Skip to main content

Data Fabric

Data Fabric​

A Data Fabric creates a structured, interconnected representation of your data using Attention-Based Graph Embeddings. This fabric enhances retrieval capabilities and supports advanced graph traversal techniques for deeper insights.

Semantic Data Fabric A Voronoi point map representing a 2D plot of contextually aware vectors as interconnected points in the data fabric.


Key Features:​

  • Integrated Context: Combines multiple data points and their attributes, capturing the full context of the information.
  • Structured Representation: Forms a network of interconnected data points, enabling advanced search and analysis techniques.
  • Enhanced Retrieval: Supports natural language search queries that consider the context and connections embedded in the data, going beyond mere keyword matching.

Creating a Data Fabric​

Various methodologies exist to create a data fabric, each tailored to different types of data and analytical needs:

1. Attention Graph Network​

index Attention Graph Network leverages attention matrices from transformer models. This method focuses on the relationships identified by attention weights between different entities or concepts within a data sequence.

  • Attention Mechanisms: Attention matrices reveal the strength and relevance of connections between data points.

  • Fabric Construction: These attention-weighted relationships are mapped into a data fabric, where nodes represent entities/concepts and edges signify the strength of their connections.

  • Use Case: Ideal for scenarios where semantic understanding of textual data is crucial, such as natural language processing tasks, semantic search queries, or any textual data analysis.

2. Temporal Graph Network​

index

Temporal Graph Networks use a module-based framework to convert time-series data—whether textual or numerical—into Attention-Based Graph Embeddings. This approach, combined with unsupervised algorithms, automatically detects anomalies.

  • Contextual Embeddings of Time-Series Data: Transforms time-series data into embeddings that capture temporal patterns and trends within the data.

  • Anomaly Detection and Forecasting: Utilizes unsupervised algorithms as part of a comprehensive anomaly detection pipeline to identify anomalies.

  • Use Case: This approach is particularly useful in fields like finance or IoT, where understanding temporal trends or detecting anomalies in time-series data is critical.

3. Code Fabric​

index

Code Fabric is designed to enhance the efficiency of code management and discovery within enterprises. Every piece of code—classes, objects, or methods—is represented as a graph, where the relationships between these components form the fabric.

  • Fabric Representation of Code: Code is treated as a network of interconnected components (e.g., classes, methods, variables). The system identifies and maps these relationships, allowing for easy discovery and reuse of similar code snippets.

  • Efficiency in Code Reuse: By connecting related pieces of code, this fabric helps developers quickly identify reusable components, improving development efficiency.

  • Use Case: articularly useful in large codebases where finding and reusing existing code components can save time and resources, especially in collaborative environments.

Conclusion​

A Data Fabric represents a data transformation approach to organizing and analyzing complex datasets by creating an interconnected network of data points.