Forecasting Engine for Boolean Expression Evaluation Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Processing and evaluating large Boolean expressions that query vast datasets in online advertising becomes costly due to increased evaluation response times, particularly when dealing with complex targeting criteria and large data sets, leading to unacceptable latency in communication between application interfaces and worker nodes.
Innovation Solution
A forecasting engine system that includes a modification detector to identify modified Boolean expressions, an expression updater to transmit only modified expressions to a forecasting cluster for evaluation, and a dynamic forecast cloud to process these expressions efficiently, reducing the need to transmit all data and minimizing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If all Boolean expressions are transmitted to the forecasting cluster for evaluation, then evaluation completeness is maintained, but communication latency and processing time increase significantly
Solution Approach 1:
The system extracts only the modified portions of Boolean expressions (changes in targeting elements, criteria, or parameters) and transmits only these differences to the forecasting cluster for evaluation, rather than transmitting complete expressions. This extraction approach maintains evaluation accuracy while dramatically reducing communication overhead and latency.
Solution Approach 2:
The Boolean expression processing is segmented into identification of modified elements, extraction of changes, and selective transmission. The system divides the evaluation process into stages: detecting what changed, isolating only those changes, and transmitting only the necessary modifications to the forecasting cluster for targeted re-evaluation.
2Loss of information
If complete Boolean expressions are processed, then evaluation accuracy is maintained, but communication bandwidth consumption increases
Solution Approach 1:
The system extracts and transmits only the modified elements of Boolean expressions (such as changed targeting demographics, technographics, or campaign parameters) rather than transmitting complete expressions. This ensures that the forecasting cluster receives sufficient information to maintain evaluation accuracy while dramatically reducing the volume of data transmitted across communication channels.
3Reliability
If frequent expression updates are transmitted, then forecasting accuracy is maintained, but system load and processing overhead increase
Solution Approach 1:
The system performs preliminary detection and identification of modified elements in Boolean expressions before transmission to the forecasting cluster. By pre-processing expressions to identify what has changed (using metadata, version tracking, or change detection mechanisms), the system prepares only the necessary modifications for transmission, reducing unnecessary processing overhead while maintaining forecasting accuracy.
Solution Approach 2:
The system applies partial action by transmitting only the portion of expression updates that are necessary (the modified elements) rather than complete re-transmissions. This partial transmission approach maintains forecasting accuracy by sending sufficient information about changes while avoiding the excessive processing load of transmitting or re-evaluating entire expressions when only minor modifications occurred.
Data Source
AI summary
The present teaching relates to method, system, and programming for evaluating expressions. An expression indicative of conditions and metadata associated therewith is obtained. A determination is made as to whether the expression corresponds to a modified version of an earlier expression based on the metadata. In response to a determination that the expression is the modified version of the earlier expression, a query associated with the modified expression is transmitted to a forecasting cluster so that the modified expression is to be evaluated by the forecasting cluster. In response to a determination that the expression does not have a corresponding earlier expression, the expression is evaluated.