Call for Papers -- Visualization in Big Data and Data Science for Business Applications
Visualization plays a crucial role in the representation of the data. Enterprises produce huge amounts of data every day, ranging from emails to different sources of web clicks or customer transactions. As a result, the need to use data analytics and visualization has grown in any business application. Big data and data science have played an important role in the medical field to the retail sector. The vast amount of information that needs to be handled in this scenario creates a challenge to deal with it effectively. Only visualization tools can provide the power of visual discovery through which the hidden value of data can be discovered. The appropriate use of visualization technologies covers the whole process of working on business problems with real-life scenarios, starting with data collection, transforming data into information, and taking the next step to extract knowledge. Data visualization is the representation of data on some form of medium and information communication through graphical traits. Big data and data science can improve your organization's business decisions by enabling them to find more insights within the data.
Data visualization represents a big piece in the context of big data analytics. It helps humans make sense of huge masses of information with ease. One of the main challenges businesses faces is how to get the most benefit from the data they collect. This challenge is further complicated as data can be collected from all corners of the organization and placed in one centralized database. Data visualization and data science for business applications play a vital role in assisting decision making and supporting strategic and tactical business operations. It is widely used by business intelligence professionals to effectively use the data available to drive insights and substantiate decisions. There is a big challenge associated with visualizing big data analytics, namely deploying technology over data science, to find the right balance between speed and accuracy. In the quest to give clients the best business solutions, it isn't easy to imagine collecting everything from simple structured data to vast amounts of non-structured real-time data to help build reliable business solutions, as businesses would need. These growing amounts of big data and advances in business applications impose higher demands on visualization tools.
In this context, it has become crucial to explore new visual analytics techniques that complement traditional data analysis processes and advance business executives' decision-making processes. Further, the scope of visualization in big data and data science is widespread, covering a broad range of topics from biology to health care, education to energy, banking to government, agriculture. As a result, the need for effective visualization is greater than ever. Against this background, this special issue focuses on the visualization of big data and data science by exploring key trends aiming to meet the demand from business applications. The purpose is to explore how visualization can be effectively applied to big data and data science for business applications.
Possible topics include, but are not limited to the following:
- Interactive knowledge discovery with big data visualization for emerging business applications
- Application of big data visualization across various business streams
- Advances in visual data analytics for business intelligence
- Collaborative visual analytics for emerging business applications
- Big data visualization and data science for emerging digital markets
- Adaptive visual analytics for business intelligence
- Emerging trends in business intelligence and analytics with big data visualization
- Visual trend analytics in emerging business era
- Innovative tools and technologies for big data and data science for business intelligence
- Challenges and opportunities of big data visualization for business intelligence
Guest Editors:
Prof. Alfred Daniel J, Karpagam Academy of Higher Education, Coimbatore, India
Prof. Ioannis Sarris, Department of Mechanical Engineering, University of West Attica, Athens, Greece
Dr. Faheim Sufi, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
Tentative Timeline:
Submission Deadline: January 20, 2025
Notification to Author: March 30, 2025
Revised Version Submission: May 20, 2025
Final Acceptance: July 20, 2025
