Adaptive Compression Scheme Selection for Variable Input Streams
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Solution Overview
Problem
Existing data compression technologies lack dynamic auto-selection capabilities to optimize compression schemes based on input stream characteristics, leading to suboptimal compression ratios and throughput in various data types and applications.
Innovation Solution
A compression engine that dynamically selects a lossless compression scheme based on input stream characteristics such as pattern length, distance, gap between pattern matches, and standard deviations, using a combination of Look-Aside-Queue, Hash Units, Scoreboard Queue, and History Buffer to identify matches and select the appropriate encoding algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed compression scheme is used, then device complexity is reduced, but compression ratio and throughput become suboptimal for various data types
Solution Approach 1:
The patent implements dynamic compression scheme selection by analyzing input stream characteristics (pattern length, distance, gap between matches, standard deviations) and automatically choosing the most appropriate compression algorithm. This dynamic adaptation allows the system to optimize compression ratios for different data types while maintaining a unified compression engine architecture that manages multiple schemes.
Solution Approach 2:
The system changes operational parameters by selecting different compression schemes based on measured characteristics of the input data stream. By monitoring parameters such as pattern length, distance, and gap between matches, the system adjusts its compression approach to match the data characteristics, thereby improving compression efficiency without requiring manual configuration.
2Productivity
If a fixed compression scheme is used, then ease of operation is improved, but throughput becomes suboptimal for various data types
Solution Approach 1:
The compression engine performs self-service by automatically analyzing its input stream and selecting the most appropriate compression scheme without external intervention. The system monitors characteristics such as pattern length, distance, and gap between matches, then autonomously decides which compression algorithm to apply, thereby optimizing throughput for different data types while maintaining operational simplicity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring input stream characteristics and using this information to adjust its compression scheme selection. By measuring parameters like pattern length, distance, and gap between matches, the system receives feedback about the data being processed and adapts its compression approach accordingly, optimizing throughput while keeping the operation transparent to users.
Data Source
AI summary
A compression scheme can be selected for an input data stream based on characteristics of the input data stream. For example, when the input data stream is searched for pattern matches, input stream characteristics used to select a compression scheme can include one or more of: type and size of an input stream, a length of a pattern, a distance from a start of where the pattern is to be inserted to the beginning of where the pattern occurred previously, a gap between two pattern matches (including different or same patterns), standard deviation of a length of a pattern, standard deviation of a distance from a start of where the pattern is to be inserted to the beginning of where the pattern occurred previously, or standard deviation of a gap between two pattern matches. Criteria can be established whereby one or more characteristics are used to select a particular encoding scheme.