Data Engineering for Data Science

Part I. Governance and Integration.- Chapter 1. Text Data Integration.- Chapter 2. Exploring the Landscape of Data Fusion.- Chapter 3. Scalable and Privacy-aware Relational Data Synthesis.- Part II Storage and Processing.- Chapter 4. Comprehensive Approach to Feature Selection.- Chapter 5. Current Systems for Managing Massive High Frequency Time Series.- Chapter 6. MLOps Systems for Developing ML Pipelines.- Chapter 7. Workload Placement and Scheduling on Heterogeneous CPU-GPU Architectures.- Part III Preparation.- Chapter 8. Privacy-Preserving Blockchain-Based Federated Learning.- Chapter 9. Example-Based Explainability in Machine Learning.- Chapter 10. Table Search in Data Lakes: Methods, Indexing Techniques, and Research Challenges.- Part IV. Analysis.- Chapter 11. Adversarial Learning for Fraud Detection.- Chapter 12. Approximate and Adaptive Methods for Inference.- Chapter 13. Analysis of Unconstrained Trajectories, the Case of AIS.- Chapter 14. Network-constrained Trajectory Data for Traffic Analytics.

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• Bases de Dados
• Inteligência artificial
• Probabilidade e Estatística
• Captura e Análise de Dados

Open Access;data science;data analysis;information retrieval;data governance;information management;machine learning;time series analysis;federated learning;data lakes