Content Ingestion Duration Estimation via Historical Publisher Data
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Solution Overview
Problem
Software development lifecycles for games, applications, and media content are complex and time-sensitive, with varying task completion times among developers, leading to potential delays and slipped release dates that can result in reduced sales and lost productivity.
Innovation Solution
A content ingestion system generates duration estimates for tasks based on historical data from multiple publishers, adjusting for individual publisher performance and service level agreements (SLAs) to provide publishers with a schedule for content ingestion, including start and end dates, helping manage content submission and adherence to deadlines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual task scheduling is used without historical data analysis, then flexibility in task assignment is maintained, but task completion time varies and delays occur
Solution Approach 1:
The system performs preliminary analysis of historical transaction data to generate duration estimates for tasks before actual content ingestion occurs. By pre-calculating expected task durations based on publisher performance patterns, the system enables better scheduling decisions and reduces task completion time variability without adding operational complexity during content submission
Solution Approach 2:
The system incorporates feedback loops where actual task completion data from previous content submissions is continuously analyzed and used to refine duration estimates. This feedback mechanism allows the scheduling system to adapt to changing patterns while maintaining accuracy, resolving the contradiction between time efficiency and system complexity
2Productivity
If uniform duration estimates are applied to all publishers, then scheduling simplicity is maintained, but individual publisher performance variations cause delays
Solution Approach 1:
The system applies local quality by generating customized duration estimates for each publisher based on their individual historical performance data. Instead of using a uniform estimation approach, the system tailors predictions to each publisher's specific patterns, thereby improving content ingestion efficiency while the automated nature of the system keeps complexity manageable
Solution Approach 2:
The system dynamically adjusts duration estimate parameters based on publisher-specific historical data, content type, and task characteristics. By changing the estimation parameters adaptively rather than using fixed values, the system improves productivity while the parameterization approach keeps the underlying system relatively simple
3Measurement precision
If detailed task breakdowns are provided to publishers, then scheduling precision is improved, but information complexity increases
Solution Approach 1:
The system segments the content ingestion process into distinct tasks with individual duration estimates, providing publishers with detailed scheduling information. This segmentation improves schedule accuracy by breaking down complex processes into measurable components while organizing information in a structured, manageable format that reduces the information processing burden
Data Source
AI summary
Embodiments establish duration estimates for tasks associated with media content ingestion, such as in application or game production. A content ingestion system receives a content submission request from a publisher. The request identifies a media content type that has a plurality of associated tasks. Based on previous submissions from the publisher, duration estimates for the tasks are generated and adjusted based on historical transaction data associated with other publishers (e.g., global averages). The duration estimates are provided to the publishers along with, for example, start and end dates for the tasks presented on a calendar or other workback schedule.