Forecast Query Matching and Queueing for Scalable Campaign Modeling
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Solution Overview
Problem
Existing systems struggle to efficiently forecast and distribute digital components over a network due to the dynamic and global nature of the Internet, making it difficult to model and predict how digital components will be provided to users.
Innovation Solution
A forecasting system that utilizes enhanced data storage, processing, and queuing techniques to efficiently allocate futurized queries to digital campaigns, decoupling matching and allocation processes, and using a high-performance queue system to manage matching data, allowing for faster and more accurate forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used to forecast digital component distribution, then the system can handle basic forecasting requests, but the system cannot scale efficiently to handle large numbers of requests and experiences high computational overhead
Solution Approach 1:
The system segments the forecasting process into distinct modules: a simulation engine that generates synthetic user queries, a matching computer that identifies relevant digital components, and an allocation computer that distributes components to users. This segmentation allows each module to be optimized independently and processed in parallel, improving forecasting speed while managing system complexity through modular design.
Solution Approach 2:
The system performs preliminary actions by pre-generating large sets of synthetic user queries offline using the simulation engine, storing them in a query pool before actual forecasting requests arrive. This preliminary preparation eliminates the need to generate queries in real-time during forecasting operations, significantly improving forecasting speed and scalability.
2Measurement precision
If the system processes all user queries in real-time to ensure accurate forecasting, then forecasting accuracy is maintained, but the system cannot handle dynamic campaigns and frequent changes efficiently
Solution Approach 1:
The system implements dynamics by allowing digital campaign parameters to be updated in real-time without requiring complete reprocessing of the query pool. The matching computer can efficiently retrieve and re-evaluate queries against updated campaign parameters, enabling the system to adapt to frequent changes while maintaining forecasting accuracy through selective reprocessing rather than full reprocessing.
3Measurement precision
If the forecasting system uses detailed matching criteria to ensure precise component allocation, then allocation accuracy improves, but the computational time and resources required increase significantly
Solution Approach 1:
The matching computer performs preliminary filtering of the synthetic query pool based on basic campaign criteria before applying detailed matching criteria. This two-stage approach eliminates obviously irrelevant queries early in the process, allowing the system to apply computationally intensive detailed matching only to a reduced subset of relevant queries, thereby maintaining matching precision while reducing overall processing time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improved forecasting of the distribution of digital components that may be provided over a network as a response to an electronic request for certain types of content. In some aspects, a method includes receiving a request comprising distribution parameters for a given digital campaign. Futurized queries are obtained from historical serving logs of a serving system that is configured to provide existing digital components using serving code. Each digital campaign of multiple digital campaigns is assigned to one of multiple matching computers. Each assigned matching computer identifies one or more of the futurized queries that match the digital campaign assigned to the matching computer. Matching data indicating the one or more futurized queries can be stored in a file system. A queue entry for each instance of matching data is added to a queue.