CALL FOR PAPERS | topics

Prospective authors are invited to submit high-quality original technical papers for presentation at the conference and publication in the conference proceedings.

Please check the topics of ICCCBDA 2024. The topics are not limited to those as below:/ Download the CFP Flyer

1. Cloud computing models, computing, and cloud applications

Topics of interest for submission include, but are not limited to:

Cloud and grid computing for big data
Cloud Application Architectures
Cloud Application Scalability and Availability
Cloud Applications Performance and Monitoring
Cloud computing and semantic web technologies
Cloud Computing Architecture and Systems
Cloud Computing Models, Simulations, Designs, and Paradigms
Cloud Computing Technologies, Services and Applications
Cloud Economics
Cloud for Enterprise Business Transformation
Cloud Management and Operations
Cloud Middleware Frameworks
Cloud Optimization and Automation
Cloud Quality and Performance
Cloud Resource Virtualization and Composition
Cloud Solution Design Patterns
Infrastructure and platform for smart computing
Models and tools for smart computing

2. Big Data Science and Engineering Applications

Topics of interest for submission include, but are not limited to:

Algorithms and Programming Techniques for Big Data Processing
Algorithms and Systems for Big Data Analysis Search
Big Data Analysis Analytics and Metrics
Big Data Analysis Architectures
Big Data Storage, Indexing, Searching and Querying
Big Data Analysis Encryption
Big Data Analysis in Mobile and Pervasive Computing
Big Data Analysis Management
Big Data Analysis Search and Mining
Big Data Open Platforms
Big Data Persistence and Preservation
Big Data Quality and Provenance Control
Big Data Toolkits
Collaborative Threat Detection using Big Data Analysis Analytics
Data mining, graph mining and data science
Experiences with Big Data Project Deployments
Foundational Models for Big Data
Hardware/software infrastructure for big data
Intelligent Data Processing
Machine learning and AI for big data
Models and Languages for Big Data Protection
Privacy Preserving Big Data Analytics
Representation Formats for Multimedia Big Data
Security and privacy for big data
Techniques, models and algorithms for big data
Tools and systems for big data
Visualization Analytics for Big Data
Cloud Computing Techniques for Big Data
Machine learning based on Big Data
Big data applications: Bioinformatics
Big Data as a Service
Big Data for Business Model Innovation
Big Data for Enterprise Transformation
Big Data for Vertical Industries (including Government, Healthcare, and Environment)
Big Data in Business Performance Management
Big Data in Enterprise Management Models and Practices
Big Data in Government Management Models and Practices
Big Data in Smart Planet Solutions
Big Data Analysis for Business Model Innovation
Big Data Analysis for Enterprise, Government and Society
Real-life Case Studies of Value Creation through Big Data Analytics
Framework for Data Understanding and Quality
New Data Standards and Programming Models
Multimedia and Multi-structured Data
City-scale Data Mining and Machine Learning
Issues, problems and opportunities in Big Data
Blockchain based lightweight data structures for IoT data
Management Issues of Social Network Big Data
Big Data Analysis Economics
SME-centric Big Data Analytics

1. 云计算模型,计算及云应用

大数据的云和网格计算
云应用程序体系结构
云应用程序的可伸缩性和可用性
云应用程序性能和监视
云计算和语义网络技术
云计算体系结构和系统
云计算模型,仿真,设计和范式
云计算技术,服务和应用程序
云经济学
企业业务转型的云
云管理和操作
云中间件框架
云优化和自动化
云质量和性能
云资源虚拟化和组成
云解决方案设计模式
智能计算的基础架构和平台
智能计算的模型和工具

2. 大数据科学与工程应用

大数据处理的算法和编程技术
大数据分析搜索算法和系统
大数据分析分析和指标
大数据分析体系结构
大数据存储、索引、检索和查询
大数据分析加密
移动和普遍计算中的大数据分析
大数据分析管理
大数据分析搜索和采矿
大数据打开平台
大数据持久性和保存
大数据质量和出处控制
大数据工具包
使用大数据分析的协作威胁检测
数据挖掘,图挖掘和数据科学
大数据项目部署的经验
大数据的基础模型
大数据的硬件/软件基础架构
智能数据处理
机器学习和大数据的AI
大数据保护的模型和语言
保留大数据分析的隐私
多媒体大数据的表示格式
大数据的安全性和隐私
大数据的技术,模型和算法
大数据的工具和系统
大数据的可视化分析
大数据的云计算技术
基于大数据的机器学习
大数据应用程序:生物信息学
大数据作为服务
商业模型创新的大数据
企业转型的大数据
垂直行业的大数据(包括政府,医疗保健和环境)
业务绩效管理中的大数据
企业管理模型和实践中的大数据
政府管理模型和实践中的大数据
智能星球解决方案中的大数据
商业模型创新的大数据分析
企业,政府和社会的大数据分析
通过大数据分析创造价值创造的现实案例研究
数据理解和质量框架
新的数据标准和编程模型
多媒体和多结构数据
城市规模的数据挖掘和机器学习
大数据中的问题、问题和机遇
基于区块链的物联网数据轻量级数据结构
社交网络大数据的管理问题
大数据分析经济学
以中小企业为中心的大数据分析