Encoding Type Determination via Statistical Distribution Analysis
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Solution Overview
Problem
Existing data processing technologies face challenges in distinguishing between binary and decimal encodings of floating-point data sets, which is crucial for accurate data exchange and processing across disparate systems.
Innovation Solution
The method involves decoding data sets using both binary and densely packed decimal (DPD) encodings, comparing the resulting distributions to an expected distribution based on Benford's law, and selecting the appropriate encoding type based on statistical analysis, such as chi-square tests, to determine the actual encoding type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is decoded using a specific encoding type (binary or DPD), then decoding efficiency is improved, but the ability to handle unknown or mixed encoding types deteriorates
Solution Approach 1:
The system performs preliminary actions by decoding the data set using multiple encoding types (binary and DPD) before determining the actual encoding type. This allows the system to prepare multiple versions of the decoded data simultaneously, enabling efficient processing once the correct encoding is identified through statistical distribution comparison.
Solution Approach 2:
The system introduces an intermediary approach by using statistical distribution analysis (comparing decoded distributions to expected distributions) as a mediator between the decoding process and the final processing decision. This intermediary step enables the system to automatically identify the correct encoding type without requiring explicit encoding type information in the data.
2Measurement precision
If multiple encoding types are decoded and compared, then encoding type identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies partial action by decoding and analyzing only the necessary portions of the data (specifically the first digit distributions) rather than processing the entire data set exhaustively. This partial analysis of statistical distributions provides sufficient accuracy for encoding type identification while significantly reducing computational complexity compared to full data processing.
Solution Approach 2:
The system changes the parameter of analysis from the raw decoded values to their statistical distributions (frequency of first digits). This parameter transformation allows for more efficient comparison and identification, as distribution patterns are more distinctive and easier to compare against expected distributions than individual values.
3Reliability
If statistical analysis is performed on decoded distributions, then encoding type determination reliability is improved, but processing time increases
Solution Approach 1:
The system segments the analysis by focusing on specific statistical characteristics (first digit distributions) rather than analyzing all aspects of the decoded data. This segmentation approach maintains high reliability in encoding type determination while reducing processing time, as the first digit distribution is a distinctive and sufficient statistic for identification.
Data Source
AI summary
Implementations of the present disclosure provide for determining an encoding type of data. Implementations include receiving a data set from a computer-readable storage medium, decoding the data set using a first encoding type to provide a first plurality of numbers, generating a first distribution based on the first plurality of numbers, decoding the data set using a second encoding type to provide a second plurality of numbers, and generating a second distribution based on the second plurality of numbers. An actual encoding type of the data set is determined based on the first distribution, the second distribution and an expected distribution, and the data set is processed based on the actual encoding type.