis [11], which mainly adopts blocks and chain data structure (DS) to verify and store data information. Meanwhile, the data generation and update are realized by distributed node consensus algorithm, whereas cryptography is used to ensure the security of data transmission and access. Finally, automatic script code is used for programming and operation (OP) [12–14]. Figure 1 depicts the features of BC.Figure 1
Features of BC.BC features anonymousness, high transparency (HT), collective maintenance, high data security, and decentralization [15]. Decentralization enables all users to participate in the BC maintenance and data storage, rather than being managed by specified users or the system itself [16, 17]. The difference between centralization and decentralization is drawn in Figure 2.Figure 2
Difference between centralization and decentralization. (a) Centralization example. (b) Decentralization example.
(a)(b)As manifested in Figure2, centralization enables users’ TXNS and OP to be carried out through the central organization, where all their information can be stored. Although centralization is very effective against data fraud in the TXNS process, there are certain uncertainties in data management (DM) and security, such as data loss and tampering. Comparatively, the decentralization is a more effective data loss prevention (DLP) model to avoid these problems, in which all BC users are given a complete account book to supervise their own information and that of others to verify the accuracy of data, thereby reducing the risk of arbitrary data tampering.


### 2.1.2. Big Data-Oriented IV

With the advent of the big data era, the BC-generated data volume is seeing a high increase. Since the human brain’s text processing ability is limited in speed and efficiency, the huge amount of information undoubtedly increases users’ difficulty in acquiring and analyzing data [18]. Hence, specific IV approaches can help users quickly retrieve the required information in big data. IV is an interdisciplinary field that mainly studies large-scale IV through the presentation of graphics or images; it is a technology that maps nonspatial abstract information into an effective visual form to help people quickly understand and analyze data information [19, 20]. Specifically, IV is realized as follows: after collecting, sorting, and analyzing the data information, the designer combines the theories of graphic semiotics, IP, DA, and data statistics (DSTA) to symbolize and graphically design the information and present it on the information carrier [21]. IV has four characteristics: intuitiveness, instantaneousness, interestingness, and interactiveness [22], as depicted in Figure 3.Figure 3
IV features.IV can be divided into two-dimensional (2D) plane class and three-dimensional (3D) space class according to DIMension (DIM). Examples of two types of IV are plotted in Figure4.Figure 4
IV classification. (a) 2D plane IV. (b) 3D spatial IV.
(a)(b)At present, the 2D plane IV is the most commonly used, including graphics, charts, plane maps, and data maps; it is often used in magazines and books. The 2D IV method organically combines pictures, symbols, and text information to obtain a more vivid, straightforward, and interesting presentation. By contrast, 3D spatial IV presents information on the carrier more intuitively through the design of 3D graphics and spatial layout. Compared with the 3D plane IV, the 3D spatial IV can display better effects and images that are more in line with people’s normal way of thinking. However, it has high requirements for the designers’ professional skills and aesthetic sense, so people are more inclined to choose 2D plane IV methods [23, 24].


## 2.1.1. BC Technology

Shortly, BC is a block-constructed time series data (TSD) chain. More elaborately, it is a distributed database based on time axis [11], which mainly adopts blocks and chain data structure (DS) to verify and store data information. Meanwhile, the data generation and update are realized by distributed node consensus algorithm, whereas cryptography is used to ensure the security of data transmission and access. Finally, automatic script code is used for programming and operation (OP) [12–14]. Figure 1 depicts the features of BC.Figure 1
Features of BC.BC features anonymousness, high transparency (HT), collective maintenance, high data security, and decentralization [15]. Decentralization enables all users to participate in the BC maintenance and data storage, rather than being managed by specified users or the system itself [16, 17]. The difference between centralization and decentralization is drawn in Figure 2.Figure 2
Difference between centralization and decentralization. (a) Centralization example. (b) Decentralization example.
(a)(b)As manifested in Figure2, centralization enables users’ TXNS and OP to be carried out through the central organization, where all their information can be stored. Although centralization is very effective against data fraud in the TXNS process, there are certain uncertainties in data management (DM) and security, such as data loss and tampering. Comparatively, the decentralization is a more effective data loss prevention (DLP) model to avoid these problems, in which all BC users are given a complete account book to supervise their own information and that of others to verify the accuracy of data, thereby reducing the risk of arbitrary data tampering.


## 2.1.2. Big Data-Oriented IV

With the advent of the big data era, the BC-generated data volume is seeing a high increase. Since the human brain’s text processing ability is limited in speed and efficiency, the huge amount of information undoubtedly increases users’ difficulty in acquiring and analyzing data [18]. Hence, specific IV approaches can help users quickly retrieve the required information in big data. IV is an interdisciplinary field that mainly studies large-scale IV through the presentation of graphics or images; it is a technology that maps nonspatial abstract information into an effective visual form to help people quickly understand and analyze data information [19, 20]. Specifically, IV is realized as follows: after collecting, sorting, and analyzing the data information, the designer combines the theories of graphic semiotics, IP, DA, and data statistics (DSTA) to symbolize and graphically design the information and present it on the information carrier [21]. IV has four characteristics: intuitiveness, instantaneousness, interestingness, and interactiveness [22], as depicted in Figure 3.Figure 3
IV features.IV can be divided into two-dimensional (2D) plane class and three-dimensional (3D) space class according to DIMension (DIM). Examples of two types of IV are plotted in Figure4.Figure 4
IV classification. (a) 2D plane IV. (b) 3D spatial IV.
(a)(b)At present, the 2D plane IV is the most commonly used, including graphics, charts, plane maps, and data maps; it is often used in magazines and books. The 2D IV method organically combines pictures, symbols, and text information to obtain a more vivid, straightforward, and interesting presentation. By contrast, 3D spatial IV presents information on the carrier more intuitively through the design of 3D graphics and spatial layout. Compared with the 3D plane IV, the 3D spatial IV can display better effects and images that are more in line with people’s normal way of thinking. However, it has high requirements for the designers’ professional skills and aesthetic sense, so people are more inclined to choose 2D plane IV methods [23, 24].


## 2.2. Design of BC-Generated Big Data-Oriented IV

### 2.2.1. Design Scheme of IV

IV is mainly the process of representing the data in the information space in graphics and charts and communicating it to users. Therefore, the IV design is the key link [25]. The specific design process is outlined in Figure 5.Figure 5
IV design process.BC-generated big data are usually high-dimensional, pluralistic, and fragmented. Before IV design, it is necessary to sort out the hierarchical and parallel relationship of information, determine the dimension of information, layer the information dimension, and classify the information elements. Reasonable and effective information architecture is the basis of IV design. In the information architecture stage, it is necessary to sort out the information and architecture of big data to be characterized and determine the information level, as charted in Figure6.Figure 6
Schematic diagram of information architecture.Figure6 describes a schematic diagram of information architecture. Through the information architecture, designers can sort out the hierarchical and parallel relationship between information and separate dimensions to provide the basis for presenting the corresponding dimension information on the page according to user actions in the later stage and achieving cognitive dimensionality reduction through the selective presentation of dimensions.


### 2.2.2. BC-Generated Big Data Information-Based Entity Relationship (ER)

It is necessary to determine the ER among the sorted structural information according to the internal relationship of the DS, which is the basis of big data-oriented IV. The data chart, including histogram, line chart, and sector chart, is the basic REPresentation (REP) of ER, in which each data entity contains a certain information DIM [26]. The ER based on Cartesian coordinate system (CCS) is the most widely used at present; in particular, CCS is the general name of right angle and oblique angle coordinate system, in which an x-axis and a y-axis intersecting at the origin form an xy plane, also known as the Cartesian plane. The most basic CCS-based ER includes polyline, histograms, scatter, and area diagrams. The rendering effect of the four data charts is drawn in Figure 7.Figure 7
Example of CCS-based ER. (a) Polyline diagram. (b) Histogram. (c) Scatter diagram. (d) Area diagram.
(a)(b)(c)(d)As presented in Figure7, the line chart focuses on the trend that the dimension of y-axis data changes with the dimension of the x-axis. The histogram emphasizes the dimensional change of the y-axis in a certain x-axis dimension interval, and a scatter chart can reflect the distribution law of data. The area chart emphasizes the changing trend of the total value. Based on the above basic graphic relationship, various graphic relationship layouts can be evolved according to the relationship between big data information dimension and representation dimensions, such as the arc diagram and matrix diagram demonstrated in Figure 8. Of these, the arc diagram can reflect the pairwise correlation between nodes at the same level, whereas the matrix graph can express the corresponding relationship between node rows and columns.Figure 8
Expanded ER. (a) Arc diagram. (b) Matrix diagram.
(a)(b)


### 2.2.3. Visual Coding Design

Next, it is necessary to carry out a visual coding design for the BC-generated big data information and its graphic ER. It mainly codes the semantic integration and visual harmony in the low-level sense to form the dimensional mapping [27]. The data DIMs are divided into quantitative information DIM and qualitative information DIM, in which quantitative information DIM refers to the attributes of information that can be quantified. In contrast, the qualitative information DIM represents the classification attribute of information. Therefore, this paper will design visual coding from quantitative and qualitative DIM.(1) Quantitative Coding DIM. Quantitative coding DIM is mainly determined through IV to obtain the magnitude sequence distribution information in a DIM quickly; generally, quantitative coding DIM enables users to quickly understand the location, size, width, length, and angle of elements. Common quantitative coding DIMs are presented in Figure 9.Figure 9
Quantitative coding DIM.Each node’s plan position in the DIM can reflect its spatial relationship. That is, users can judge its value and size by observing the relative position of a node in the coordinate axis plane compared with other nodes. The similarity between nodes can also be determined by the distribution of each node in the plane and grouped to form the visual effect of “cluster.” Color contrast refers to the color difference between the REP color of the node and the background (BG) color, as signified in Figure10.Figure 10
Color contrast.Figure10 proves that the higher the contrast between the REP color and the BG color, the stronger the visual saliency (VS) is, and the more likely it will be noticed by the user; this feature can be used to distinguish quantitative values of DIMs. The REP DIMs of line length and width, angle and slope, and size and volume are revealed in Figure 11.Figure 11
Comparison of different length and width lines. (a) Length/width of line. (b) Angle/slope. (c) Size. (d) Volume.
(a)(b)(c)(d)The visual effects of lines will differ according to their length and width. People use longer or wider lines to represent large values, but the effective perception level of line width is less than its length. The angle and slope in REP DIM must be combined with the plane position or node color for comprehensive REP. The quantitative comparison will be more difficult when the relative positional angle is greater than 90°. The quantitative comparison will be more difficult, so acute angles are more suitable for quantitative comparison. Finally, common quantitative REP DIMs also include size and volume. Still, humans’ ability to recognize size hierarchy is limited, so size and volume are generally only used for relative comparison between nodes.(2) Qualitative Coding DIM. The qualitative coding DIM mainly uses different hues and graphic shapes to represent the attribute categories in the same DIM to distinguish different attribute categories quickly. The qualitative REP DIM of hue is illustrated in Figure 12.Figure 12
Qualitative REP DIM of hue. (a) Two hue REP methods. (b) Four hue REP methods.
(a)(b)Hues represent different attribute categories, and the more colors there are, the better the REP effect is. Figure12 manifests the visual effect of distinguishing attributes with two and four hues. Obviously, the recognizability of two-hue attributes is significantly better than that of the four-hue attribute. Therefore, in the design of IV, the number of hue types should be minimized within a reasonable range so that people can quickly obtain the required information. Different graphic shapes can also distinguish different attribute categories. Graphics (shapes) are divided into semantic featureless geometric graphics and featured semantic graphics, as depicted in Figure 13.Figure 13
Qualitative REP DIM of shapes. (a) Semantic featureless geometric graphics. (b) Semantic featured graphics.
(a)(b)Semantic featureless shapes can refer to any attribute and be marked accordingly; it contains a relatively simple structure with a more concise and comfortable VI. By comparison, given the semantic featured shapes, people can quickly obtain the corresponding information through observation without labeling; however, such shapes usually contain many nodes, resulting in a more chaotic and complex VI, which is not conducive to information access. Therefore, the two shape-based qualitative REP DIMs have their own advantages and disadvantages, which must be selected according to the actual needs during IV design.(3) VI Component Design. VI component design mainly expresses the REP semantics in a specific format. Users can gradually approach their target object by interacting with the VI components. VI components include the hyperlink, radio box, range slider, drop-down box, and the input box, as drawn in Figure 14.Figure 14
Examples of VI components. (a) Hyperlinks, radio boxes, and range sliders. (b) Drop-down boxes and input boxes.
(a)(b)As shown in Figure14, the hyperlink is a jump mechanism, in which users can be connected to the corresponding interface through a click on the hyperlink; radio boxes and drop-down boxes are usually single choices, which are used to determine the attribute of a specific DIM; the range of DIMs can be directly selected by dragging the slider, but it cannot be accurately positioned; at this time, the user can directly enter the queried information terms in the input box for accurate positioning.


## 2.2.1. Design Scheme of IV

IV is mainly the process of representing the data in the information space in graphics and charts and communicating it to users. Therefore, the IV design is the key link [25]. The specific design process is outlined in Figure 5.Figure 5
IV design process.BC-generated big data are usually high-dimensional, pluralistic, and fragmented. Before IV design, it is necessary to sort out the hierarchical and parallel relationship of information, determine the dimension of information, layer the information dimension, and classify the information elements. Reasonable and effective information architecture is the basis of IV design. In the information architecture stage, it is necessary to sort out the information and architecture of big data to be characterized and determine the information level, as charted in Figure6.Figure 6
Schematic diagram of information architecture.Figure6 describes a schematic diagram of information architecture. Through the information architecture, designers can sort out the hierarchical and parallel relationship between information and separate dimensions to provide the basis for presenting the corresponding dimension information on the page according to user actions in the later stage and achieving cognitive dimensionality reduction through the selective presentation of dimensions.


## 2.2.2. BC-Generated Big Data Information-Based Entity Relationship (ER)

It is necessary to determine the ER among the sorted structural information according to the internal relationship of the DS, which is the basis of big data-oriented IV. The data chart, including histogram, line chart, and sector chart, is the basic REPresentation (REP) of ER, in which each data entity contains a certain information DIM [26]. The ER based on Cartesian coordinate system (CCS) is the most widely used at present; in particular, CCS is the general name of right angle and oblique angle coordinate system, in which an x-axis and a y-axis intersecting at the origin form an xy plane, also known as the Cartesian plane. The most basic CCS-based ER includes polyline, histograms, scatter, and area diagrams. The rendering effect of the four data charts is drawn in Figure 7.Figure 7
Example of CCS-based ER. (a) Polyline diagram. (b) Histogram. (c) Scatter diagram. (d) Area diagram.
(a)(b)(c)(d)As presented in Figure7, the line chart focuses on the trend that the dimension of y-axis data changes with the dimension of the x-axis. The histogram emphasizes the dimensional change of the y-axis in a certain x-axis dimension interval, and a scatter chart can reflect the distribution law of data. The area chart emphasizes the changing trend of the total value. Based on the above basic graphic relationship, various graphic relationship layouts can be evolved according to the relationship between big data information dimension and representation dimensions, such as the arc diagram and matrix diagram demonstrated in Figure 8. Of these, the arc diagram can reflect the pairwise correlation between nodes at the same level, whereas the matrix graph can express the corresponding relationship between node rows and columns.Figure 8
Expanded ER. (a) Arc diagram. (b) Matrix diagram.
(a)(b)


## 2.2.3. Visual Coding Design

Next, it is necessary to carry out a visual coding design for the BC-generated big data information and its graphic ER. It mainly codes the semantic integration and visual harmony in the low-level sense to form the dimensional mapping [27]. The data DIMs are divided into quantitative information DIM and qualitative information DIM, in which quantitative information DIM refers to the attributes of information that can be quantified. In contrast, the qualitative information DIM represents the classification attribute of information. Therefore, this paper will design visual coding from quantitative and qualitative DIM.(1) Quantitative Coding DIM. Quantitative coding DIM is mainly determined through IV to obtain the magnitude sequence distribution information in a DIM quickly; generally, quantitative coding DIM enables users to quickly understand the location, size, width, length, and angle of elements. Common quantitative coding DIMs are presented in Figure 9.Figure 9
Quantitative coding DIM.Each node’s plan position in the DIM can reflect its spatial relationship. That is, users can judge its value and size by observing the relative position of a node in the coordinate axis plane compared with other nodes. The similarity between nodes can also be determined by the distribution of each node in the plane and grouped to form the visual effect of “cluster.” Color contrast refers to the color difference between the REP color of the node and the background (BG) color, as signified in Figure10.Figure 10
Color contrast.Figure10 proves that the higher the contrast between the REP color and the BG color, the stronger the visual saliency (VS) is, and the more likely it will be noticed by the user; this feature can be used to distinguish quantitative values of DIMs. The REP DIMs of line length and width, angle and slope, and size and volume are revealed in Figure 11.Figure 11
Comparison of different length and width lines. (a) Length/width of line. (b) Angle/slope. (c) Size. (d) Volume.
(a)(b)(c)(d)The visual effects of lines will differ according to their length and width. People use longer or wider lines to represent large values, but the effective perception level of line width is less than its length. The angle and slope in REP DIM must be combined with the plane position or node color for comprehensive REP. The quantitative comparison will be more difficult when the relative positional angle is greater than 90°. The quantitative comparison will be more difficult, so acute angles are more suitable for quantitative comparison. Finally, common quantitative REP DIMs also include size and volume. Still, humans’ ability to recognize size hierarchy is limited, so size and volume are generally only used for relative comparison between nodes.(2) Qualitative Coding DIM. The qualitative coding DIM mainly uses different hues and graphic shapes to represent the attribute categories in the same DIM to distinguish different attribute categories quickly. The qualitative REP DIM of hue is illustrated in Figure 12.Figure 12
Qualitative REP DIM of hue. (a) Two hue REP methods. (b) Four hue REP methods.
(a)(b)Hues represent different attribute categories, and the more colors there are, the better the REP effect is. Figure12 manifests the visual effect of distinguishing attributes with two and four hues. Obviously, the recognizability of two-hue attributes is significantly better than that of the four-hue attribute. Therefore, in the design of IV, the number of hue types should be minimized within a reasonable range so that people can quickly obtain the required information. Different graphic shapes can also distinguish different attribute categories. Graphics (shapes) are divided into semantic featureless geometric graphics and featured semantic graphics, as depicted in Figure 13.Figure 13
Qualitative REP DIM of shapes. (a) Semantic featureless geometric graphics. (b) Semantic featured graphics.
(a)(b)Semantic featureless shapes can refer to any attribute and be marked accordingly; it contains a relatively simple structure with a more concise and comfortable VI. By comparison, given the semantic featured shapes, people can quickly obtain the corresponding information through observation without labeling; however, such shapes usually contain many nodes, resulting in a more chaotic and complex VI, which is not conducive to information access. Therefore, the two shape-based qualitative REP DIMs have their own advantages and disadvantages, which must be selected according to the actual needs during IV design.(3) VI Component Design. VI component design mainly expresses the REP semantics in a specific format. Users can gradually approach their target object by interacting with the VI components. VI components include the hyperlink, radio box, range slider, drop-down box, and the input box, as drawn in Figure 14.Figure 14
Examples of VI components. (a) Hyperlinks, radio boxes, and range sliders. (b) Drop-down boxes and input boxes.
(a)(b)As shown in Figure14, the hyperlink is a jump mechanism, in which users can be connected to the corresponding interface through a click on the hyperlink; radio boxes and drop-down boxes are usually single choices, which are used to determine the attribute of a specific DIM; the range of DIMs can be directly selected by dragging the slider, but it cannot be accurately positioned; at this time, the user can directly enter the queried information terms in the input box for accurate positioning.


## 2.3. BE-Based Big Data-Oriented ISP

The traditional BC data ISP interface generally has a complex information layout and is difficult to use, or the interface is too concise, lacks corresponding guide icons, and is less user-friendly [28]. Under such VI, learning cost is amplified, thus producing negative UE, which is not conducive to product promotion. To that end, the BC big data-oriented IV design guarantees a more convenient and quick information retrieval (IR) for users. Therefore, the user-product interaction should be fully considered in the IV design process. The VI should be concise, understandable, and smooth, thereby reducing learning costs and improving users’ satisfaction. Finally, this paper proposes a BE-based visual BC data-oriented ISP. Based on the IV design theory, the ISP is designed by fully considering the UB psychology.


### 2.3.1. ISP Design Strategy

The design of the ISP mainly considers the BE and emotional experience (EE), as reflected in Figure15.Figure 15
The design strategy of visual ISP.Usability awareness means that every icon, format, and language expression used in the ISP VI should be designed in a way acceptable to most people; in other words, the designs can be perceived and used correctly by users, which is the basis of ISP design. Thereupon, it is also necessary to adopt appropriate methods to guide users to manipulate or approach the target object; it can be realized by layering the information from simple to deep or providing novice guidance to new users. At the same time, the ISP needs to give corresponding feedback timely to remind the user to confirm their OP while emphasizing the importance of specific content through feedback methods, such as sound or touch. The timeliness feedback is an important system performance indicator for users.Further, users’ psychological state and emotional state in product use can be collectively called the EE. Based on BE design, users’ deep emotion and consciousness should be fully considered to help to design a more humanized and interesting ISP, thereby further improving UX. Firstly, the icons and information in the VI should be accompanied by detailed prompts to help users get started quickly; these prompts and descriptions can be a pop-up window, floating layers, toast, and other modes. Besides, the unimportant information in the VI should be removed, whereas the more important information should be highlighted by font size or color contrast, namely, information noise reduction (NR); in this way, the VI will become more concise and clearer, with a better visual effect.


### 2.3.2. Design of Visual ISP

Based on the above strategies, the visual ISP will be designed. The ISP consists of four modules: home page, IV tool, communication community, and personal center. The platform architecture is detailed in Figure16.Figure 16
Visual ISP architecture.Concretely, the home page of the visual ISP mainly focuses on information query, navigation, and information display. Users can select the query information by entering keywords or clicking the guide component in the corresponding query area. Relevant tools are displayed in the VI visualized navigation bars. By clicking the corresponding REP graphics, BC-generated data information can be visualized in different forms, facilitating user visual perception (VP). Apparently, a sound and user-friendly interaction design (ID) can attract more browsing and exploration users. The community mainly provides an ISP for users to exchange and share relevant experiences. Users can view, manage, and share account information, login information, and favorites in the personal center.


## 2.3.1. ISP Design Strategy

The design of the ISP mainly considers the BE and emotional experience (EE), as reflected in Figure15.Figure 15
The design strategy of visual ISP.Usability awareness means that every icon, format, and language expression used in the ISP VI should be designed in a way acceptable to most people; in other words, the designs can be perceived and used correctly by users, which is the basis of ISP design. Thereupon, it is also necessary to adopt appropriate methods to guide users to manipulate or approach the target object; it can be realized by layering the information from simple to deep or providing novice guidance to new users. At the same time, the ISP needs to give corresponding feedback timely to remind the user to confirm their OP while emphasizing the importance of specific content through feedback methods, such as sound or touch. The timeliness feedback is an important system performance indicator for users.Further, users’ psychological state and emotional state in product use can be collectively called the EE. Based on BE design, users’ deep emotion and consciousness should be fully considered to help to design a more humanized and interesting ISP, thereby further improving UX. Firstly, the icons and information in the VI should be accompanied by detailed prompts to help users get started quickly; these prompts and descriptions can be a pop-up window, floating layers, toast, and other modes. Besides, the unimportant information in the VI should be removed, whereas the more important information should be highlighted by font size or color contrast, namely, information noise reduction (NR); in this way, the VI will become more concise and clearer, with a better visual effect.


## 2.3.2. Design of Visual ISP

Based on the above strategies, the visual ISP will be designed. The ISP consists of four modules: home page, IV tool, communication community, and personal center. The platform architecture is detailed in Figure16.Figure 16
Visual ISP architecture.Concretely, the home page of the visual ISP mainly focuses on information query, navigation, and information display. Users can select the query information by entering keywords or clicking the guide component in the corresponding query area. Relevant tools are displayed in the VI visualized navigation bars. By clicking the corresponding REP graphics, BC-generated data information can be visualized in different forms, facilitating user visual perception (VP). Apparently, a sound and user-friendly interaction design (ID) can attract more browsing and exploration users. The community mainly provides an ISP for users to exchange and share relevant experiences. Users can view, manage, and share account information, login information, and favorites in the personal center.


## 2.4. System Performance Test Method

### 2.4.1. System Operation Environment and Configuration

The software environment, hardware environment, network environment, and performance test tools of the system are listed in Table1.Table 1
System operation environment and configuration table.
Operating environment typeProjectConfigurationSoftware environmentOperation system (OS)Linux OSWeb application serverTomcat6DatabaseMicrosoft SQL Server 2008BrowserGoogle ChromeHardware environmentHard disk capacity50 GMemory4 GCentral processing unit (CPU)Intel XeonEnvironmental maintenancePerformance test toolWeb Application Stress Tool (WAST)


### 2.4.2. Performance Test Index

(1) Event Handling Success Rate (SR). The SR of the system handling events is calculated by the following equation:
(1)SR=PsPs+Pf,where Ps is the total number of successfully handled events by the system and Pf is the total number of times the unsuccessfully handled events.(2) Throughput. Throughput (denoted by transaction per second (TPS)) refers to the unit-time user request (USRQ) processed by the system. TPS is one of the key indexes in network maintenance and fault identification (FI). In particular, the throughput index mainly reflects the ability of the server to withstand pressure and its load capacity. The calculation of TPS reads the following:
(2)TPS=CNART,where CN is the concurrent number and ART is the average system response time (RT).(3) CPU Occupancy Rate (OR). CPU OR refers to the percentage of CPU resources occupied by real-time running programs in the machine, indicating the machine’s program concurrency ability at a specific point in time (PIT). The CPU OR during system OP is calculated by the following equation:
(3)OR=TcTf=CPI×ICTf,where IC is the number of instructions used during program execution, Tf is the clock frequency, Tc is the number of clock cycles, and CPI is the average number of clock cycles required to execute each instruction. CPI calculation is displayed in the following equation:
(4)CPI=∑i=1nCPIi×Pi=∑i=1nCPIi×ICiIC,where Pi is the frequency of use of class i instructions, CPIi is the clock cycles required to execute the class i instruction, n is the number of classes of all instructions, and ICi is the number of the class i instructions.This section will use the WAST to test the system’s performance. During the test, the concurrent user is 100, and the server is operated simultaneously. The system CPU OR and memory utilization rate can be controlled within 50%, the event handling SR is higher than 90%, and the average RT is less than 0.5 s, which means that the system meets the standard and can be popularized.


## 2.4.1. System Operation Environment and Configuration

The software environment, hardware environment, network environment, and performance test tools of the system are listed in Table1.Table 1
System operation environment and configuration table.
Operating environment typeProjectConfigurationSoftware environmentOperation system (OS)Linux OSWeb application serverTomcat6DatabaseMicrosoft SQL Server 2008BrowserGoogle ChromeHardware environmentHard disk capacity50 GMemory4 GCentral processing unit (CPU)Intel XeonEnvironmental maintenancePerformance test toolWeb Application Stress Tool (WAST)


## 2.4.2. Performance Test Index

(1) Event Handling Success Rate (SR). The SR of the system handling events is calculated by the following equation:
(1)SR=PsPs+Pf,where Ps is the total number of successfully handled events by the system and Pf is the total number of times the unsuccessfully handled events.(2) Throughput. Throughput (denoted by transaction per second (TPS)) refers to the unit-time user request (USRQ) processed by the system. TPS is one of the key indexes in network maintenance and fault identification (FI). In particular, the throughput index mainly reflects the ability of the server to withstand pressure and its load capacity. The calculation of TPS reads the following:
(2)TPS=CNART,where CN is the concurrent number and ART is the average system response time (RT).(3) CPU Occupancy Rate (OR). CPU OR refers to the percentage of CPU resources occupied by real-time running programs in the machine, indicating the machine’s program concurrency ability at a specific point in time (PIT). The CPU OR during system OP is calculated by the following equation:
(3)OR=TcTf=CPI×ICTf,where IC is the number of instructions used during program execution, Tf is the clock frequency, Tc is the number of clock cycles, and CPI is the average number of clock cycles required to execute each instruction. CPI calculation is displayed in the following equation:
(4)CPI=∑i=1nCPIi×Pi=∑i=1nCPIi×ICiIC,where Pi is the frequency of use of class i instructions, CPIi is the clock cycles required to execute the class i instruction, n is the number of classes of all instructions, and ICi is the number of the class i instructions.This section will use the WAST to test the system’s performance. During the test, the concurrent user is 100, and the server is operated simultaneously. The system CPU OR and memory utilization rate can be controlled within 50%, the event handling SR is higher than 90%, and the average RT is less than 0.5 s, which means that the system meets the standard and can be popularized.


## 3. Design Effect Display and Test Results

### 3.1. Design Effect Display of Big Data Visual ISP

The design effect of the proposed BE-based BC big data-oriented visual ISP is presented in Figure17.Figure 17
Design effect display of blockchain big data visual ISP. (a) Interface effect of traditional BC platform. (b) Design sketch of the homepage of the proposed ISP. (c) Design sketch of the personal center of the proposed ISP platform.
(a)(b)(c)Figure17 illuminates that the traditional BC platform interface adopts black background and white or gray font color, and the overall color matching is more or less repressive. Moreover, some characters’ sizes and the color contrast between them and the background are inconsistent and difficult to identify. For example, the navigation bar’s font size and color design, which is more important for guiding users, is unreasonable, which virtually reduces the user’s sense of experience. By contrast, the proposed BE-based BC big data-oriented visual ISP has the basic functions of information query, module jump, classified navigation, and account information management. Besides, the overall interface of the proposed ISP is tidy and straightforward, the information layout is reasonable, and the presentation method is more intuitive so that users can quickly find the operation area and the required information after entering the page. The proposed ISP interface adopts the overall theme tone of light color, improving visual comfort. It uses color contrast to distinguish the operation area and background, which has a good visual effect. To sum up, the proposed BE-based BC big data-oriented visual ISP fully considers the user’s BE in the design process. The overall presentation effect of the interface is comfortable, concise, and easy to operate. It is suitable for the IV of BC-generated big data.


### 3.2. Performance Test Results

Subsequently, this section tests the performance of the proposed system on 100 concurrent users. Figure18 plots the specific results.Figure 18
System performance test results. (a) Test results of system service host’s TPS and event handling SR. (b) Test results of system service-end average RT and terminal-side average RT. (c) Test results of CPU OR and memory OR during system OP.
(a)(b)(c)As revealed in Figure18(a), the service processing host throughput of the system’s four functional modules is 101.56 times/s, 100.29 times/s, 102.28 times/s, and 101.36 times/s, respectively, all more than 100 times/s. The event handling SR is 99.8%, 99.6%, 99.7%, and 99.5%, respectively, all more than 99%, far more than 90% of the standard, and the treatment SR is high. As suggested in Figure 18(b), the average RT on the terminal side of the four functional modules is 0.29 s, 0.35 s, 0.38 s, and 0.34 s, respectively, all maintained below 0.4 s. The average RT of business end processing is 0.26 s, 0.21 s, 0.29 s, and 0.24 s, respectively, which is less than 0.3 s and lower than the standard 0.5 s. As implied in Figure 18(c), during the system operation, the CPU OR of the four functional modules is 17%, 29%, 21%, and 19%, respectively, all below 30%. The memory OR is 15%, 27%, 11%, and 22%, respectively, below 30%, which is far lower than the standard 50%.To sum up, the proposed BC-generated big data-oriented visual ISP has excellent throughput and event processing capacity when processing business. The average response speed and CPU and memory occupancy are lower than the standard value, and the performance meets the standard. It is suitable for the IV of BC big data.


## 3.1. Design Effect Display of Big Data Visual ISP

The design effect of the proposed BE-based BC big data-oriented visual ISP is presented in Figure17.Figure 17
Design effect display of blockchain big data visual ISP. (a) Interface effect of traditional BC platform. (b) Design sketch of the homepage of the proposed ISP. (c) Design sketch of the personal center of the proposed ISP platform.
(a)(b)(c)Figure17 illuminates that the traditional BC platform interface adopts black background and white or gray font color, and the overall color matching is more or less repressive. Moreover, some characters’ sizes and the color contrast between them and the background are inconsistent and difficult to identify. For example, the navigation bar’s font size and color design, which is more important for guiding users, is unreasonable, which virtually reduces the user’s sense of experience. By contrast, the proposed BE-based BC big data-oriented visual ISP has the basic functions of information query, module jump, classified navigation, and account information management. Besides, the overall interface of the proposed ISP is tidy and straightforward, the information layout is reasonable, and the presentation method is more intuitive so that users can quickly find the operation area and the required information after entering the page. The proposed ISP interface adopts the overall theme tone of light color, improving visual comfort. It uses color contrast to distinguish the operation area and background, which has a good visual effect. To sum up, the proposed BE-based BC big data-oriented visual ISP fully considers the user’s BE in the design process. The overall presentation effect of the interface is comfortable, concise, and easy to operate. It is suitable for the IV of BC-generated big data.


## 3.2. Performance Test Results

Subsequently, this section tests the performance of the proposed system on 100 concurrent users. Figure18 plots the specific results.Figure 18
System performance test results. (a) Test results of system service host’s TPS and event handling SR. (b) Test results of system service-end average RT and terminal-side average RT. (c) Test results of CPU OR and memory OR during system OP.
(a)(b)(c)As revealed in Figure18(a), the service processing host throughput of the system’s four functional modules is 101.56 times/s, 100.29 times/s, 102.28 times/s, and 101.36 times/s, respectively, all more than 100 times/s. The event handling SR is 99.8%, 99.6%, 99.7%, and 99.5%, respectively, all more than 99%, far more than 90% of the standard, and the treatment SR is high. As suggested in Figure 18(b), the average RT on the terminal side of the four functional modules is 0.29 s, 0.35 s, 0.38 s, and 0.34 s, respectively, all maintained below 0.4 s. The average RT of business end processing is 0.26 s, 0.21 s, 0.29 s, and 0.24 s, respectively, which is less than 0.3 s and lower than the standard 0.5 s. As implied in Figure 18(c), during the system operation, the CPU OR of the four functional modules is 17%, 29%, 21%, and 19%, respectively, all below 30%. The memory OR is 15%, 27%, 11%, and 22%, respectively, below 30%, which is far lower than the standard 50%.To sum up, the proposed BC-generated big data-oriented visual ISP has excellent throughput and event processing capacity when processing business. The average response speed and CPU and memory occupancy are lower than the standard value, and the performance meets the standard. It is suitable for the IV of BC big data.


## 4. Conclusions

With the advent of the big data era, the increasing amount of BC-generated data makes it more difficult for people to obtain information effectively. Although this can be effectively solved by IV technology, the existing research pays more attention to the practicability of BC technology and IV while seldom considering the UE level, resulting in the poor visual effect of the BC platform interface. Given the above problems, this paper first expounds on BC technology, the related theory of IV, and the IV design method of BC-generated big data. Secondly, this paper fully considers the problem of user experience and creatively proposes a BE-based big data visual ISP. Finally, the system performance is tested. The results corroborate that compared with the traditional BC platform interface, the overall color matching and layout of the proposed BC big data-oriented visual ISP are more reasonable, concise, and intuitive, which can significantly reduce the learning cost of users and improve their sense of experience. Meanwhile, the host throughput, processing SR, average ST, CPU OR, and memory OR of each system module meet the standards, proving that the proposed system can realize the basic operation and ensure good operation performance. Therefore, it is suitable for the IV of BC-generated big data. The disadvantage of this paper is that it only tests the basic operation performance of the platform but does not explore the user’s sense of experience. In the future, it is necessary to conduct a corresponding trial operation of the platform and collect user feedback to improve the platform continuously. This research is aimed at providing important technical support for the IV of BC big data and improving the efficiency of users’ acquisition and analysis of data information.



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*Source: 1380387-2022-02-22.xml*