[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-252822-105":3,"detail-sidebar-cat-1-en-105":81,"doc-detail-252822-en":126},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","open-research-issues-and-tools-for-visualization-and-big-data-analytics-read-research-paper","Open Research Issues and Tools for Visualization and Big Data Analytics - read research paper","","Digital growth accelerates data flows, making traditional processing unable to keep pace with massive, rapidly expanding datasets. In response, big data analytics systems are crucial to turn high-volume information into actionable insights for decisionmaking. This paper studies big data visualization, detailing its key characteristics, benefits, and challenges, and highlights tool support that serves both beginners and experienced users. The discussion connects visualization with analytics needs such as clarity, relevance, and operational efficiency.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/presentations/","Presentations",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/open-research-issues-and-tools-for-visualization-and-big-data-analytics-read-research-paper/252822/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/open-research-issues-and-tools-for-visualization-and-big-data-analytics-read-research-paper/252822.png","ImageObject",442,249,{"name":42,"@type":43},"Aladdin","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-27","2026-09-13",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",16,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why are big data analytics systems needed?","Question",{"text":63,"@type":64},"Massive data growth makes traditional processing unable to catch up, so analytics systems are needed to transform high-volume information into valuable, decision-ready insights.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What role does data visualization play in big data analytics?",{"text":68,"@type":64},"Data visualization makes big data more intelligible and enables accurate, interactive exploration of trends, patterns, and insights that are harder to detect from raw formats.",{"name":70,"@type":61,"acceptedAnswer":71},"Which challenges arise from the characteristics of big data?",{"text":72,"@type":64},"The paper highlights the 10Vs (extending the classic 3Vs) and emphasizes that well-designed systems must balance processing objectives with computational and financial costs.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},252822,1791742761,{"code":4,"msg":82,"data":83},"success",[84,88,93,98,103,107,112,117,122],{"id":85,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":86,"slug":87},11,90,"presentations",{"id":89,"doc_module":22,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},12,"Resumes",80,"resumes",{"id":94,"doc_module":22,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},14,"Invoices",70,"invoices",{"id":99,"doc_module":22,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},15,"Posters",60,"posters",{"id":55,"doc_module":22,"doc_module_name":25,"category_name":104,"show_sort_weight":105,"slug":106},"Social Media",50,"social-media",{"id":108,"doc_module":22,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},17,"Forms",40,"forms",{"id":113,"doc_module":22,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},18,"Letters",30,"letters",{"id":118,"doc_module":22,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},21,"Paper Templates",5,"papers-templates",{"id":123,"doc_module":22,"doc_module_name":25,"category_name":124,"show_sort_weight":4,"slug":125},158,"General","general-158",{"code":4,"msg":82,"data":127},{"doc_id":79,"user_id":128,"nickname":42,"user_avatar":129,"doc_module":22,"category_id":85,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":135,"language":136,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":12,"update_tm":140,"read_time":141},2336478503145,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","arXiv :2404 . 12505v1 [ cs .DB] 18 Apr 2024  \nOpen Research Issues and Tools for Visualization and Big Data Analytics  \nRania Mkhinini Gahar 1 ,2 Olfa Arfaoui 1 ,3 Minyar Sassi Hidri4  \n1 National Engineering School of Tunis, University of Tunis El Manar, Tunisia  \n2 OASIS Research Laboratory  \n3 RISC Research Laboratory  \n4 Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University,  \nDammam, Saudi Arabia  \nAbstract  \nThe new age of digital growth has marked all fields. This technological evolution has impacted data flows which have witnessed a rapid expansion over the last decade that makes the data traditional processing unable to catch up with the rapid flow of massive data. In this context, the implementation of a big data analytics system becomes crucial to make big data more relevant and valuable. Therefore, with these new opportunities appear new issues of processing very high data volumes requiring companies to look for big data-specialized solutions. These solutions are based on techniques to process these masses of information to facilitate decisionmaking. Among these solutions, we find data visualization which makes big data more intelligible allowing accurate illustrations that have become accessible to all. This paper examines the big data visualization project based on its characteristics, benefits, challenges and issues. The project, also, resulted in the provision of tools surging for beginners as well as well as experienced users.  \nKeywords: Big Data, DataViz, Cloud, Big Data Analytics, Business Intelligence.  \n1 Introduction  \nEvery day we generate 2 .5 trillion bytes of data. In fact, 90% of the world’s data was created in the past two recent years only [1] . The data comes from different sources such as the sensors used to collect climate information, social Media Posts, digital images, and online videos, online Purchase, transnational records and GPS (Global Positioning System) signals from mobile phones. This data, which has resulted essentially from the meeting of three elements which are Internet, social networks, and smart devices (computers, tablets, smartphones,  \nconnected objects...), is called big data or massive data. It is considered very interesting according to the pertinent information that may contain. Actually, we note that 13 of business leaders’ decisions are based on information they don’t trust or don’t have and half of them say they don’t have access to the information they need to do their job and 83% of CIOs (Chief Information Officer) cite analytical business intelligence (BI) as part of their plans to improve their competitiveness.  \nMoreover, 60% of CEOs (Chief Executive Officers) need to improve the capture and understanding of information in order to make decisions more faster [2] . For example, data could be analyzed to i) detect customer feelings and reactions or critical or life-threatening conditions in hospitals to intervene in time;  \nii) predict weather patterns to plan the optimal use of wind turbines and make decisions based on transactional data in real-time; iii) identify criminals and threats from videos, sounds and data streams; iv) studying student reactions during a lesson and predict which ones will succeed in the basis of statistics and models gathered over the years (big data in the education field) .  \nResearch on big data analytics is entering a new phase called fast data where multiple gigabytes of data arrive in the big data systems every second [3, 4, 5, 6, 7, 8] . Modern big data systems collect inherently complex data streams due to the 3 basic Vs which are Volume, Velocity, and Variety and to which are added Veracity, Validity, Vulnerability, Volatility, Visualization which consequently give rise to the 10Vs [9] of big data. The well-designed big data systems must be able to deal with all 10Vs effectively by creating a balance between data processing objectives and cost (i.e., computational, financ","cbCaicbYj9OH7m70","https://ap.wps.com/l/cbCaicbYj9OH7m70","pdf",895319,28,"English","# Introduction\n## Big data growth and its drivers\n## Data visualization as a core analytics enabler\n## The data challenges behind the 10Vs model","[{\"question\":\"Why are big data analytics systems needed?\",\"answer\":\"Massive data growth makes traditional processing unable to catch up, so analytics systems are needed to transform high-volume information into valuable, decision-ready insights.\"},{\"question\":\"What role does data visualization play in big data analytics?\",\"answer\":\"Data visualization makes big data more intelligible and enables accurate, interactive exploration of trends, patterns, and insights that are harder to detect from raw formats.\"},{\"question\":\"Which challenges arise from the characteristics of big data?\",\"answer\":\"The paper highlights the 10Vs (extending the classic 3Vs) and emphasizes that well-designed systems must balance processing objectives with computational and financial costs.\"}]","Open Research Issues and Tools for Visualization and Big Data Analytics - read research paper | PDF",1789263075,10]