Hardware Text Analytics Processor for Real-Time Data
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Solution Overview
Problem
Current text analytics technologies face challenges in efficiently processing large volumes of unstructured data in real time, requiring significant computing resources and lacking flexibility across various applications.
Innovation Solution
A hardware-based programmable text analytics processor (TAP) comprising a tokenizer, tagger, parser, and classifier, implemented with dedicated hardware components and machine learning models, enabling real-time conversion of unstructured data into structured data for text analytics applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text analytics is performed on large volumes of unstructured data using conventional processing methods, then information extraction and analysis can be achieved, but computing resource consumption becomes excessively intensive and processing speed decreases
Solution Approach 1:
The system segments text analytics processing into distinct functional modules (tokenizer, tagger, parser, classifier) that can be independently configured and executed. Each module handles a specific aspect of text processing, allowing parallel execution and optimized resource allocation for different processing tasks simultaneously.
Solution Approach 2:
The processing pipeline is designed to be dynamically configurable, where the sequence and activation of processing modules can be adjusted based on specific application requirements. This allows the system to adapt processing intensity and resource allocation to match the actual complexity and volume of incoming data streams.
2Productivity
If dedicated hardware components are implemented for text analytics processing, then processing efficiency and real-time capability are improved, but device complexity increases
Solution Approach 1:
The hardware architecture implements universal processing modules that can handle multiple text analytics functions through configurable parameters and processing rules. The same physical hardware infrastructure supports various text processing tasks by dynamically adjusting module configurations rather than requiring dedicated hardware for each function.
Solution Approach 2:
The system employs a nested module structure where simpler processing functions (tokenization) are contained within more complex functions (parsing, classification). This hierarchical organization allows complex text analytics tasks to be broken down into manageable nested components, reducing overall system complexity while maintaining comprehensive processing capability.
3Adaptability or versatility
If customizable processing pipelines are formed for different text analytics applications, then flexibility and adaptability are enhanced, but system configuration complexity increases
Solution Approach 1:
The system provides pre-configured processing pipelines and module templates that are prepared in advance for common text analytics applications. Users can select and activate these pre-configured pipelines based on their specific needs, significantly reducing the configuration effort required while still achieving application-specific optimization.
Solution Approach 2:
The processing pipeline configuration includes automatic detection and adaptation capabilities that allow the system to self-configure optimal processing paths based on the characteristics of the input data and desired output. This reduces manual configuration complexity by enabling the system to automatically adjust module sequences and parameters.
Data Source
AI summary
A hardware-based programmable text analytics processor has a plurality of components including at least a tokenizer, a tagger, a parser, and a classifier. The tokenizer processes an input stream of unstructured text data and identifies a sequence of tokens along with their associated token ids. The tagger assigns a tag to each of the sequence of tokens from the tokenizer using a trained machine learning model. The parser parses the tagged tokens from the tagger and creates a parse tree for the tagged tokens via a plurality of shift, reduce and/or finalize transitions based on a trained machine learning model. The classifier performs classification for tagging and parsing by accepting features extracted by the tagger and the parser, classifying the features and returning classes of the features back to the tagger and the parser, respectively. The TAP then outputs structured data to be processed for various text analytics processing applications.