Data Transformation Layer

Data Querying

The Data Querying module implements a distributed query engine designed to efficiently retrieve data across the decentralized network. It supports multiple query languages, such as SQL and GraphQL, to provide flexibility for different use cases. The module leverages query optimization techniques, including predicate pushdown and distributed join algorithms, to enhance performance. Additionally, it implements caching mechanisms to further improve query efficiency. The module offers a federated query interface that abstracts the complexity of querying data from distributed sources, providing a seamless and simplified experience for users.

Data Enrichment

The Data Enrichment module enhances existing data by leveraging AI to integrate additional information from internal and external sources. Advanced AI-powered entity resolution techniques are used to intelligently link related data across different sources, ensuring high levels of accuracy and consistency. AI-based data fusion algorithms are employed to combine information from multiple sources, providing deeper insights and a more comprehensive dataset. The module also uses AI to automatically generate derived features and calculated fields, optimizing data for downstream use. Additionally, AI enables real-time enrichment pipelines for streaming data, allowing dynamic data augmentation and continuous learning for real-time decision-making.

Feature Engineering

The Feature Engineering Module is a critical component of the data transformation layer, responsible for creating, selecting, and transforming features to improve the performance of machine learning models and enhance data analysis capabilities. We will utilize both deterministic as well as AI / ML approaches for the following

• Feature Generation (Featuretools, Deep Feature Synthesis, etc.)

• Feature Selection (SHAP, Boruta, SelectKBest, etc.)

• Feature Transformation

MLops

The MLops module is responsible for managing the entire machine learning lifecycle within the decentralized environment. It implements version control for data, models, and code, ensuring traceability and reproducibility throughout the development process. The module provides automated pipelines for model training and validation, streamlining the workflow and enhancing efficiency. It also supports distributed model training across network nodes, leveraging the decentralized nature of the system for scalability and collaboration. Additionally, it includes infrastructure for model deployment and serving, enabling seamless integration into production environments. The MLops module monitors model performance and detects data drift, offering alerting mechanisms to ensure ongoing accuracy and relevance. Furthermore, it facilitates A/B testing and the gradual rollout of new models, ensuring controlled experimentation and deployment.

Data Transformation Orchestration

The Data Transformation Orchestration module manages the execution and sequencing of data transformation tasks, ensuring that processes run in the correct order. It implements workflow management for complex data processing pipelines, allowing for efficient coordination of multiple tasks. The module provides scheduling and triggering mechanisms to support both batch and real-time data processing. Additionally, it supports distributed task execution across network nodes, enabling scalability and collaboration within the decentralized network. The module also implements fault tolerance and error handling, ensuring that transformation jobs can recover from failures and maintain the integrity of the data processing workflow.