Video Viewing Data Translation for MapReduce Aggregation
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Solution Overview
Problem
Existing methods for measuring audience viewership in television viewing are inefficient, consuming large computing resources without providing commensurate value, particularly when using the MapReduce Framework for aggregating video viewing activity.
Innovation Solution
Implementing a data translation processor that translates detailed video viewing activity data into aggregated values according to analyst-defined rules, reducing the data load for downstream processing within the MapReduce Framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed video viewing activity data is processed directly by the MapReduce Framework, then comprehensive analytical studies can be created, but computing resource consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by implementing a data translation processor that aggregates detailed video viewing activity data into summary data before it enters the MapReduce Framework. This pre-processing step consolidates detailed records into aggregated metrics, reducing the volume of data that requires computationally intensive processing while preserving the essential information needed for accurate viewership measurement.
2Productivity
If detailed values are translated to aggregated values before processing, then computing resources are reduced, but data processing time may increase due to the translation step
Solution Approach 1:
The translation processor performs aggregation in advance, converting detailed viewing activity data into summary data before the MapReduce processing stage. This preliminary consolidation reduces the overall computational burden and enables faster processing of the reduced data set, improving productivity while managing time through optimized translation operations.
3Loss of information
If the MapReduce Framework processes all detailed data, then complete analytical insights are obtained, but the system requires more hardware resources
Solution Approach 1:
The patent extracts and consolidates essential information from detailed video viewing activity data through the translation processor, which aggregates individual records into summary statistics. This extraction process separates the critical analytical insights from the voluminous detailed data, allowing the MapReduce Framework to process only the essential aggregated information, thereby reducing hardware resource requirements while maintaining analytical completeness.
4Adaptability or versatility
If multiple extract files are created for different analytical studies, then each study has optimized data, but the time and cost of data preparation increases
Solution Approach 1:
The translation processor creates a universal aggregated data structure that can serve multiple analytical study purposes. By translating detailed data into a standardized aggregated format that preserves essential viewing activity information, the system enables a single data preparation process to support various analytical investigations, eliminating the need to create separate extract files for each study and thereby reducing data preparation time and costs.
Data Source
AI summary
Methods, systems and apparatuses are described for using Linear, DVR, and VOD video viewing activity data for more efficient downstream processing to create analytical studies of second-by-second viewing activity for program, channel, house, device, viewer, demographic, and geographic attributes. Such attributes may be determined for one or more viewing devices. Attributes associated with different viewing devices may be replaced with a common substitute value. Video viewing metrics associated with a common substitute value may be determined.