Video Viewing Data Translation for Efficient MapReduce Analytics
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Solution Overview
Problem
Existing methods for aggregating video viewing activity data using the MapReduce Framework consume excessive computing resources without adding commensurate value, necessitating a more efficient data translation strategy to reduce workload and enhance analytical studies.
Innovation Solution
Implementing a data translation processor that translates detailed video viewing activity data into aggregated values using analyst-defined rules, preparing the data for efficient ingestion by the MapReduce Framework, thereby reducing the data volume processed and enabling a single file to be used for multiple analytical studies.
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 excessive computing resources are consumed
Solution Approach 1:
The patent applies preliminary action by performing data translation and aggregation operations before the MapReduce processing stage. The system translates detailed viewing activity data into aggregated values in advance, reducing the data volume that needs to be processed by the computationally intensive MapReduce framework, thereby preserving measurement precision while reducing resource consumption
Solution Approach 2:
The patent extracts only the essential aggregated values from the detailed viewing activity data through translation rules, separating the critical measurement information from the redundant detailed data. This extraction process removes unnecessary data elements before MapReduce processing, maintaining analytical accuracy while significantly reducing computing resource requirements
2Measurement precision
If detailed video viewing activity data is processed by the MapReduce Framework, then comprehensive analytical studies can be created, but processing time increases
Solution Approach 1:
The system performs preliminary aggregation and translation of detailed viewing data into summarized values before MapReduce processing. This advance preparation reduces the computational burden during the actual analytical study execution, maintaining measurement precision while significantly reducing processing time
Solution Approach 2:
The patent segments the data processing workflow into distinct stages: initial data translation and aggregation, followed by MapReduce processing. This segmentation allows the system to handle detailed data efficiently in the first stage, then process only the aggregated results in the second stage, reducing overall processing time while preserving analytical accuracy
3Adaptability or versatility
If multiple extract files with embedded translated values are created for different analytical studies, then each study can be optimized, but time and cost increase
Solution Approach 1:
The patent creates a universal translated data file that can serve multiple analytical studies with different requirements. The translation rules are designed to be adaptable and configurable, allowing the same translated file structure to support various analytical objectives without requiring separate extract files for each study, thus reducing time and cost while maintaining versatility
Solution Approach 2:
The system uses configurable translation rules that can be adjusted through parameter changes to accommodate different analytical study requirements. By modifying the translation parameters rather than creating entirely new extract files, the system achieves adaptability for multiple studies while significantly reducing the time and resources needed for file creation
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.