Stream Analyzer for Real-Time Digital Content Classification
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Solution Overview
Problem
The rapid dissemination of information over the Internet poses challenges in identifying and filtering out harmful or dangerous content, necessitating improved systems for real-time classification and filtering of digital data streams.
Innovation Solution
A digital-content-analysis system comprising a network services component, a stream monitor, and a stream analyzer that separates and classifies data streams into categories using a lexical analyzer and classifier, with a rating system and parameter sharing mechanism across a network, enabling distributed analysis and filtering of digital content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time classification of data streams is implemented, then harmful content identification capability is improved, but processing time and computational resources are consumed
Solution Approach 1:
The data stream is segmented into discrete tokens through lexical analysis, allowing the classifier to process individual units rather than entire streams at once. This segmentation enables more efficient processing while maintaining real-time classification capability, as the classifier can evaluate tokens independently and accumulate results as the stream progresses.
Solution Approach 2:
The system performs preliminary lexical analysis to tokenize and normalize data before classification. This preliminary processing prepares the data in advance, transforming it into a format suitable for rapid classification. By preparing the data structure beforehand, the actual classification operation can proceed more quickly without sacrificing accuracy.
2Reliability
If distributed analysis system is deployed, then classification coverage and reliability are improved, but system complexity increases
Solution Approach 1:
The stream analyzer is designed as a universal component that can classify multiple types of digital content streams (text, email, web pages, etc.) using the same core classification mechanism. This multi-functionality allows a single distributed system architecture to handle diverse content types, improving coverage without requiring separate specialized systems for each content type.
Solution Approach 2:
The network services component acts as an intermediary layer between the distributed stream analyzers and the rest of the system. It manages communication protocols, coordinates requests and responses, and handles parameter sharing between distributed nodes. This intermediary layer simplifies the overall system architecture by centralizing coordination logic, making the distributed system easier to implement and maintain.
3Measurement precision
If parameter sharing across network is implemented, then classification accuracy is improved, but network communication overhead increases
Solution Approach 1:
The system shares only the necessary parameters required for accurate classification rather than transmitting complete datasets or all possible parameters. This partial sharing approach reduces network communication overhead while maintaining sufficient accuracy. The network services component selectively transmits only the parameter subsets needed for each classification task, avoiding unnecessary data transfer.
Data Source
AI summary
A digital-content-analysis system includes a network services component adapted to receive a remote service request, a stream monitor adapted to form digital content into at least one data stream, and a stream analyzer interoperably coupled to the network services component and the stream monitor and adapted to receive and analyze the at least one data stream. The stream analyzer includes a lexical analyzer adapted to separate the at least one data stream into a plurality of tokens, a classifier adapted to classify the at least one data stream into at least one category by processing at least a subset of the plurality of tokens and computing a classification rating for at least one category, and a set of parameters. At least a subset of the parameters contains information developed from previous operation of the stream analyzer.