Big Data, Computer science, Analytics and Data Mining

Welcome Message

About Conference

The 12th World Congress on Big Data, Computer Science, Analytics and Data Mining, scheduled for January 25–26, 2027, in Paris, France, is a global platform designed to bring together researchers, academicians, data scientists, computer scientists, engineers, technology experts, industry professionals, and young researchers from around the world.

In today's rapidly evolving digital era, data has become one of the most valuable resources for scientific discovery, business innovation, intelligent decision-making, and technological advancement. The increasing volume, variety, and complexity of data have created new opportunities as well as significant challenges in areas such as data storage, processing, analysis, security, visualization, and knowledge discovery. At the same time, advances in artificial intelligence, machine learning, deep learning, cloud computing, distributed systems, and intelligent automation are transforming the way organizations collect, manage, and utilize information.

The conference aims to provide an interdisciplinary forum for discussing the latest research developments, innovative methodologies, technological applications, and future challenges in Big Data, Computer Science, Data Analytics, Artificial Intelligence, and Data Mining. The program will encourage participants to share original research, practical experiences, emerging ideas, and real-world applications across academia and industry.

The congress will feature keynote presentations, scientific sessions, oral and poster presentations, interactive discussions, workshops, and networking opportunities. Participants will have the opportunity to connect with international experts, establish professional collaborations, exchange knowledge, and explore innovative solutions to contemporary challenges in the digital world. The scope of the conference reflects current research directions across major computing and data-science events, including scalable data systems, AI and machine learning, data mining, cloud and edge computing, privacy, security, visualization, and responsible AI.

We warmly invite researchers, professionals, innovators, and students to join us in Paris, France, and contribute to meaningful discussions that will help shape the future of intelligent computing and data-driven technologies.

Sessions & Tracks

The 12th World Congress on Big Data, Computer Science, Analytics and Data Mining will feature a comprehensive scientific program covering fundamental research, emerging technologies, advanced computational methods, and real-world applications. The sessions are designed to encourage interdisciplinary collaboration and provide a platform for participants to explore current developments and future directions.

1: Big Data Technologies and Infrastructure

This track focuses on technologies and architectures designed to manage, process, and analyze large-scale and complex datasets. Topics may include Big Data platforms, distributed computing, scalable storage systems, data processing frameworks, stream computing, cloud-based infrastructure, high-performance computing, NoSQL databases, and data-intensive applications.

 2: Data Science and Advanced Analytics

This session explores modern approaches for extracting meaningful insights from structured, semi-structured, and unstructured data. Topics include statistical analysis, predictive analytics, prescriptive analytics, business intelligence, exploratory data analysis, time-series analysis, anomaly detection, decision-support systems, and large-scale data visualization.

3: Data Mining and Knowledge Discovery

This track will focus on innovative techniques for discovering hidden patterns, relationships, and valuable knowledge from large and complex datasets. Areas of interest include classification, clustering, association rule mining, pattern recognition, graph mining, web mining, text mining, social media analytics, information retrieval, and knowledge discovery systems.

4: Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning are transforming modern computing and data analysis. This session will cover supervised and unsupervised learning, reinforcement learning, deep learning, neural networks, intelligent systems, computational intelligence, predictive modeling, optimization techniques, and AI-powered applications. Current conference programs also increasingly include generative AI, foundation models, large language models, and AI agents.

5: Deep Learning and Intelligent Computing

This track will highlight recent developments in deep neural networks and advanced intelligent computing techniques. Topics may include convolutional neural networks, transformer architectures, representation learning, multimodal learning, reinforcement learning, generative models, intelligent automation, and applications of deep learning across healthcare, industry, finance, and other sectors.

6: Cloud, Edge and Distributed Computing

This session will explore the evolution of computing architectures supporting modern data-intensive applications. Topics include cloud computing, edge and fog computing, distributed systems, serverless computing, virtualization, resource management, scalable platforms, high-performance computing, and the integration of AI with cloud and edge environments.

 7: Data Engineering and Database Systems

This track focuses on the technologies and processes required to collect, organize, integrate, manage, and maintain high-quality data. Topics include data preprocessing, data cleaning, ETL pipelines, data integration, data warehousing, database management systems, NoSQL technologies, data quality, semantic data engineering, and scalable data architectures.

8: Natural Language Processing and Information Retrieval

This session will focus on computational techniques for understanding, processing, and generating human language. Areas include natural language processing, speech technologies, sentiment analysis, text classification, information extraction, conversational AI, search technologies, language models, multilingual computing, and intelligent information retrieval.

 9: Computer Vision and Multimedia Analytics

This track covers advanced computational approaches for analyzing images, videos, and multimedia data. Topics may include image processing, object detection, image recognition, video analytics, pattern recognition, medical imaging, remote sensing, augmented reality, virtual reality, and multimodal intelligent systems.

 10: Cybersecurity, Privacy and Data Protection

As organizations increasingly depend on digital systems and large-scale data, protecting information has become a critical priority. This session will explore cybersecurity, data privacy, secure computing, privacy-preserving analytics, federated learning, cryptography, data protection, trustworthy systems, risk management, and security challenges in cloud and Big Data environments.

 11: Internet of Things and Smart Technologies

This track will focus on connected devices and intelligent environments that generate and utilize large volumes of real-time data. Topics include the Internet of Things, sensor networks, cyber-physical systems, smart cities, smart healthcare, smart agriculture, industrial IoT, intelligent transportation, and real-time analytics.

 12: Data Visualization and Business Intelligence

Effective visualization and business intelligence tools help transform complex datasets into understandable and actionable insights. This session will explore interactive visualization, visual analytics, dashboards, business intelligence platforms, decision-support systems, storytelling with data, and human-centered approaches to data interpretation.

13: Responsible AI, Ethics and Data Governance

The growing influence of AI and data-driven systems has increased the importance of transparency, fairness, accountability, privacy, and ethical decision-making. This track will address explainable AI, trustworthy AI, algorithmic bias, responsible innovation, AI governance, data governance, regulatory challenges, and ethical frameworks for intelligent technologies.

14: Big Data Applications in Industry and Society

This session will showcase practical applications of Big Data, Analytics, and Artificial Intelligence across different sectors. Areas of interest include healthcare analytics, financial technologies, education, agriculture, manufacturing, retail, transportation, environmental monitoring, smart cities, digital government, and industrial automation.

 15: Emerging Technologies and Future Computing

This track is dedicated to emerging ideas and technologies that may shape the future of computer science and data-driven innovation. Topics may include quantum computing, blockchain, digital twins, autonomous systems, human-centered computing, next-generation networks, sustainable computing, green AI, and future computational paradigms.

Why to attend

• Opportunity to meet world’s Diabetes, Obesity & Heart Experts
• Keynote forums by renowned Physicians & Professors
• Plenary talks and Poster presentations by global experts
• Best platform for Global business and networking opportunities
• Oral & Poster presentations by Young Researchers
• Best poster & Young Research Forum awards
• Data Scientists and Data Analysts
• Big Data Engineers and Data Engineers
• Computer Scientists and IT Professionals
• Artificial Intelligence and Machine Learning Researchers
• Data Mining and Knowledge Discovery Experts

Who to attend

Faculty Members and Researchers:

•  Keynote Speech
•  Plenary Speech
•  Poster Presentation
•  Symposium (4-5 member team)        
•  Workshop

Universities, Associations & Societies:

•   Association Partnering
•   Academic Partnering  
•   Group Participation

Students and Research Scholars:

•   Poster Competition (Best Poster Award)       
•   Young Researcher Forum (YRF Award)
•   Group Participation  

Business Delegates:

•   Speaker Presentations
•   Symposium hosting   
•   Book Launch event   
•   Networking opportunities      
•   Audience participation

Product Manufacturers:

•   Exhibitor and Vendor booths
•   Sponsorship opportunities
•   Product launch
•   Workshop
•   Scientific Partnering
•   Marketing and Networking with clients

Market Analysis

The global market for Big Data, Computer Science, Analytics, Artificial Intelligence, and Data Mining is experiencing significant growth as organizations increasingly depend on data-driven technologies to improve decision-making, operational efficiency, customer experiences, and innovation. The rapid expansion of digital platforms, cloud computing, connected devices, social media, e-commerce, and intelligent systems continues to generate enormous volumes of structured and unstructured data.

According to recent market estimates, the global Big Data Analytics market is projected to grow from approximately USD 447.68 billion in 2026 to USD 1,176.57 billion by 2034, reflecting strong long-term demand for advanced analytics, AI integration, cloud-based platforms, and real-time data processing.

Another 2026 market analysis estimates that the broader Big Data and Analytics market could grow from USD 151.89 billion in 2026 to USD 249.06 billion by 2030, driven by increasing adoption of artificial intelligence, machine learning, predictive analytics, real-time data applications, industry-specific analytics solutions, and IoT integration.

Key Market Drivers

Several important factors are contributing to the rapid expansion of the industry:

• Increasing adoption of Artificial Intelligence and Machine Learning
• Growth of cloud computing and scalable data infrastructure
• Rising demand for real-time and predictive analytics
• Expansion of the Internet of Things and connected devices
• Increasing use of data-driven decision-making across industries
• Growing demand for cybersecurity and fraud detection
• Development of Generative AI and intelligent automation
• Increasing importance of data governance, privacy, and responsible AI

The integration of Generative AI, augmented analytics, intelligent automation, data fabric architectures, and cloud-native platforms is also reshaping how organizations collect, manage, analyze, and utilize information. At the same time, privacy concerns, cybersecurity risks, governance challenges, and the shortage of skilled professionals remain important challenges for the industry.

Industry Applications and Opportunities

Big Data and Analytics are now being applied across a wide range of sectors, including healthcare, finance, banking, retail, manufacturing, telecommunications, education, transportation, agriculture, government, and smart cities. Organizations are increasingly using advanced analytics to predict trends, identify risks, optimize supply chains, personalize customer experiences, detect fraud, and improve strategic decision-making.

The growing convergence of AI and Big Data is creating new opportunities for intelligent systems that can automate complex processes and transform large volumes of information into actionable insights. Recent market research also highlights the increasing importance of governed, scalable, and repeatable AI-driven intelligence across areas such as customer engagement, cybersecurity, finance, and supply-chain management.

Future Outlook

The future of the industry is expected to be strongly influenced by AI-powered analytics, autonomous and self-service analytics, real-time data processing, predictive and prescriptive intelligence, edge computing, cloud technologies, and responsible AI. Investment in AI infrastructure and cloud capacity also remains a major focus across the global technology sector, although organizations continue to evaluate the long-term returns and sustainability of these investments.

As the global digital ecosystem continues to expand, the need for skilled professionals, innovative research, and advanced technological solutions will continue to grow. The 12th World Congress on Big Data, Computer Science, Analytics and Data Mining provides a timely platform to explore these market developments, discuss emerging challenges, and examine the technologies that will shape the future of data-driven innovation.

Participants & participation options    

• Speaker: Deliver keynote or invited lectures, share research and innovations with global delegates.
• Delegate: Attend sessions, panel discussions, networking events; access conference materials and expert interactions.
• Poster Presenter: Showcase original research via printed or digital posters; engage in one-on-one discussions.
• Exhibitor: Display products, technologies, or services; network with delegates and potential collaborators.
• Video Presentation: Present research via recorded sessions; ideal for those unable to attend in person.
• E-Poster Presenter: Share research electronically; participate in virtual Q&A and discussions.
• Virtual Registration: Attend live-streamed sessions, access recordings, materials, and virtual networking; ideal for international participants.

Participant Benefits

• Earn CPD/CME points
• Networking with global experts and peers
• Certificate of participation
• DOI publication opportunities in conference proceedings
• Access to cutting-edge research
• Professional visibility for speakers, presenters, and exhibitors
• Opportunities for global collaboration

Abstract Details: Peer-reviewed; accepted abstracts published in proceedings with DOI for global visibility. Authors receive an Abstract Acceptance Letter confirming inclusion.

Secure Your Place & Submit Your Abstract

• Registration Link: https://bigdataanalytics.enggconferences.com/registration.php
• Abstract Submission Link: https://bigdataanalytics.enggconferences.com/abstract-submission.php
•  Don’t miss the opportunity to participate, present your research, and network with global experts.

For Inquiries:

            Phone: +44 2045861247

            Website: https://bigdataanalytics.enggconferences.com/

Past Conference Report-2026

The 2026 edition of the World Congress on Big Data, Computer Science, Analytics and Data Mining served as an important platform for bringing together researchers, academicians, computer scientists, data analysts, industry professionals, technology innovators, and students to exchange knowledge and discuss the latest developments in the rapidly evolving world of data and intelligent technologies.

The conference created opportunities for participants to present innovative research, share practical experiences, and explore emerging challenges in Big Data, Computer Science, Artificial Intelligence, Data Analytics, Machine Learning, and Data Mining. The scientific discussions highlighted the growing importance of transforming complex and large-scale datasets into meaningful knowledge that can support research, innovation, and strategic decision-making. A major focus of the 2026 discussions was the increasing integration of Artificial Intelligence with modern data ecosystems. Topics such as Generative AI, machine learning, intelligent automation, data platforms, scalable computing, data governance, and responsible AI continued to gain significant attention across the wider 2026 data and AI conference landscape.

The scientific program also emphasized the importance of data mining and knowledge discovery. Researchers explored techniques for identifying hidden patterns, relationships, trends, and valuable insights from large and complex datasets. Areas such as predictive analytics, pattern recognition, text and web mining, database systems, information retrieval, and intelligent decision-support systems demonstrated the growing role of data-driven technologies across multiple disciplines. Another important area of discussion was the development of modern computing infrastructure. Cloud computing, distributed systems, edge computing, high-performance computing, and scalable data architectures continue to provide the technological foundation for managing the increasing volume and complexity of digital information. Comparable 2026 conferences in Paris also reflected strong interest in the intersection of data mining, Big Data, databases, machine learning, cybersecurity, and computer science.

The 2026 conference landscape further highlighted the growing need for responsible and trustworthy technologies. As organizations increasingly adopt AI and automated decision-making systems, greater attention is being given to data privacy, cybersecurity, transparency, explainability, ethical AI, and effective data governance. These discussions demonstrated that technological innovation must be accompanied by responsible practices to ensure the secure and sustainable use of data. The event also encouraged international networking and interdisciplinary collaboration, enabling participants from academic institutions, research organizations, and industry to exchange perspectives and explore potential partnerships. The combination of scientific presentations, technical discussions, and professional interaction created a valuable environment for sharing ideas and promoting future research initiatives.

The broader 2026 data and AI ecosystem also showed a strong shift toward practical deployment and business value. For example, a major Paris-based 2026 event structured its program around modern data architecture, governance and regulation, and business applications of data and AI, reflecting the industry's increasing focus on moving from experimentation to real-world implementation.