Big Data analytics are emerging fields that have recently drawn much attention from research communities in computer science, information technology, social science, and many other disciplines. In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. 2024 IEEE the 9th International Conference on Big Data Analytics (ICBDA) provides a leading forum for disseminating the latest research in Big Data Research, Development, and Application.
ICBDA calls for submission of work from a wide range of domains, including but not limited to Big Data Models and Algorithms, Big Data Architectures, Big Data Search and Mining, healthcare, social networks, and media. Authors are encouraged to submit papers covering big data topics, such as service-oriented technologies, machine learning, predictive analytics, data modeling, system architectures, data mining, and simulation.
ICBDA2024 is co-sponsored by Waseda University, Japan and Research Institute of Big Data Analytics, Xi'an Jiaotong-Liverpool University, China. Prof. Steven Guan (Xi'an Jiaotong-Liverpool University, China) and Prof. Hayato YAMANA (Waseda University, Japan) take charge of the Conference Co-chairs.
Aims and Scope
2024 the 9th International Conference on Big Data Analytics (ICBDA) has established itself as the world’s premier research conference in Big Data Analytics. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. ICBDA draws researchers, application developers, and practitioners from a wide range of Big Data Analytics related areas such as Big Data Models and Algorithms, Big Data Architectures, Big Data Management, Big Data Protection, Big Data Search and Mining and Big Data for Enterprise, Government and Society. By promoting novel, high-quality research findings, and innovative solutions to challenging Big Data Analytics, the conference seeks to advance the state-of-the-art in Big Data Analytics.
Topics of Interest
Topics of interest include, but are not limited to:
Big Data Models and Algorithms
Foundational Models for Big Data
Algorithms and Programming Techniques for Big Data Processing
Big Data Analytics and Metrics
Representation Formats for Multimedia Big Data
Big Data Architectures
Cloud Computing Techniques for Big Data
Big Data as a Service
Big Data Open Platforms
Big Data in Mobile and Pervasive Computing
Big Data Management
Big Data Persistence and Preservation
Big Data Quality and Provenance Control
Management Issues of Social Network Big Data
Big Data Protection, Integrity and Privacy
Models and Languages for Big Data Protection
Privacy Preserving Big Data Analytics
Big Data Encryption
Security Applications of Big Data
Anomaly Detection in Very Large Scale Systems
Collaborative Threat Detection using Big Data Analytics
Big Data Search and Mining
Algorithms and Systems for Big Data Search
Distributed, and Peer-to-peer Search
Machine learning based on Big Data
Visualization Analytics for Big Data
Big Data for Enterprise, Government and Society
Big Data Economics
Real-life Case Studies of Value Creation through Big Data Analytics
Big Data for Business Model Innovation
Big Data Toolkits
Big Data in Business Performance Management
SME-centric Big Data Analytics
Big Data for Vertical Industries (including Government, Healthcare, and Environment)
Scientific Applications of Big Data
Large-scale Social Media and Recommendation Systems
Experiences with Big Data Project Deployments
Big Data in Enterprise Management Models and Practices
Big Data in Government Management Models and Practices
Big Data in Smart Planet Solutions
Big Data for Enterprise Transformation
Blockchain
Blockchain based lightweight data structures for IoT data
Blockchain based IoT security solutions
Blockchain in cyber physical systems
Blockchain in social networking
Bolckchain in crowdsourcing and crowdsensing
Blockchain in 5G
Blockchain in edge and cloud computing
Blockchain and trust management
