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Готові списки джерел за темами / Data Analysis and Visualization / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Data Analysis and Visualization.

Статті в журналах з теми "Data Analysis and Visualization"

Автор: Grafiati

Опубліковано: 13 лютого 2024

Оновлено: 25 липня 2025

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1

C Nair, Geethu. "Data Visualization Tools: A Comparative Analysis." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 1599–602. https://doi.org/10.21275/sr241127115505.

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2

Keswani, Hrishikesh, Krishit Shah, Hritik Hassani, Moses Gadkar, and Er Manoj Kavedia. "Data Visualization and Analysis of COVID-19 Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 1328–37. http://dx.doi.org/10.22214/ijraset.2022.47179.

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Анотація:

Abstract: During the COVID-19 pandemic, many data visualizations were created to alert the public to the rapidly growing threat. Statistics on the spread of COVID-19 have been displayed on data dashboards, a mechanism for sharing information throughout the pandemic, which has aided in this process. When developing the visuals for COVID-19, the majority of time was spent on the technical aspects of designing and evaluating various visualization methods. Little is understood about the inner workings of visualization production processes due to the complex sociotechnical environments in which the

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3

Chin, George, Mudita Singhal, Grant Nakamura, Vidhya Gurumoorthi, and Natalie Freeman-Cadoret. "Visual Analysis of Dynamic Data Streams." Information Visualization 8, no. 3 (2009): 212–29. http://dx.doi.org/10.1057/ivs.2009.18.

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Анотація:

For scientific data visualizations, real-time data streams present many interesting challenges when compared to static data. Real-time data are dynamic, transient, high-volume and temporal. Effective visualizations need to be able to accommodate dynamic data behavior as well as Abstract and present the data in ways that make sense to and are usable by humans. The Visual Content Analysis of Real-Time Data Streams project at the Pacific Northwest National Laboratory is researching and prototyping dynamic visualization techniques and tools to help facilitate human understanding and comprehension

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4

Dessiaming, Takdir Zulhaq, Siska Anraeni, and Suwito Pomalingo. "COLLEGE ACADEMIC DATA ANALYSIS USING DATA VISUALIZATION." Jurnal Teknik Informatika (Jutif) 3, no. 5 (2022): 1203–12. http://dx.doi.org/10.20884/1.jutif.2022.3.5.310.

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Data is a collection of information that contains a broad picture related to a situation. The amount of data is not necessarily better, because a large data set makes it difficult to convert data into information in a timely manner, especially in analyzing data which produces meaningful and relevant information which ultimately results in quick and appropriate action. Higher education management in Indonesia requires fast and accurate academic reports so that it can facilitate strategic decision making in order to improve the quality of education. This study aims to carry out a comprehensive p

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5

O'Donoghue, Seán I., Benedetta Frida Baldi, Susan J. Clark, et al. "Visualization of Biomedical Data." Annual Review of Biomedical Data Science 1, no. 1 (2018): 275–304. http://dx.doi.org/10.1146/annurev-biodatasci-080917-013424.

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The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the pro

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6

Kullman, Kaur, and Don Engel. "Interactive Stereoscopically Perceivable Multidimensional Data Visualizations for Cybersecurity." Journal of Defence & Security Technologies 4, no. 1 (2022): 37–52. http://dx.doi.org/10.46713/jdst.004.03.

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Анотація:

Interactive Data Visualizations (IDV) can be useful for cybersecurity subject matter experts (CSMEs) while they are exploring new data or investigating familiar datasets for anomalies, correlating events, etc. For an IDV to be useful to a CSME, interaction with that visualization should be simple and intuitive (free of additional mental tasks) and the visualization’s layout must map to a CSME's understanding. While CSMEs may learn to interpret visualizations created by others, they should be encouraged to visualize their datasets in ways that best reflect their own ways of thinking. Developing

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7

Heer, Jeffrey, and Joseph M. Hellerstein. "Data visualization and social data analysis." Proceedings of the VLDB Endowment 2, no. 2 (2009): 1656–57. http://dx.doi.org/10.14778/1687553.1687621.

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8

Cruz, António, Joel P. Arrais, and Penousal Machado. "Interactive and coordinated visualization approaches for biological data analysis." Briefings in Bioinformatics 20, no. 4 (2018): 1513–23. http://dx.doi.org/10.1093/bib/bby019.

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AbstractThe field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only repr

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9

Kharismatunnisaa, Fiona, and Yourdan Saputra. "Analysis of Google Play Store Apps Data Using Tableau Data Visualization Application." Journal of Applied Science, Technology & Humanities 1, no. 3 (2024): 280–85. http://dx.doi.org/10.62535/fct2yw28.

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This research aims to enhance understanding of big data management and processing. One of the challenges faced is the complexity and large volume of data, which requires effective tools and techniques for analysis and visualization. The objective of this study is to analyze Google Play Store app data based on categories and ratings, and to visualize the results using Tableau. The research method employs a quantitative approach with a framework that includes problem formulation, data collection from the Google Play Store Apps database obtained from kaggle.com, data processing, and analysis usin

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10

Vineet, Patel, Kumar Maurya Rajan, Sharma Pooja, and Aishwarya. "Tools and Techniques of Data Visualization." Journal of Advancement in Parallel Computing 6, no. 2 (2023): 20–40. https://doi.org/10.5281/zenodo.7928983.

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Анотація:

<em>Data visualization is the process of displaying data in a graphical or pictorial style to make it easier to understand. It aids in establishing facts and deciding on courses of action. It will help any field of study that needs creative ways to convey substantial amounts of complicated material. Large datasets may be shown graphically using data visualization, a powerful tool. Several methods and technologies for data visualization are in use. </em><em>Data Presentation and Data Exploration are two major objectives of data visualization technologies. Many decision-makers rely on data visua

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11

Kim, Min Jung, and Eun Ryung Hyun. "Analysis of User Empathy Levels Based on Types of Data Visualization." Korea Institute of Design Research Society 8, no. 4 (2023): 256–66. http://dx.doi.org/10.46248/kidrs.2023.4.256.

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This study aims to explore techniques for eliciting emotional empathy in data visualization. To achieve this, it utilizes preceding research to derive tools for measuring levels of empathy, and analyzes the impact of different types of data visualization on empathy and charitable behaviors to develop a humanism-based data design strategy. The methodology encompasses both literature review and empirical research, reviewing 11 previous studies to identify the types of data visualization and tools for empathy measurement. For empirical analysis, four types of visualizations were created and subje

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12

Devineni, Siva Karthik. "AI-Enhanced Data Visualization: Transforming Complex Data into Actionable Insights." Journal of Technology and Systems 6, no. 3 (2024): 52–77. http://dx.doi.org/10.47941/jts.1911.

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Purpose: The purpose of this study is to explore how artificial intelligence (AI) becomes a part of data visualization. Thus, data from complex datasets are transformed into dynamic, interactive, and personalized visual experiences that will help in deeper insights and actionable knowledge. The research is supposed to design a holistic system and rules for using AI to make data visualization more effective and super interactive for the users. Methodology: The methodology involves the in-depth examination of artificial intelligence-based data visualization tools and platforms by using case stud

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13

Gadiparthi, Sivanagaraju. "Effective Visualization Techniques for Multi-dimensional Data: A Comparative Analysis." International Journal of Science and Research (IJSR) 13, no. 5 (2024): 152–56. http://dx.doi.org/10.21275/sr24501104057.

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14

T., Aditya Sai Srinivas, Sravanthi Y., Vinod Kumar Y., and Dwaraka Srihith I.V. "From Charts to Dashboards: An Overview of Data Visualization Methods." Advancement of Computer Technology and its Applications 7, no. 1 (2023): 1–14. https://doi.org/10.5281/zenodo.10060647.

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<i>Data Visualization plays a pivotal role in data science and analytics, involving the representation of data through graphical means like charts, graphs, and maps. These visualizations serve as powerful tools to unveil hidden patterns and insights within intricate datasets. As a Data Science professional, familiarity with diverse data visualization techniques is indispensable. This article provides an exploration of these techniques, offering insights into the specific charts and graphs associated with each.</i>

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15

Singh,, Annu. "Democratizing Data Visualization and Insights Extraction with Pandas, Generative AI, and CSV Data." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33437.

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Data visualization and insights extraction are crucial components of modern data-driven decision-making processes. However, traditional methods often require extensive coding knowledge, creating barriers for non-technical users. This whitepaper presents a comprehensive solution that integrates the powerful data manipulation capabilities of the Pandas library with cutting-edge Generative AI and natural language processing techniques. By leveraging a fine-tuned GPT-3 model trained on a diverse corpus of data analysis and visualization resources, our approach enables users to upload CSV data file

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16

Gudhadhe, Mrudula M. "Grade Scope Analysis: Data Visualization." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 7058–64. https://doi.org/10.22214/ijraset.2025.70073.

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Abstract: In spite of the fast growth of educational technology, the on-going challenge of converting raw data about students into useful insights continues to slow education progress. Grade Scope Analysis: Empowering Educators Using Modern Data Analytics and Interactive Visualization Technologies addresses this gap by providing an integrated system allowing educators, administrators, and policymakers to understand students' performance by means of interactive and dynamic visualizations. Made available to educational institutions of all levels, the system effectively transforms enormous quanti

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17

Pandey, Aryamaan. "Comparative Study of Data Visualization Tools in Big Data Analysis for Business Intelligence." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2591–600. http://dx.doi.org/10.22214/ijraset.2022.44400.

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Abstract: In this era of information, the development of figures has radically expanded, where a colossal amount of data is being delivered from various sources. Because of this huge collection, the worth of information turns into a significant component in each perspective. Data communication is very important to any business - be it small, midsize orBrobdingnagian. Businesses need Data Visualizations to identify data trends at a rapid pace, which would otherwise be tedious. Data Visualization is a robust technology capable of presenting a large dataset in a graphical format. Its centrality g

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18

Researcher. "VISUAL RHETORIC IN THE INFORMATION ERA: ADVANCING DATA COMMUNICATION THROUGH EFFECTIVE VISUALIZATION STRATEGIES." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 253–66. https://doi.org/10.5281/zenodo.13789089.

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This article explores the fundamental principles and advanced techniques of effective data visualization in the context of modern data storytelling. We examine the cognitive basis for visual information processing and distinguish between mere data presentation and impactful data narratives. The article delineates four core principles of successful data visualization: clarity, relevance, aesthetics, and interactivity. Through a comprehensive analysis of current tools and methodologies, we demonstrate how these principles can be applied to transform complex datasets into compelling visual narrat

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19

Qu, Kecheng. "Application of data visualization in enterprise data analysis." MATEC Web of Conferences 395 (2024): 01038. http://dx.doi.org/10.1051/matecconf/202439501038.

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Data visualization is an indispensable part of data analysis, transforming abstract data into intuitive information through charts, images and other forms to help people better understand the laws and trends behind the data. As the volume of data continues to increase, people need to understand and analyze data more intuitively and effectively. As a result, data visualization becomes a powerful tool that can help people extract useful information from massive amounts of data and present it in an intuitive way. This paper will focus on the concept, technology and application of data visualizati

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20

Zhang, Ying, Karsten Klein, Oliver Deussen, Theodor Gutschlag, and Sabine Storandt. "Robust visualization of trajectory data." it - Information Technology 64, no. 4-5 (2022): 181–91. http://dx.doi.org/10.1515/itit-2022-0036.

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Abstract The analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large and complex trajectory data sets are routinely collected these days. While automated analysis methods are available to extract characteristics of trajectories such as statistics on the geometry, movement patterns, and locations that might be associated with important events, human inspection is still required to interpret the results, derive parameters for the analysis, compare trajectories and patterns, and to

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21

Tong, Yuxuan. "Housing data visualization and analysis." Applied and Computational Engineering 69, no. 1 (2024): 154–61. http://dx.doi.org/10.54254/2755-2721/69/20241518.

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Data visualization is a powerful tool that can assist individuals and organisations in comprehending vast amounts of data and extracting valuable insights from it. The most significant function of data visualization is to make recommendations by figuring out the essence of the occurrence of the data. This paper will take housing data as an example, raise relevant questions, and reveal the logic behind the data and the relationship between variables through data visualization, linear regression, and other statistical methods.

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22

Kresh, J. Yasha, and Arthur D'Adamo. "Cardiovascular data visualization and analysis." Journal of the American College of Cardiology 17, no. 2 (1991): A14. http://dx.doi.org/10.1016/0735-1097(91)91023-8.

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23

Orner, Sylvia. "Data Visualization for Collection Analysis." Pennsylvania Libraries: Research & Practice 11, no. 1 (2023): 34–44. http://dx.doi.org/10.5195/palrap.2023.278.

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Анотація:

Due to the increasingly digital nature of library resources and collections, it is sometimes difficult to envision a library’s unified holdings and to understand how they have changed over time. Conducting a collection analysis and applying data visualization techniques can be an excellent way to get a top-down view of the collection as a whole. This article outlines the author’s process for a collection analysis of the Weinberg Memorial Library’s entire catalog of print and electronic resources. It explores the rationale behind some key collection analysis decisions and discusses approaches f

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24

Merlin, Florrence Joseph, and Lourdusamy Ravi. "Feature analysis of ontology visualization methods and tools." Computer Science and Information Technologies 1, no. 2 (2020): 61–77. https://doi.org/10.11591/csit.v1i2.p61-77.

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Visualization is a technique of creating images, graphs or animations to share knowledge. Different kinds of visualization methods and tools are available to envision the data in an efficient way. The visualization tools and techniques enable the user to understand the knowledge in an easy manner. Nowadays most of the information is presented semantically which provides knowledge based retrieval of the information. Knowledge based visualization tools are required to visualize semantic concepts. This article analyses the existing semantic based visualization tools and plug-ins. The features and

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25

Wang, Lidong. "Big Data and IT Network Data Visualization." International Journal of Mathematical, Engineering and Management Sciences 3, no. 1 (2018): 9–16. http://dx.doi.org/10.33889/ijmems.2018.3.1-002.

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Visualization with graphs is popular in the data analysis of Information Technology (IT) networks or computer networks. An IT network is often modelled as a graph with hosts being nodes and traffic being flows on many edges. General visualization methods are introduced in this paper. Applications and technology progress of visualization in IT network analysis and big data in IT network visualization are presented. The challenges of visualization and Big Data analytics in IT network visualization are also discussed. Big Data analytics with High Performance Computing (HPC) techniques, especially

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26

.S, Harinishri. "COVID-19 DATA ANALYSIS AND DATA VISUALIZATION." International Journal of Engineering Applied Sciences and Technology 5, no. 4 (2020): 267–71. http://dx.doi.org/10.33564/ijeast.2020.v05i04.040.

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27

Kumar, Nitin, and Gaurav . "Data Analysis and Data Visualization using Python." International Journal of Computer Sciences and Engineering 7, no. 5 (2019): 1287–91. http://dx.doi.org/10.26438/ijcse/v7i5.12871291.

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28

Shelly, Mark A. "Exploratory Data Analysis: Data Visualization or Torture?" Infection Control and Hospital Epidemiology 17, no. 9 (1996): 605–12. http://dx.doi.org/10.2307/30141948.

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29

Shelly, Mark A. "Exploratory Data Analysis: Data Visualization or Torture?" Infection Control and Hospital Epidemiology 17, no. 9 (1996): 605–12. http://dx.doi.org/10.1086/647397.

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30

Sharma, Sharad, and Sri Chandra Dronavalli. "Data Analysis and Visualization of Crime Data." Electronic Imaging 36, no. 1 (2024): 364–1. http://dx.doi.org/10.2352/ei.2024.36.1.vda-364.

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31

Bangkit Habiburrohman, Budi Yanto, and Muhammad Arif. "Data Visualization Using Google Data Studio: A Case Study of the 2019 Presidential Election Results." JOURNAL OF ICT APLICATIONS AND SYSTEM 2, no. 2 (2023): 68–73. https://doi.org/10.56313/jictas.v2i2.396.

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The 2019 Indonesian Presidential Election generated a significant amount of data requiring effective visualization for analysis and comprehension. This study demonstrates the application of Google Data Studio, a data visualization tool, for creating interactive dashboards based on election results. The dataset was sourced from the official Bureau of Statistics and processed to produce visualizations such as scorecards, bar charts, and pie charts, facilitating detailed insights into regional and candidate-specific voting patterns. The methodology includes data collection, processing, and visual

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32

Jofre, Ana, Steve Szigeti, and Sara Diamond. "Materializing data." DAT Journal 1, no. 2 (2016): 2–14. http://dx.doi.org/10.29147/2526-1789.dat.2016v1i2p2-14.

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The visualization of data elucidates trends and patterns in the phenomena that the data represents, and opens accessibility to understanding complicated human and natural processes represented by data sets. Research indicates that interacting with a visualization amplfies cognition and analysis. A single visualization may show only one facet of the data. To examine the data from multiple perspectives, engaged citizens need to be able to construct their own visualizations from a data set. Many tools for data visualization have responded to this need, allowing non-data experts to manipulate and

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33

Ayoola, Israel. "Enhancing Business Decision-Making with Advanced Data Visualization: A Sectoral Comparative Analysis." International Journal of Research and Innovation in Social Science VIII, no. X (2024): 1–8. http://dx.doi.org/10.47772/ijriss.2024.8100001.

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In today’s data-driven business landscape, advanced data visualization techniques have emerged as critical tools for enhancing decision-making processes across various industries. This comparative study explores the impact of these techniques in sectors such as finance, healthcare, retail, and energy. The research highlights how interactive dashboards, heat maps, 3D visualizations, and geospatial tools facilitate real-time data interpretation, enabling decision-makers to identify trends, patterns, and anomalies more efficiently. By examining the adoption and application of data visualization t

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34

Nugroho, Agung Yuliyanto. "Data science: Building visual understanding." Jurnal Teknologi Cerdas 1, no. 1 (2024): 1–6. https://doi.org/10.70310/7y9mnh17.

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Анотація:

Data science is rapidly evolving and plays a vital role in a variety of fields, from business to healthcare. However, the complexity of the data generated often makes it difficult for stakeholders without a technical background to understand. Data visualization is here to bridge this gap, enabling complex information to be translated into more intuitive and understandable graphical representations. Through effective visualization, patterns, trends, and insights hidden in data can be identified more quickly, facilitating better and faster decision-making. This article explores the critical role

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35

Haimerl, Edgar. "The visualization of dialect data with VDM." Zeitschrift für romanische Philologie 139, no. 4 (2023): 991–1002. http://dx.doi.org/10.1515/zrp-2023-0040.

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Abstract Visualization of the results is the final and essential step in dialect data analysis. For geographically dispersed data such as dialect data, colorful maps have long been the standard of visualization. The focus is on polygon maps, which can be used to represent contiguous areas particularly well. The Dialectometric visualization of script data is quite similar to the visualization of data from dialect atlases. For this reason, the VDM application (Visual DialectoMetry) can be used for the analysis of text documents from the DocLing1 corpus with few modifications. After a short tour

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Yang, Bo, Dong Tian, and Guihua Shan. "Tobacco Spatial Data Intelligent Visual Analysis." Electronics 11, no. 7 (2022): 995. http://dx.doi.org/10.3390/electronics11070995.

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A multi-module visualization framework is designed and a visual analysis system called TobaccoGeoVis is implemented to analyze tobacco spatial data efficiently. The proposed system provides a visualization technology for overlaying multiple graphics on a map to enrich the form of tobacco spatial data visualization. The system also adopts artificial intelligence algorithms and multi-view linkage interactive methods and provides flexible data-attribute field mapping and graphical parameter configuration to analyze tobacco spatial data. We demonstrated that the system is user-friendly and the app

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37

Parhwal, Divyam. "DATA ANALYSIS OF METROLOGICAL DATA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34399.

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This project report is set to give an interactive visualization and analytical presentation for Meteorological records in Finland. These Meteorological records Data of Finland is recorded by way of integrating the three current infrastructures for numerical weather prediction, observational information and satellite tv for pc image processing and this is recorded. The Meteorological data used in the study consists of near- floor atmospheric elements including wind direction, apparent temperature, cloud layer(s), ceiling peak, visibility, current weather, wind velocity, cloud cowl and precipita

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38

Shi, Yang, Yechun Peng, Jieying Ding, Xingyu Lan, and Nan Cao. "Double Tap for This Post: Understanding the Communication of Data Visualization on Social Media." Proceedings of the ACM on Human-Computer Interaction 9, no. 2 (2025): 1–27. https://doi.org/10.1145/3710963.

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Data visualizations are increasingly used by news outlets on social media to communicate insights to a broad audience. However, little is known about how readers interact with and respond to data visualizations in these quick-consumption environments. In this work, we introduce a conceptual model that categorizes visualization reading that leads to the communication effect of likes on Instagram. The model was developed through a grounded theory analysis of the statements explaining the reasoning behind the likes of visualization, which were recorded from a preliminary study. Informed by coding

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39

Segall, Richard S. "Transdisciplinary Applications of Data Visualization and Data Mining Techniques as Represented for Human Diseases." Journal of Systemics, Cybernetics and Informatics 22, no. 7 (2024): 6–15. https://doi.org/10.54808/jsci.22.07.6.

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Data visualization and data mining are transdisciplinary tools for predictive and descriptive analytics. This presentation shows the abundance of data visualization tools currently available that are applicable for multi-disciplinary data. Some examples of visualization are presented as applied for multi-disciplines. Results of applying the most commonly used data visualization tools of Tableau [1] and Power BI [2] are presented as preliminary outputs of a funded Seed Money Grant that applies data for transmissible diseases for humans and also plant pathology. Tableau Software, LLC is an Ameri

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Dragan, Dinu, Veljko Petrovic, Dusan Gajic, Zarko Zivanov, and Dragan Ivetic. "An empirical study of data visualization techniques in PACS design." Computer Science and Information Systems 16, no. 1 (2019): 247–71. http://dx.doi.org/10.2298/csis180430017d.

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Анотація:

The paper presents an empirical study of multidimensional visualization techniques. The study is motivated by the problem of decision making in PACS (Picture Archiving and Communications System) design. A comprehensive survey of visualizations used in literature is performed and these survey results are then used to produce the final set of considered visualizations: tables (as control), scatterplots, parallel coordinates, and star plots. An electronic testing tool is developed to present visualizations to three sets of experimental subjects in order to determine which visualization technique

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41

Vera-Piazzini, Ofelia, Massimiliano Scarpa, and Fabio Peron. "Building Energy Simulation and Monitoring: A Review of Graphical Data Representation." Energies 16, no. 1 (2022): 390. http://dx.doi.org/10.3390/en16010390.

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Data visualization has become relevant in the framework of the evolution of big data analysis. Being able to understand data collected in a dynamic, interactive, and personalized way allows for better decisions to be made when optimizing and improving performance. Although its importance is known, there is a gap in the research regarding its design, choice criteria, and uses in the field of building energy consumption. Therefore, this review discusses the state-of-the-art of visualization techniques used in the field of energy performance, in particular by considering two types of building ana

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42

Akkucuk, Ulas, and Mehmet Nafi Artemel. "Patent Data Visualization." International Journal of Research in Business and Social Science (2147-4478) 5, no. 3 (2016): 66–79. http://dx.doi.org/10.20525/ijrbs.v5i3.358.

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The importance given by the governments to building a sound intellectual property infrastructure is increasing in developing countries and especially in Central Asian countries. This infrastructure is continuously improved to live up to a common standard in collaboration with government agencies, educational institutions and international agencies. In this paper, the infrastructure developments that took place in the Central Asian countries is going to be elaborated and furthermore some statistical analyses will be used in order to compare the differences and similarities between the Central A

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Hua, Ziyi. "Data Visualization and Prediction Model Analysis." ITM Web of Conferences 70 (2025): 03003. https://doi.org/10.1051/itmconf/20257003003.

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Data visualization plays an important role in the process of data analysis. It can transform complex data into intuitive charts, which helps us understand data more effectively. The purpose of this paper is to study the application of data visualization and compare different prediction models. In this work, I present three different prediction models and apply them on the US airline industry datasets by using Python as the programming language. Then these data sets are made into a bar graph and analy1zed through these three prediction models. Finally, this paper provides some suggestions on ho

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44

Dahake, Prof Hemant, and Shahbaz Hasan Anwarul Hasan Sheikh. "BIM Data Analysis and Visualization Workflow." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2806–10. http://dx.doi.org/10.22214/ijraset.2023.52159.

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Abstract: Building Information Modeling (BIM) has emerged as a powerful technology for managing complex construction projects, providing a way to streamline communication, increase collaboration, and improve project outcomes. However, one area where BIM implementation still requires improvement is data analysis. The quality of data provided by BIM software is critical for making informed decisions, optimizing workflows, and improving project outcomes. This research paper comprehensively reviews the latest advancements in BIM data analysis and visualization techniques. The paper discusses the b

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Kiran, Choudhary, and Sharma Gajanand. "Big Data Visualization: Tools and Technique." Journal of Management Engineering and Information Technology (JMEIT) 5, no. 1 (2018): 1–2. https://doi.org/10.5281/zenodo.1186152.

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Data Visualization is the process of presenting row data into graphical format. It speeds up the process of data analysis. Visualization provide well designed visual encoding can exterminate cognitive calculation with ingenious perceptual conclusions and improve comprehension, memory, and decision making. Visual representations help people analytic thinking, because our brains process visual information efficiently. Data Visualization help to quickly interpret the complication of understanding data in numerical form.

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S., R., Priyanka V., Shivani S., and Dipanshu Nagpal. "Twitter Data Sentiment Analysis and Visualization." International Journal of Computer Applications 180, no. 20 (2018): 14–16. http://dx.doi.org/10.5120/ijca2018916463.

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Gupta, Vijay. "An Analysis of Data Visualization Tools." International Journal of Computer Applications 178, no. 10 (2019): 4–7. http://dx.doi.org/10.5120/ijca2019918811.

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Grané, Aurea, Giancarlo Manzi, and Silvia Salini. "Dynamic Mixed Data Analysis and Visualization." Entropy 24, no. 10 (2022): 1399. http://dx.doi.org/10.3390/e24101399.

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One of the consequences of the big data revolution is that data are more heterogeneous than ever. A new challenge appears when mixed-type data sets evolve over time and we are interested in the comparison among individuals. In this work, we propose a new protocol that integrates robust distances and visualization techniques for dynamic mixed data. In particular, given a time t∈T={1,2,…,N}, we start by measuring the proximity of n individuals in heterogeneous data by means of a robustified version of Gower’s metric (proposed by the authors in a previous work) yielding to a collection of distanc

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M, Mufeeda. "Crime Data Analysis, Visualization and Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. 3 (2021): 71–78. http://dx.doi.org/10.22214/ijraset.2021.32800.

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Post, Frits H. "Data Visualization: Featuring Interactive Visual Analysis." Computer Graphics Forum 30, no. 2 (2011): xxiii. http://dx.doi.org/10.1111/j.1467-8659.2011.01911.x.

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