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International Conference on Smart Computing and Electronic Enterprise (ICSCEE)

作者： Fahad, S. K. Ahammad Yahya, Abdulsamad Ebrahim Al Madinah Int Univ Fac Comp & Informat Technol Shah Alam Malaysia Northern Border Univ Fac Comp & Informat Technol Rafha Saudi Arabia

ISBN: (纸本)9781538648384

A tremendous amount of data comes with a vast amount of knowledge. Decent use of the persistent information can assist to overcome provocations and support to establish further sophisticated judgment. data visualization techniques are authenticated scientifically as thousand times reliable rather than textual representation. The premature data visualization system met some difficulties and there has some solution for handle this kind of big quantity of data. data science used two distinct languages Python and R to visualize big data undeviatingly. There also have a lot of tools in operating business. This paper is focused on the visualization technique of Python and R. R appears including the extraordinary visualization library alike ggplot2, leaflet, and lattice to defeat the provocation of the extensive volume. Python has several particular libraries for data visualization. Commonly they are Bokeh, Seaborn, Altair, ggplot and Pygal. Also, with most modern, secure and powerful zero coding GUI's accessories to describe big data visualization for genuine recognition with practical determination. Method and process of visual description of data are significant to recover specific knowledge from the large-scale dataset.

关键词： big data visualization Python visualization R visualization GUI visualization Zero coding visualization visualization Tools

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big data visualization Tools: A Survey The New Paradigms, Methodologies and Tools for Large data Sets visualization 6

Big Data Visualization Tools: A Survey <i>The New Paradigms<...

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6th International Conference on data Science, Technology and Applications (data)

作者： Caldarola, Enrico G. Rinaldi, Antonio M. Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy CNR Inst Ind Technol & Automat Bari Italy Univ Naples Federico II IKNOS Lab Intelligent & Knowledge Syst Via Toledo 402 I-80134 Naples Italy

ISBN: (纸本)9789897582554

In the era of big data, a great attention deserves the visualization of large data sets. Among the main phases of the data management's life cycle, i.e., storage, analytics and visualization, the last one is the most strategic since it is close to the human perspective. The huge mine of data becomes a gold mine only if tricky and wise analytics algorithms are executed over the data deluge and, at the same time, the analytic process results are visualized in an effective, efficient and why not impressive way. Not surprisingly, a plethora of tools and techniques have emerged in the last years for big data visualization, both as part of data Management Systems or as software or plugins specifically devoted to the data visualization. Starting from these considerations, this paper provides a survey of the most used and spread visualization tools and techniques for large data sets, eventually presenting a synoptic of the main functional and non-functional characteristics of the surveyed tools.

关键词： big data big data visualization Graph visualization Information visualization big data Analytics Visual Analytics

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Filter and Embedded Feature Selection Methods to Meet big data visualization Challenges

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Computers, Materials & Continua 2023年第1期74卷 817-839页

作者： Kamal A.ElDahshan AbdAllah A.AlHabshy Luay Thamer Mohammed Mathematics Department Faculty of ScienceAl-Azhar UniversityCairo11884Egypt

This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection *** reduce the volume of big data and minimize model training time(Tt)while maintaining data *** contributed to meeting the challenges of big data visualization using the embedded method based“Select from model(SFM)”method by using“Random forest Importance algorithm(RFI)”and comparing it with the filter method by using“Select percentile(SP)”method based chi square“Chi2”tool for selecting the most important features,which are then fed into a classification process using the logistic regression(LR)algorithm and the k-nearest neighbor(KNN)***,the classification accuracy(AC)performance of LRis also compared to theKNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are ***,the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the *** making several comparisons,the study suggests(SFMLR)using SFM based on RFI algorithm for feature selection,with LR algorithm for data *** proposal proved its efficacy by comparing its results with recent literature.

关键词： data Redaction features selection Select from model Select percentile big data visualization data visualization

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Blockchain-based green big data visualization: BGbV

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COMPLEX & INTELLIGENT SYSTEMS 2022年第5期8卷 3707-3718页

作者： Shahzad, Iqra Maqbool, Ayesha Rana, Tauseef Mirza, Alina Khan, Wazir Zada Kim, Sung Won Bin Zikria, Yousaf Din, Sadia Natl Univ Sci & Technol NUST Dept Comp Software Engn MCS Islamabad Pakistan Natl Univ Sci & Technol NUST Dept Comp Sci NBC Islamabad Pakistan Natl Univ Sci & Technol NUST Dept Elect Engn MCS Islamabad Pakistan Capital Univ Sci & Technol Dept Comp Sci Islamabad Pakistan Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea

The progression of Internet of Things (IoT) has resulted in generation of huge amount of data. Effective handling and analysis of such big volumes of data proposes a crucial challenge. Existing cloud-based frameworks of big data visualization are rising costs for servers, equipment, and energy consumption. There is a need for a green solution targeting lesser cost and energy consumption with tamper-proof record-keeping, storage, and interactive visualization with only demanded data. We have proposed a Blockchain-based Green big data visualization (BGbV) solution using Hyperledger Sawtooth for optimum utilization of organization resources. BGbV will support current distributed data visualization platforms and guarantee benefits like security and data availability with lesser storage costs. It helps reduce costs by utilizing small resources that are already available and consume less energy, making it an environmentally friendly solution.

关键词： IoT Decentralized visualization Blockchain big data visualization Hyperledger Sawtooth Secure distributed storage Green solution

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big data visualization using multimodal feedback in education

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 96卷

作者： Cui, Yong Song, Xiao Hu, Qinglei Li, Ying Shanthini, A. Vadivel, Thanjai Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing Peoples R China Beihang Univ BUAA Sch Cyber Sci & Technol Beijing Peoples R China Beihang Univ BUAA Sch Comp Sci & Engn Beijing Peoples R China SRM Univ Dept Comp Sci Chennai Tamil Nadu India Veltech Univ Chennai Tamil Nadu India

Currently, big data Analytics help educators better analyze students and monitor their progress and likelihood of success. The online learning experience applies to the experiences in the online computer-mediated digital system to collect information. An essential part of education training programs is challenging and effectively implemented between teachers and students in the online learning feedback system. Hence, a big data visualization assisted Multimodal Feedback Framework (BDVMFF) is proposed to boost students' confidence, self-consciousness, and motivation in the online learning platform. The BDVMFF offers the teacher a digital workflow to effectively exchange both the writing and the input to use multimodal feedback effectively. This system provides teachers and students with an effective and straightforward digital learning environment. The simulation results of BDVMFF show the highest performance ratio (97.9%), the efficiency ratio (96.1%), the grade analysis ratio (93.5%), the computation ratio (95.3%), and the lowest response time compared to existing methods.

关键词： big data visualization Multimodal feedback Online learning Student Grade Analysis Behaviour

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big data visualization of the quantification of influencing factors and key monitoring indicators in the refined oil products market based on fuzzy mathematics

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第4期40卷 6219-6229页

作者： Zhu, Yu Liu, Xiantao Sch Southwest Petr Univ Chengdu Sichuan Peoples R China

In this paper, an in-depth study on the quantification of influencing factors and big data visualization of key monitoring indicators in the refined oil products market is carried out through fuzzy mathematical methods, and a system for quantifying influencing factors and big data visualization of key monitoring indicators in the refined oil products market with the fuzzy mathematical background is designed and implemented. The system realizes the functions of flow visualization, attack visualization, target tracking visualization, etc., and optimizes the system from the perspectives of performance and visualization effect. It achieves the display and interaction of multi-dimensional data in space and time with multiple views, angles, and dimensions. data tagging and data correlation for key aspects of the product production process are realized through fuzzy mathematics and other means, and a quality traceability system for the manufacturing industry is realized on this basis, through which the data of some key stages of the product production process can be displayed retrospectively. The study proves that the business model of refined oil logistics platform based on value network can significantly improve the user's perceived value and benefit all parties within the value network, realizing the complementary advantages of refined oil production enterprises and logistics platform companies, improving the efficiency of enterprise's logistics and maximizing the profit of each subject within the value network to achieve profitability for all parties.

关键词： Fuzzy mathematics refined products market quantification of influences key monitoring indicators big data visualization

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UX Design of a big data visualization Application Supporting Gesture-Based Interaction with a Large Display

UX Design of a Big Data Visualization Application Supporting...

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19th International Conference on Human-Computer Interaction (HCI International)

作者： Ntoa, Stavroula Birliraki, Chryssi Drossis, Giannis Margetis, George Adami, Ilia Stephanidis, Constantine Fdn Res & Technol Hellas FORTH ICS Iraklion Greece Univ Crete Dept Comp Sci Iraklion Greece

ISBN: (纸本)9783319585215;9783319585208

The explosion of information has led to the proliferation of big data as an influential business and research domain. data center infrastructure management is a sector largely affected by big data, however the visualization of, and interaction with, big data in the context of a data center room is a challenging endeavor. This paper presents the iterative design and development of a 3D data Centre visualization application featuring gesture-based interaction with a high resolution large screen display. As result of the design iterations three distinct system versions were developed, evolving the supported functionality, the User Interface and the interaction methods. The paper presents the evolution of the system, the results of an expert-based evaluation which was carried out during the development life-cycle, as well as the challenges faced and lessons learned regarding the User Experience design of a big data application deployed in a large display, supporting gestural interaction.

关键词： big data big data visualization 3D data center data center infrastructure management Gestural interaction Natural interaction Large screen display User experience design UX

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Competitive advantage of data-driven analytical capabilities: the role of big data visualization and of organizational agility

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MANAGEMENT DECISION 2022年第4期60卷 953-975页

作者： Medeiros, Mauricius Munhoz de Macada, Antonio Carlos Gastaud Univ Fed Rio Grande do Sul Escola Adm Porto Alegre RS Brazil

Purpose In the digital age, the use of data and analytical capabilities to guide business decisions and operations plays a strategic role for organizations to gain competitive advantage (CA). However, the paths by which analytical capabilities convey their effect to CA are not yet fully known and few studies address the role of behavioral and cultural aspects of related of analytical capabilities. The purpose of this paper is to analyze how data-driven culture (DDC) and business analytics (BA) affect CA, considering the mediating effects of big data visualization (BDV) and organizational agility (OA). Design/methodology/approach A survey was conducted with 173 managers who are BDV and BA users in Brazilian organizations of various economic segments. The data were analyzed through structural equation modeling and mediation tests. Findings The evidence indicates that DDC and BDV are antecedents of BA. The following complementary mediations were discovered: BDV in the relationship between DDC and BA;BA in the relationship between DDC and CA;and OA in the relationship between BA and CA. It was also discovered that OA explains the transmission of most of the effect of BA to CA. Practical implications This study can help organizations to understand the importance of cultural and behavioral aspects related to the use of the analytical capabilities. Thereby, managers can establish policies and strategies to extract value from data and leverage business agility and competitiveness through use BDV and BA. Originality/value This study fills an important research gap by developing an original research model and discussing empirical evidence on how DDC and BA affect CA, considering the mediating effects of BDV and OA.

关键词： big data visualization data-driven culture Business analytics Organizational agility Competitive advantage

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Centrality Clustering-Based Sampling for big data visualization

Centrality Clustering-Based Sampling for Big Data Visualizat...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tam Thanh Nguyen Song, Insu James Cook Univ Australia Sch Business IT Singapore Campus Singapore Singapore

ISBN: (纸本)9781509006199

Information visualization is essential for improving effectiveness and efficiency of data exploration and knowledge discovery. Therefore, visualization has been used in a wide range of fields from biology, medicine, criminal activity analysis to business and education. Information visualization has become more important than ever as the amount of data being generated has increased dramatically in recent years. One of the major difficulties of information visualization is performance, and this is even more critical when visualizing big data. One potential solution to this challenge is data sampling while maintaining fidelity of visual representation. In this paper, we propose two new centrality clustering-based sampling approaches that apply centrality measures on clusters of data points in order to make more informed sampling than random sampling approaches. We evaluate the new methods on graph data sets. The results show that the new methods significantly outperform existing data sampling methods in term of perceived differences and their ability to preserve essential visual information. Moreover computational complexity is comparable or even better than simple random sampling methods.

关键词： big data big data visualization data sampling clustering algorithm cluster sampling large data sets

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An Innovative Methodology for big data visualization in Oceanographic Domain

An Innovative Methodology for Big Data Visualization in Ocea...

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作者： Antonino Galletta Salma Allam Lorenzo Carnevale Moulay Ali Bekri Rachid El Ouahbi Massimo Villari MIFT Department University of Messina Lab MIASH & Lab MACS Department of Mathematics & Computer ScienceFaculty of SciencesMoulay Ismail University Lab MACS Department of Mathematics & Computer ScienceFaculty of SciencesMoulay Ismail University Lab MIASH Department of Mathematics & Computer ScienceFaculty of SciencesMoulay Ismail University

Nowadays,thanks to new technologies,we are observing an explosion of data in different fields such as clinical,environmental and so *** this context,a typical example of the well-known big data problem is represented by *** this work,we propose an innovative platform for managing the oceanographic *** specifically,we present two innovative visualization techniques:general overview and site specific *** prove the goodness of the proposed system in terms both of performance and user experience.

关键词： Oceanography Acidification big data big data visualization microservices IoT geolocation

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