Set Top Box Data Cleaning for Buffer Overflow and Clock Offset Correction
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Solution Overview
Problem
The collection and analysis of television viewing behavior data from set-top boxes (STBs) face challenges due to memory buffer overflows, inclusion of non-human generated tuning events, and clock offsets, which affect the precision and reliability of the data, exacerbated by the lack of standardization across different STB models and firmware versions.
Innovation Solution
A system and method for cleaning television viewing data that includes modules for detecting and correcting memory buffer overflows, non-human generated tuning events, and clock offsets, using a central processing unit and storage device to execute data collection, cleaning, and analysis modules, which utilize algorithms to identify and correct these issues, ensuring accurate and reliable data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If viewing behavior data is collected from diverse STB models, then data coverage is improved, but data precision deteriorates due to lack of standardization
Solution Approach 1:
A data cleaning module is introduced as an intermediary between the diverse STB data sources and the analysis system. This module receives raw viewing behavior data from multiple STB models and formats, detects and corrects buffer overflow issues, and standardizes the data into a consistent format, thereby enabling both broad data coverage and high measurement precision simultaneously
2Ease of operation
If STB data is collected without cleaning, then data collection simplicity is improved, but reliability deteriorates due to buffer overflows and non-human events
Solution Approach 1:
The data cleaning module performs preliminary actions on the raw STB data before it enters the analysis system. It detects buffer overflow conditions, identifies non-human generated tuning events, and corrects these issues in advance, ensuring that only reliable and valid data is available for subsequent analysis while maintaining simple data collection procedures
3Adaptability or versatility
If diverse STB models are supported, then system adaptability is improved, but device complexity increases due to varying reporting formats
Solution Approach 1:
The data cleaning module serves as a universal intermediary that handles the complexity of diverse STB reporting formats. It detects the specific format of incoming data from different STB models and applies appropriate cleaning and normalization rules, allowing the system to maintain high adaptability to various STB models while keeping the overall system architecture manageable and not excessively complex
Data Source
AI summary
A system and method for cleaning television viewing behavior data collected from set top boxes by detecting and correcting various problems that can occur in the viewing data. Three problems that may be detected and corrected by the system include: overflows of memory buffers; inclusion of non-human generated tuning events; and presence of clock offsets. After cleaning the television viewing behavior data, the cleaned data may be used to analyze audience viewing behavior in a manner that achieves a higher degree of accuracy than can be achieved by using uncleaned television viewing behavior data.