integrated machine learning with data Base.pptx

seminar

1. Deep Learning Innovations In Database Systems Presented to Dr. Muhammed Ali Blaffer By: Hussein Fadhil Dhabab 2023-2024 University Of Tabriz Computer Engineering Faculty Artificial Intelligence Department

2. Introduction Deep learning (DL), a rapidly evolving subset of machine learning, has shown tremendous potential across various domains. Today, we will explore how deep learning is revolutionizing database systems, making them more efficient, intelligent, and adaptive. Our focus will cover innovations in query optimization, indexing, anomaly detection, data cleaning, and storage optimization.

3. Database Model for Deep Learning Applications The database model for a deep learning application revolves around efficiently managing datasets, data samples, labels, models, and performance metrics, ensuring seamless storage, retrieval, and analysis of data and models

4. Exploring Deep Learning Techniques in Database Systems Deep Learning Techniques used in Databases :- Convolutional Neural Networks ( CNNs) Recurrent Neural Networks ( RNNs) Autoencoders

5. Deep Learning's Applications in Databases Smarter Search: Find what you need faster, Deep learning understands the meaning behind your words . Prediction Powerhouse: Uncover hidden patterns for tasks like fraud detection, customer churn analysis, and more . Personalized Experiences: Deep learning personalizes recommendations and marketing, making them more relevant and effective . Data on a Diet: Automatically compress and clean data, saving space and boosting database efficiency.

6. Deep Learning Advancements in Database Systems Deep learning is revolutionizing databases! Here's a quick look: Query Optimization: Deep learning models can analyze past queries to suggest the fastest way to run new ones. Indexing and Search: Deep learning can create dynamic indexes and understand search intent for more accurate results. Security: Deep learning can detect anomalies and suspicious activity to prevent fraud and breaches. Data Integration: Deep learning can automate data mapping and transformation between different sources. Natural Language Processing: Users can interact with databases using natural language, making them more accessible.

7. Challenges and Future Directions Deep learning in databases offers exciting potential but faces challenges. Interpretability : Deep learning models are often opaque, making it hard to trust their decisions in databases . Scalability : Training these models requires significant resources, and they often need vast amounts of data . Privacy and Security: Deep learning can raise privacy concerns and be vulnerable to attacks.

8. Challenges and Future Directions Promising future directions include : Specialized architectures: Develop deep learning models specifically for database tasks. Privacy-preserving techniques: Leverage federated learning and differential privacy to train models on distributed data without compromising security. Integration with AI: Combine deep learning with other AI tools for more robust and interpretable database systems.

9. Conclusion Advancements in deep learning are revolutionizing database systems by enhancing query optimization, indexing, security, data integration, and natural language processing. These innovations improve efficiency, performance, security, accessibility, and user-friendliness. Continued research will lead to further integration of deep learning techniques, resulting in even greater improvements in database technology.

10. References Marcus, A., Mozafari, B., Beutel, A., Idreos, S., & Zdonik, S. (2019). Deep learning databases. arXiv preprint arXiv:1902.09136 . Chen, D., Ma, J., & Liu, Y. (2019). Deep learning for database systems: A survey. IEEE Transactions on Knowledge and Data Engineering, 31(7), 1353-1369 . Wang, R., Wu, J., Zhu, M., & Yu, P. S. (2020). Learning data placement in distributed databases. Proceedings of the VLDB Endowment, 13(12), 3050-3063

11. Thank You