Big Data Management
The objective of big data organization is to ensure a high level of data quality and accessibility for business intelligence and big data analytics uses. Corporations, government agencies and other organizations employ big data management strategies to help them struggle with fast-growing pools of data, typically involving many terabytes or even petabytes of information saved with different file formats. Effective big data management helps companies locate valuable information in big sets of unstructured data and semi-structured data from a selection of sources, including call detail records, system logs and social media sites.
- Search and Mining of variety of data including scientific and engineering,social,sensor/IoT/IoE, and multimedia data
- Algorithms and Systems for Big Data Search
- Distributed, and Peer-to-peer Search
- Big Data Search Architectures, Scalability and Efficiency
- Data Acquisition, Integration, Cleaning, and Best Practices
- Visualization Analytics for Big Data
- Computational Modelling and Data Integration
- Large-scale Recommendation Systems and Social Media Systems
- Cloud/Grid/Stream Data Mining- Big Velocity Data Link and Graph Mining
- Semantic-based Data Mining and Data Pre-processing
- Mobility and Big Data
- Multimedia and Multi-structured Data- Big Variety Data
Related Conference of Big Data Management
September 10-11, 2024
7th International Conference on Artificial Intelligence, Machine Learning and Robotics
Amsterdam, Netherlands
October 24-25, 2024
10th World Congress on Computer Science, Machine Learning and Big Data
Zurich, Switzerland
November 25-26, 2024
10th International Conference and Expo on Computer Graphics & Animation
Vancouver, Canada
Big Data Management Conference Speakers
Recommended Sessions
- Big Data Analytics
- Big Data Infrastructure
- Big Data Management
- Big Data Security, Privacy and Trust
- Cloud computing.
- Clustering
- Cyber security
- Data Mining Applications in Science, Engineering, Healthcare and Medicine
- Data Mining Methods and Algorithms
- Data Warehousing
- ETL (Extract,Transform, And Load)
- Forecasting from Big Data
- New Visualization Techniques
- OLAP Technologies
- Ransomeware
- Tools, Techniques and Trends for Big Data Analytics.