ML-Based Data Feed Version Routing for Query Optimization
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Solution Overview
Problem
Large datasets are difficult to query efficiently due to varying levels of accuracy and computational costs, as conventional methods struggle to optimize query execution time and cost effectively, especially in contexts like network security analysis where exploratory queries require quick results but may need high accuracy later.
Innovation Solution
The system employs machine learning techniques to generate and train models that automatically select versions of data feeds, ranging from full to sampled or aggregated data, to optimize query execution speed and cost while maintaining appropriate accuracy, using a routing model to determine the best data feed version for each query and a checking model to ensure result quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full data feeds are used for queries, then accuracy is improved, but query execution time and computing costs increase
Solution Approach 1:
The system dynamically selects different data feed versions (full, sampled, aggregated, sketch) based on query characteristics and user needs. The routing model adjusts the data feed version dynamically to match the required accuracy level, resolving the contradiction between accuracy and execution time by making the data processing approach adaptive rather than static
Solution Approach 2:
The system changes the data feed parameters (completeness, detail level, processing intensity) based on query requirements. By varying parameters such as sampling rate, aggregation granularity, and data sketch precision, the system achieves different accuracy levels while optimizing execution time and computational cost
2Measurement precision
If full data feeds are used for queries, then accuracy is improved, but computing costs increase
Solution Approach 1:
The routing model dynamically adjusts data feed version selection based on query characteristics, ensuring that computational resources are allocated efficiently. By matching the data feed version to the actual query requirements, the system avoids unnecessary computational expenditure while maintaining adequate accuracy
Solution Approach 2:
The system varies data feed parameters (sample rate, aggregation level, sketch precision) to optimize the balance between computational cost and query accuracy. This parameter adjustment allows the system to provide sufficient accuracy for most queries at lower computational cost
3Reliability
If exploratory queries are executed with high accuracy, then result reliability is improved, but query execution time increases
Solution Approach 1:
The system applies different data feed versions to different query types dynamically. Exploratory queries can use lighter versions (sampled, aggregated) for quick insights, while analytical queries requiring high reliability use full data feeds, resolving the contradiction through adaptive query handling
Solution Approach 2:
For exploratory queries, the system applies partial action by using sampled or aggregated data feeds that provide sufficient reliability for hypothesis generation without the full computational cost of processing complete data, allowing researchers to explore data characteristics efficiently
Data Source
AI summary
Systems and methods for querying large amounts of data are disclosed. Several different versions of a data feed are provided, ranging from a full set of data to various other versions that are smaller or faster to query (e.g., sampled versions, aggregations, sketches) . . . . A machine learning model is trained on features of input queries run against the various versions of the data feed and the corresponding results. The trained model is then applied to a new query to choose, automatically, which version of the data feed to apply the query against. That is, the system can select which version of the data feed to use when executing the given query, optimizing speed and/or compute costs while providing an appropriate level of accuracy for the given query.