Multi-dimensional Array Compression for Subscriber Event Data
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Solution Overview
Problem
Conventional data compression techniques require significant resources for processing and storage, and often result in loss of data when decompressing content consumption information in cable network environments, making it inefficient for managing large volumes of subscriber tuning data.
Innovation Solution
A system that converts content consumption information into a multi-dimensional information array, applying loss-less compression algorithms to reduce storage requirements, and using offset information for quick retrieval of compressed data, allowing efficient processing of queries regarding subscriber content consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression techniques are applied to content consumption information, then data storage requirements are reduced, but data loss occurs during decompression
Solution Approach 1:
The patent transforms event data from its original format into a multi-dimensional array structure with specific parameters (subscribers dimension, time dimension, channels dimension). This structural parameter change enables application of lossless compression algorithms that exploit the regularities and redundancies in the transformed data format, achieving both compression and data preservation.
2Quantity of substance
If conventional compression algorithms are used on content consumption information, then storage capacity is reduced, but processing resources and time are significantly increased
Solution Approach 1:
The patent performs preliminary transformation of event data into a multi-dimensional array format before compression. This preliminary action organizes the data in a structure that reveals patterns and redundancies, making the subsequent compression process more efficient and reducing the computational resources needed compared to applying compression directly to raw event data.
Solution Approach 2:
By changing the data structure parameters to a multi-dimensional array format, the patent creates a representation that is more amenable to efficient compression. The structured format allows compression algorithms to work more effectively, reducing both storage requirements and processing overhead.
3Measurement precision
If detailed tuning information for multiple subscribers is tracked, then query accuracy is improved, but data management complexity increases
Solution Approach 1:
The patent transforms flat event data into a multi-dimensional array with explicit dimensions for subscribers, time, and channels. This dimensional transformation organizes the complexity into structured axes, making it easier to manage and query while preserving detailed information about subscriber tuning behavior across multiple dimensions.
Solution Approach 2:
The multi-dimensional array structure serves multiple functions simultaneously: it preserves detailed tuning information for accurate queries, enables efficient compression through pattern recognition, and provides a unified framework for various types of content consumption analysis. This universal structure reduces overall data management complexity despite the detailed information retained.
Data Source
AI summary
A data management resource receives event log data indicating content consumption by multiple subscribers in a network environment. The data management resource converts the event data into bit strings stored in a multi-dimensional information array. One dimension of the multi-dimensional information array represents the multiple subscribers. Another dimension of the multi-dimensional information array represents time. The bit strings stored in the multi-dimensional information array indicate at what time, if any, a respective subscriber tunes to consume particular. The data compression resource stores a compressed rendition of the multi-dimensional array information in a repository for subsequent application of queries.