Duplicate Index Compression Through Reordering and Self-Updates
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Solution Overview
Problem
Current data models in business applications often lack normalization of textual content, leading to increased index size and reduced usability and performance due to duplicate texts and documents, which are not normalized, causing inefficiencies in compression and query performance.
Innovation Solution
A compression scheme that combines duplicate index entries by reordering and self-updating, independent of the underlying data model, to reduce complexity and errors, and enhance supportability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If duplicate index entries are not combined, then query performance is maintained, but index size increases and compression efficiency decreases
Solution Approach 1:
The patent combines duplicate index entries by reordering and self-updating mechanisms. The compression scheme identifies and merges duplicate entries into a single entry, reducing the overall index size while maintaining query performance through the self-update mechanism that keeps the index current.
Solution Approach 2:
The patent performs preliminary reordering of index entries before compression. By pre-sorting and organizing entries, the system prepares the index structure to facilitate efficient duplicate detection and merging during the compression process, reducing the need for complex operations during actual compression.
2Manufacturing precision
If manual normalization is performed, then data quality improves, but processing time and complexity increase
Solution Approach 1:
The patent implements a self-update mechanism where the index structure automatically maintains itself during compression. The self-update process eliminates the need for manual normalization operations by having the system automatically identify and process duplicates, reducing processing time while maintaining data quality.
Solution Approach 2:
The patent replaces manual normalization operations with an automated compression algorithm. Instead of requiring human intervention or complex manual processes to normalize data, the system uses computational algorithms to automatically identify, reorder, and merge duplicate entries, significantly reducing processing time.
3Productivity
If compression is applied to unnormalized data, then compression speed is maintained, but compression ratio decreases
Solution Approach 1:
The patent performs preliminary reordering of index entries before compression. This pre-processing step organizes the data in a way that facilitates efficient duplicate detection and merging during compression, enabling the system to achieve high compression ratios while maintaining fast compression speeds.
Solution Approach 2:
The patent uses a dynamic compression approach where the compression algorithm adapts to the data structure during the compression process. The self-update mechanism dynamically adjusts the index structure during compression, allowing the system to achieve optimal compression ratios without significantly reducing compression speed.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for compression operation combining duplicate index entries independent of a data model. One example method includes operations to identify an update to at least one entry in a compressed index that includes a plurality of entries, each associated with a unique entry ID. An entry ID of each entry associated with the update are identified. A self-update is performed for each entry not associated with the entry IDs associated with the update, which comprises inserting a value associated with those non-updated entries to an uncompressed index in connection with that entry's corresponding entry ID. For each entry associated with the update, a particular update value from the identified update is inserted into the uncompressed index associated with the particular entry ID. When completed, the uncompressed index is compressed into a new version of the compressed index.