How does AI Agent cope with the challenge of fusing multi-source heterogeneous data?

AI Agents address the challenge of fusing multi-source heterogeneous data through a combination of techniques that ensure seamless integration, normalization, and contextual understanding of diverse data types. The process involves several key steps:

1. Data Ingestion and Preprocessing: AI Agents first collect data from multiple sources (e.g., structured databases, unstructured text, IoT sensors, images, or APIs). They then preprocess the data to handle inconsistencies, such as missing values, different formats, or varying scales. For example, if an AI Agent receives temperature data in Celsius from one source and Fahrenheit from another, it normalizes them to a single scale.

2. Schema Alignment and Ontology Mapping: Heterogeneous data often has different schemas or structures. AI Agents use schema matching or ontology-based approaches to map related concepts across sources. For instance, if one dataset uses "customer_id" and another uses "user_ID," the Agent aligns these fields logically.

3. Feature Engineering and Representation Learning: To fuse data effectively, AI Agents extract meaningful features or use representation learning (e.g., embeddings) to convert diverse data types into a common format. For example, text data might be converted into vector embeddings, while time-series sensor data is normalized into statistical features.

4. Fusion Techniques: Depending on the use case, AI Agents employ different fusion strategies:

   • Early Fusion: Combining raw data before processing (e.g., concatenating structured and unstructured data into a single model input).
   • Late Fusion: Processing each data source separately and combining predictions (e.g., aggregating results from text and image models).
   • Hybrid Fusion: A mix of early and late fusion for balanced performance.

5. Contextual Reasoning and Decision-Making: The fused data is analyzed in context to derive insights or make decisions. For example, an AI Agent in smart manufacturing might combine sensor data (machine health), maintenance logs (text), and production schedules (structured) to predict equipment failures.

Example: In healthcare, an AI Agent might integrate electronic health records (structured), doctor’s notes (unstructured text), and medical imaging (images) to provide a comprehensive patient diagnosis. The Agent normalizes lab results, extracts key information from notes, and analyzes scans to generate actionable insights.

For scalable and efficient data fusion, especially in cloud environments, Tencent Cloud’s Data Lakehouse and AI-powered Data Integration services can help manage multi-source data storage, processing, and real-time analytics. Additionally, Tencent Cloud TI-ONE (AI Platform) supports advanced machine learning models for heterogeneous data fusion tasks.