Graph-Based Compression of Impression Records for Faster Retrieval
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Solution Overview
Problem
Current methods for storing and accessing large amounts of impression data, such as those collected by promotion and marketing services, are inefficient and ineffective, leading to challenges in storage capacity and data access speed.
Innovation Solution
The implementation of webgraph-based techniques for compressing data records, which generates a compressed list of impression data records by creating a directed link graph and using properties like locality and similarity to encode duplicate values, allowing for multi-dimensional compression and efficient storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current storage methods are used for impression data, then data can be stored, but storage efficiency is poor and storage capacity is insufficient
Solution Approach 1:
The patent segments the impression data into multiple dimensions (user ID, device ID, ad ID, timestamp, etc.) and creates separate sorted lists for each dimension. This segmentation allows independent compression of each dimension using graph-based techniques, improving overall storage efficiency while maintaining the ability to reconstruct the complete dataset.
Solution Approach 2:
The patent transforms the one-dimensional storage approach into a multi-dimensional approach by creating sorted lists across multiple dimensions (user dimension, device dimension, ad dimension, time dimension). This dimensional transformation enables graph-based compression to be applied more effectively, reducing the overall data size while preserving query capabilities.
2Speed
If large amounts of data are stored, then data completeness is maintained, but data access speed decreases
Solution Approach 1:
The patent performs preliminary sorting of data across multiple dimensions before compression. By pre-sorting the data and identifying patterns in advance, the system enables faster query processing later, as the compressed structure preserves the sorted order and allows for efficient range queries and pattern matching without full decompression.
Solution Approach 2:
The patent creates compressed representations (graphs) that capture the essential patterns and relationships in the data without storing every individual record. These graph structures serve as efficient copies that can be queried rapidly, providing fast access to aggregate information and patterns while the full detailed data can be retrieved only when necessary.
3Productivity
If graph-based compression is applied, then storage efficiency improves and data size reduces, but system complexity increases
Solution Approach 1:
The patent changes the representation parameters of the data by transforming raw impression records into graph-based structures with specific properties (nodes representing unique values, edges representing co-occurrences). This parameter transformation enables more efficient storage while the graph operations provide a systematic way to handle queries, balancing the increased structural complexity with improved operational efficiency.
Data Source
AI summary
In general, embodiments of the present invention provide systems, methods and computer readable media for data record compression using graph-based techniques.