Hardware-Accelerated Metadata Generation for Unstructured Data Search
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Solution Overview
Problem
Current systems face challenges in efficiently and unifiedly accessing and managing large volumes of unstructured data, as well as integrating it with structured data, due to limitations in indexing techniques and performance bottlenecks, leading to slow search times and inefficient data management.
Innovation Solution
The implementation of a hardware-accelerated system that uses a coprocessor to generate metadata for both structured and unstructured data, enabling rapid indexing and search operations by streaming data through a reconfigurable logic device, thereby reducing latency and enabling efficient management of larger data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional indexing techniques are used to manage unstructured data, then data management is simplified, but search performance deteriorates due to bottlenecks in processing large volumes of data
Solution Approach 1:
The patent segments the indexing process into distinct stages: data ingestion, metadata extraction, index construction, and search query processing. By dividing the workflow into modular components, the system can process different data types through specialized pipelines, improving overall throughput and reducing bottlenecks in handling large volumes of unstructured data
Solution Approach 2:
The patent implements preliminary indexing by extracting metadata and creating indexes during data ingestion rather than during search operations. This advance preparation stores pre-computed metadata and indexes in optimized data structures, enabling rapid retrieval during search operations without requiring real-time processing of raw data
2Adaptability or versatility
If more data is indexed to improve search coverage, then data management complexity increases, but access efficiency deteriorates due to resource constraints
Solution Approach 1:
The patent implements a universal indexing framework that handles multiple data types (structured, semi-structured, and unstructured data) through a common architecture. The system uses standardized metadata schemas and unified indexing operations that work across different data formats, eliminating the need for separate processing pipelines for each data type and reducing overall system complexity
Solution Approach 2:
The patent introduces metadata as an intermediary layer between raw data and search queries. This metadata abstraction layer standardizes access to diverse data types by extracting key attributes and relationships into a unified format, allowing the search system to operate on standardized metadata rather than dealing with the complexity of raw data formats directly
Data Source
AI summary
Disclosed herein are methods and systems for integrating an enterprise's structured and unstructured data to provide users and enterprise applications with efficient and intelligent access to that data. In accordance with exemplary embodiments, the generation of feature vectors about unstructured data can be hardware-accelerated by processing streaming unstructured data through a reconfigurable logic device, a graphics processor unit (GPU), or chip multi-processor (CMP) to determine features that can aid clustering of similar data objects.