November 09–10, 2024 | Melbourne, Australia
The 10th edition of the International Conference on Data Mining and Database Management Systems (DMDBS 2024) brought together data scientists, researchers, software engineers, and academics to explore the evolving landscape of intelligent data handling. Held in Melbourne, the two-day event featured keynotes, technical presentations, tutorials, and collaborative discussions centered on innovation in big data analytics, database performance, and AI-driven data solutions.
Key Highlights of DMDBS 2024
| Area of Focus | Description |
|---|---|
| Advanced Data Mining Techniques | Emphasis on deep learning, ensemble models, and hybrid algorithms for predictive analytics |
| Big Data Architecture | Presentations on scalable storage, distributed processing, and real-time data stream frameworks |
| AI and Knowledge Discovery | Integration of machine learning into automated data classification and anomaly detection |
| Database Optimization | Sessions on query performance, indexing strategies, and NoSQL tuning for high-load applications |
| Industry Use Cases | Case studies from healthcare, finance, and e-commerce showcasing real-world mining implementations |
| Workshops and Tutorials | Hands-on sessions on data lake architecture, cloud-native databases, and privacy-preserving mining |
Objectives of the Conference
- Explore latest trends in AI-powered data mining and intelligent database systems
- Connect researchers from academia and industry for global knowledge exchange
- Bridge research with practical applications to impact business and development
- Promote sustainable computing through efficient database systems and green AI
Themes and Technical Tracks
| Track | Key Topics Included |
|---|---|
| Machine Learning & AI | Classification, clustering, deep neural networks, reinforcement learning |
| Big Data Analytics | Hadoop, Spark, Flink, distributed systems, data wrangling |
| Data Warehousing & OLAP | ETL tools, cube analysis, performance tuning |
| Text & Web Mining | NLP, sentiment analysis, search algorithms |
| Database Models & Systems | Relational, graph, temporal, object-oriented, and document stores |
| Data Privacy & Security | Federated learning, anonymization, access control |
| Cloud and Edge Databases | Serverless DBs, IoT-integrated systems, 5G data architecture |
Participation and Delegates
| Category | Count (Approx.) |
|---|---|
| Researchers & Professors | 300+ |
| Industry Professionals | 150+ |
| Doctoral Scholars & Students | 200+ |
| International Delegates | 100+ (from 20+ countries) |
Keynote Speakers
Dr. Anya Kulkarni (MIT) – AI-Driven Data Mining: Challenges and Next Frontiers
Prof. David Wong (University of Melbourne) – Cloud-Native Databases for the Next Decade
Dr. Li Cheng (Tencent AI Lab) – Real-time Big Data Processing at Scale
Workshops & Tutorials
Mining Time-Series Data with LSTM Networks
Privacy-Preserving Computation in Federated Databases
Hands-on with Apache Iceberg & Delta Lake
Visualizing Big Data with Python Dash and Plotly
Feedback and Outcomes
| Aspect | Participant Comments |
|---|---|
| Organization | Seamless registration and time-bound sessions |
| Content Depth | Rich mix of theory, hands-on demos, and case studies |
| International Networking | Valuable global contacts and future collaboration leads |
| Practical Relevance | Industry sessions well-aligned with business challenges |
Future Recommendations
- Provide more hybrid participation options for remote attendees
- Introduce student paper presentation tracks at the undergraduate level
- Support collaborative research groups formed post-event
- Include a dedicated track on green computing and sustainable data practices