# User Knowledge, Data Modelling, and Visualization: Handling through the Fuzzy Logic-Based Approach

Authors: Xiaoqun Liao; Shah Nazir; Yangbin Zhou; Muhammad Shafiq; Xuelin Qi
Journal: Complexity
 (2021)

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## Abstract

In modern day technology, the level of knowledge is increasing day by day. This increase is in terms of volume, velocity, and variety. Understanding of such knowledge is a dire need of an individual to extract meaningful insight from it. With the advancement in computer and image-based technologies, visualization becomes one of the most significant platforms to extract, interpret, and communicate information. In data modelling, visualization is the process of extracting knowledge to reveal the detail data structure and process of data. The proposed study aim is to know about the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. The experimental setup is validated through the data user modelling dataset available in the UCI web repository. The results show that the model is effective and efficient in situations where uncertainty and complexity arise.


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## Body

## 1. Introduction

With the development of modern day technologies, user knowledge is increasing day by day. Users are trying to capture the essential information from the domain knowledge which is large in volume. Capturing the important information is toward the success for smooth functionality of user knowledge. Data exist in different forms such as structured and unstructured. The big data are the data whose scale, diversity, and complexity need new algorithms, structures, techniques, and analytics for the managements and visualizations and to pull out hidden information [1]. According to IDC [2], in late 2011, about 1.8 ZB of data was created as of that year. Worldwide, electronic data of approximately 1.2 ZB (1021) are generated per year by different sources [3]. By 2020, 40 ZB data is expected [4].Visual contexts are made from the visualization of user knowledge and data to translate the information through graphs or maps for making data easier for humans. In visualization, patterns are identified from a large bulk of data and are plotted through information visualization, graphics, and statistical graphics. Data visualization is one of the processes of data science in which data are collected, modelled, and processed, so the visualization should be in order to draw conclusions from the data. Visualization of data has importance in every field of life. It can be used in teaching, healthcare, artificial intelligence, big data, and many others to share the extracted information with the stockholders.Knowledge, data, and information are widely used in visualization in interrelated perspectives. Visualization indicates diverse stages of understanding and abstraction. Visualization aim is to gain meaningful insights from the data [5]. Advancements in visualization bring more venture reporting.Through data visualization, one can interact with data and go for analysis. Several benefits can be gained from visualization of data, such as efficient way to communicate, concrete, and abstract the message and innovative approaches for scientific and engineering purposes. Visualization of information is “the graphical presentation of abstract data” which “attempts to reduce the time and the mental effort users need to analyze large datasets.” [6]The contribution of the proposed research is to know about the user knowledge, data modelling, and visualization by handling with applications of the fuzzy logic-based approach. The experimental setup of the proposed research is validated through the data user modelling dataset available in the UCI web repository [7]. The following are the key concepts of the proposed research:(i)
To know about the user knowledge, data modelling, and visualization(ii)
To use the fuzzy logic-based approach for handling user knowledge, data modelling, and visualization(iii)
To visualize the dataset in order to get meaningful insights(iv)
To validate the work by using the “data user modelling dataset”The organization of the paper is as follows: Section2 represents the related work regarding user knowledge, data modelling, and visualization with different literature studies. Section 3 shows the research method and modelling of the proposed study with the detail of visualization of the dataset. Section 4 discusses the results and discussion section of the paper. The paper is concluded in Section 5.


## 2. Related Work

Different approaches, tools, and techniques are used in practice for user knowledge, data modelling, and visualization. Sahu and Dwivedi [8] proposed an approach of knowledge transfer by the domain-independent use latent factor for cross-domain recommender systems. The method used tr-factorization. The authors in [9] studied the usage of media effects in online commentaries on creating knowledge. The user groups were divided into three categories: passive participants, active participants, and bystander. Their experimental results revealed that the active participants largely tend to use tablet PC and smartphones for the creation of knowledge in the online space. Flowers and Meyer [10] focused on the user knowledge value for entrepreneurs and tackled the gap in the literature associated to the activities of entrepreneurs and user knowledge in the digital services. The framework of Innovation Opportunity Space was proposed and applied on a UK-based mobile telephony supplier Giffgaff for the issues faced by the user knowledge application to digital services. Constant [11] extracted important insights from the crystal’s geometry and physical properties for the creation of new structuring according to the methodology of knowledge-visualization. Desimoni and Po [12] presented the analysis of the state-of-the-art tools for the visualization of linked data. Full list of 77 linked data visualization tool in the previous research and integrating new tools published recently online. Based on usability and their features, the visualization tools are compared and described.Huang et al. [13] explored the cognitive approach for following the user-centred process in visualization graphs. A graph-based visualization model was proposed which is a two-stage conceptualized assessment cycle. Silva et al. [14] proposed a solution of visual analytics based on the use of several coordinate views for the description of diverse aspects of ontology and the technique of degree of interest use for reduction of complexity in the visual representation of ontology. Yu and Shi [15] presented a mini survey consisting of the user-based taxonomy that converts the works of the state of the art in the field. Luo [16] investigated how cognitive style, task difficulty, and spatial ability affect choice and preference of the visualization format and how the visualization selected affects the confidence and decision accuracy. Gebremeskel and Biazen [17] designed a system which is able to analyze and handle large-scale data. The authors in [18] presented TrajAnalytics, open-source software, for modelling, transforming, and visualizing the urban trajectory data for the study of urban and transportation. The approach allows practitioners to understand the data of the population mobility and find out knowledge. A conceptual model for data is presented which incorporates the geostructure with trajectory data with the help of different access queries of data.Rojas and Villegas presented an approach of representation and scheme of investigative visualization for the decision tree in the knowledge discovery database process for data mining [19]. Macek and Atzmueller [20] presented a new concept of visualization for the user history interactions. Association rules are derived and visualized through heatmaps. The impact of the approach is demonstrated by real-world examples of data such as Twitter dump of 2009. Giunchiglia et al. [21] proposed the SemUI tool-based solution as the multitiered method consists of the (a) semantic layer which incorporates data through the notion of the entity of the real world and groups them based on their differences and similarities and (b) layer of visualization which concurrently shows several views based on entity properties. The authors in [22] presented an approach of visual analytics for the visual data mining and interactive machine learning. In the approach, techniques of multidimensional data visualization are applied for the facilitation of user interactions with machine learning and data mining process. Rafi [23] proposed a multidimensional interface for adopting the resource space model and presented its advantages in the property letting application. The authors in [24] presented a methodology for exploiting visual language CoDe based on the logic paradigm. CoDe gives the structure for organizing visualization by the CoDe model and represents the relationships between items of the information graphically. The authors in [25] proposed a model of visual analytic knowledge generation to tie different frameworks.


## 3. Research Methodology

The following sections briefly explain the methodology section of the proposed research.


### 3.1. Library-Based Search to Show the Status of the Existing Research

Different libraries were searched for identifying relevant information regarding the user knowledge, data modelling, and visualization. The purpose was to identify information such as the number of publications in the given year, publication topics, publication disciplines, publication title, and type of publications. For the search process, famous libraries such as IEEE, ScienceDirect, Springer, Tailor & Francis, Wiley online library, and MDPI were searched for showing the relevant information. This information was presented in the form of different figures. Figure1 represents the type of publications in the IEEE library.Figure 1
Publication type.Figure2 represents the conference locations where the conferences were held.Figure 2
Conference locations.Figure3 represents the topics of publications along with the total number of publications.Figure 3
Topics with the number of publications.Figure4 represents the publication years and the number in the ScienceDirect library.Figure 4
Years and the number of publications.Figure5 represents the article types with the publication number.Figure 5
Article types with the total number of publications.Figure6 represents the publication title with the total number of publications.Figure 6
Publication title with the total number of publications.Figure7 represents the article types along with the paper number in the Springer library.Figure 7
Article types along with the total number of papers.Figure8 represents the content types along with the total number of publications.Figure 8
Type of content with the publication number.Figure9 represents the publication topic with the number of papers.Figure 9
Publication topic with the number of papers.Figure10 represents the number of publications published in the given languages.Figure 10
Number of publications published in the given languages.Figure11 represents the discipline along with the number of publications in the Tailor & Francis library.Figure 11
Discipline along with the number of publications.Figure12 represents the discipline along with the number of publications in the Wiley online library.Figure 12
Discipline along with the number of publications in the Wiley online library.Figure13 represents the discipline of publication in the MDPI library.Figure 13
Discipline of publication.


### 3.2. Visualization of the Dataset

The purpose of user knowledge and visualization is to extract meaningful insights from the knowledge and present it in an organized form to be easily understood and analysed. The experimental setup of the proposed research is validated through the data user modelling dataset available in the UCI web repository [7]. Visualization of the class distribution of the dataset was done to plot each class which is very low, low, middle, and high. The purpose of this visualization is to show the dataset which is easily understandable to the reader as compared to study the actual dataset. Figure 14 represents the visualization of class distribution “high”.Figure 14
Visualization of the class distribution “high.”Figure15 represents the visualization of class distribution “very_low.”Figure 15
Visualization of class distribution “very_low.”Figure16 represents the visualization of class distribution “middle.”Figure 16
Visualization of class distribution “middle.”Figure17 represents the visualization of class distribution “low.”Figure 17
Visualization of class distribution “low.”


### 3.3. User Knowledge, Data Modelling, and Visualization through the Fuzzy Logic

Visualization is to give a meaningful structure to huge data and to extract the significant information from them. With the help of visualization, information can be mined for the extraction of meaningful insights for the analysis under consideration. The important terminologies used for visualization are shown in Figure18.Figure 18
Common terminologies used for visualization.The fuzzy logic (FL) is a tool used to solve a situation of vagueness and uncertainty. It was developed by A. Z. Lofti in 1965 [26, 27]. FL has several applications in different fields of real life such as in control system, washing machines, transmission system of cars, vacuum cleaner, and software system [28–32]. The fuzzy inference system used in the proposed research makes it simple to structure the facts and provide a way out for the vague information. For more details regarding the fuzzy concept, refer Zadeh [26].The concept of fuzzy logic was used for user knowledge, data modelling, and visualization of the proposed study. Initially, different inputs STG, SCG, STR, LPR, PEG were plotted with the membership functions (mf) low, medium, and high. The output UNS was plotted with the mf very low, low, medium, high, and very high. Figure19 shows the input, output, and mf plotting process of the proposed model.Figure 19
Input and output plotting process of the proposed model along with the mf.After plotting the inputs, output, and mf, rules were made to model the proposed system of user knowledge, data modelling, and visualization. Figure20 shows the rule editor of the proposed system.Figure 20
Rule editor of the proposed system.Different rules were designed from the mf. Some of these rules are given in the following:(1)
If (STG is Medium) and (SCG is Low) and (STR is Low) and (LPR is Low) and (PEG is Low) then (UNS is Very_low) (0.1)(2)
If (STG is Medium) and (SCG is Medium) and (STR is Low) and (LPR is Low) and (PEG is Low) then (UNS is Very_low) (0.2)(3)
If (STG is Medium) and (SCG is Medium) and (STR is Medium) and (LPR is High) and (PEG is High) then (UNS is Very_high) (0.8)(4)
If (STG is High) and (SCG is Low) and (STR is Low) and (LPR is High) and (PEG is High) then (UNS is High) (0.61)(5)
If (STG is Low) and (SCG is Low) and (STR is High) and (LPR is High) and (PEG is High) then (UNS is High) (0.7)(6)
If (STG is Medium) and (SCG is Medium) and (STR is Medium) and (LPR is High) and (PEG is High) then (UNS is High) (0.8)The obtained model from the designing of rules is shown in Figure21.Figure 21
Proposed model obtained from the plotting of the mf and designing of rules.


## 3.1. Library-Based Search to Show the Status of the Existing Research

Different libraries were searched for identifying relevant information regarding the user knowledge, data modelling, and visualization. The purpose was to identify information such as the number of publications in the given year, publication topics, publication disciplines, publication title, and type of publications. For the search process, famous libraries such as IEEE, ScienceDirect, Springer, Tailor & Francis, Wiley online library, and MDPI were searched for showing the relevant information. This information was presented in the form of different figures. Figure1 represents the type of publications in the IEEE library.Figure 1
Publication type.Figure2 represents the conference locations where the conferences were held.Figure 2
Conference locations.Figure3 represents the topics of publications along with the total number of publications.Figure 3
Topics with the number of publications.Figure4 represents the publication years and the number in the ScienceDirect library.Figure 4
Years and the number of publications.Figure5 represents the article types with the publication number.Figure 5
Article types with the total number of publications.Figure6 represents the publication title with the total number of publications.Figure 6
Publication title with the total number of publications.Figure7 represents the article types along with the paper number in the Springer library.Figure 7
Article types along with the total number of papers.Figure8 represents the content types along with the total number of publications.Figure 8
Type of content with the publication number.Figure9 represents the publication topic with the number of papers.Figure 9
Publication topic with the number of papers.Figure10 represents the number of publications published in the given languages.Figure 10
Number of publications published in the given languages.Figure11 represents the discipline along with the number of publications in the Tailor & Francis library.Figure 11
Discipline along with the number of publications.Figure12 represents the discipline along with the number of publications in the Wiley online library.Figure 12
Discipline along with the number of publications in the Wiley online library.Figure13 represents the discipline of publication in the MDPI library.Figure 13
Discipline of publication.


## 3.2. Visualization of the Dataset

The purpose of user knowledge and visualization is to extract meaningful insights from the knowledge and present it in an organized form to be easily understood and analysed. The experimental setup of the proposed research is validated through the data user modelling dataset available in the UCI web repository [7]. Visualization of the class distribution of the dataset was done to plot each class which is very low, low, middle, and high. The purpose of this visualization is to show the dataset which is easily understandable to the reader as compared to study the actual dataset. Figure 14 represents the visualization of class distribution “high”.Figure 14
Visualization of the class distribution “high.”Figure15 represents the visualization of class distribution “very_low.”Figure 15
Visualization of class distribution “very_low.”Figure16 represents the visualization of class distribution “middle.”Figure 16
Visualization of class distribution “middle.”Figure17 represents the visualization of class distribution “low.”Figure 17
Visualization of class distribution “low.”


## 3.3. User Knowledge, Data Modelling, and Visualization through the Fuzzy Logic

Visualization is to give a meaningful structure to huge data and to extract the significant information from them. With the help of visualization, information can be mined for the extraction of meaningful insights for the analysis under consideration. The important terminologies used for visualization are shown in Figure18.Figure 18
Common terminologies used for visualization.The fuzzy logic (FL) is a tool used to solve a situation of vagueness and uncertainty. It was developed by A. Z. Lofti in 1965 [26, 27]. FL has several applications in different fields of real life such as in control system, washing machines, transmission system of cars, vacuum cleaner, and software system [28–32]. The fuzzy inference system used in the proposed research makes it simple to structure the facts and provide a way out for the vague information. For more details regarding the fuzzy concept, refer Zadeh [26].The concept of fuzzy logic was used for user knowledge, data modelling, and visualization of the proposed study. Initially, different inputs STG, SCG, STR, LPR, PEG were plotted with the membership functions (mf) low, medium, and high. The output UNS was plotted with the mf very low, low, medium, high, and very high. Figure19 shows the input, output, and mf plotting process of the proposed model.Figure 19
Input and output plotting process of the proposed model along with the mf.After plotting the inputs, output, and mf, rules were made to model the proposed system of user knowledge, data modelling, and visualization. Figure20 shows the rule editor of the proposed system.Figure 20
Rule editor of the proposed system.Different rules were designed from the mf. Some of these rules are given in the following:(1)
If (STG is Medium) and (SCG is Low) and (STR is Low) and (LPR is Low) and (PEG is Low) then (UNS is Very_low) (0.1)(2)
If (STG is Medium) and (SCG is Medium) and (STR is Low) and (LPR is Low) and (PEG is Low) then (UNS is Very_low) (0.2)(3)
If (STG is Medium) and (SCG is Medium) and (STR is Medium) and (LPR is High) and (PEG is High) then (UNS is Very_high) (0.8)(4)
If (STG is High) and (SCG is Low) and (STR is Low) and (LPR is High) and (PEG is High) then (UNS is High) (0.61)(5)
If (STG is Low) and (SCG is Low) and (STR is High) and (LPR is High) and (PEG is High) then (UNS is High) (0.7)(6)
If (STG is Medium) and (SCG is Medium) and (STR is Medium) and (LPR is High) and (PEG is High) then (UNS is High) (0.8)The obtained model from the designing of rules is shown in Figure21.Figure 21
Proposed model obtained from the plotting of the mf and designing of rules.


## 4. Results and Discussion

The fuzzy inference system was designed for handling user knowledge, data modelling, and visualization. The purpose of this study was to plot and visualize the user knowledge and data modelling and to present the concept of visualization to present the data in a meaningful form for the reader. The model was designed from different inputs, mf, and output. Model description of the designed model is given in Table1.Table 1
Proposed model description.
showfis(a)Name = DUMV, Type, mamdani, Inputs/Outputs, NumInputMFs = [3 3 3 3 3], NumOutputMFs = 5, NumRules = 64, AndMethod = min, OrMethod = max, ImpMethod = min, AggMethod = max, DefuzzMethod = centroid, InLabels = STG, SCG, STR, LPR, PEG, OutLabels = UNS, InRange = [0 1], InMFLabels = Low, Medium, High, OutMFLabel Low, Medium, High, Very_low, Very_high, InMFTypes = trimfThe fuzzy system was obtained in order to pass the inputs. The following is the structure of the proposed system:>> fismat = readfisfismat =name: “fuzzy system”type: “mamdani”andMethod: “min”orMethod: “max”defuzzMethod: “centroid”impMethod: “min”aggMethod: “max”input: [1 × 5 struct]output: [1 × 1 struct]>>a = readfisa =name: “fuzzy system”type: “mamdani”andMethod: “min”orMethod: “max”defuzzMethod: “centroid”impMethod: “min”aggMethod: “max”input: [1 × 5 struct]output: [1 × 1 struct]After that, the input values of each attribute were passed in order to get the decision. The following is an example of the input format:>> out = evalfis ([0.08 0.08 0.1 0.1 0.1], fismat)out = 0.5000>> out = evalfis ([0.2 0.01 0.2 0.1 0.2], fismat)out = 0.5000>> out = evalfis ([0.7 0.8 0.7 0.9 0.6], fismat)out = 0.9044>> out = evalfis ([0.5 0.8 0.5 0.9 0.6], fismat)out = 0.5047>> out = evalfis ([0.5 0.8 0.5 0.6 0.6], fismat)out = 0.5000>> out = evalfis ([0.9 0.8 0.5 0.6 0.9], fismat)out = 0.9044


## 5. Conclusion

The data and information are the dire needs of modern day technology and real life. Both data and information are rapidly increasing with the passage of time. It becomes very difficult to understand all the things at the same time. The user needs meaningful extraction from the knowledge of data and information. Visualization plays an important role to extract meaningful information and insights from it. The proposed study is an endeavour toward the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. Fuzzy logic deals with uncertainty and vagueness when they arise in the data. Experimental setup of the proposed research is validated through the data user modelling dataset available in the UCI web repository. Model of the fuzzy inference system was designed based on the inputs, mf, output, and fuzzy rules. The results show that the model is effective and efficient in situations where uncertainty and complexity arise.



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*Source: 6629086-2021-02-26.xml*