Intent-Aware Sampling Agent for Low-Latency Data Exploration
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Solution Overview
Problem
Exploratory data analytics (EDA) systems face challenges in running queries against large datasets due to latency issues, leading to errors and diversion from the intended analysis path when using samples instead of full datasets, as the best sampling technique depends on the query structure, context, and data distribution.
Innovation Solution
An intent-aware sampling agent module using reinforcement learning, such as deep reinforcement learning, is employed to automatically select appropriate samples based on implicit user intents, latency, and termination characteristics, ensuring low-latency query responses that preserve the analyst's intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If queries are run against samples instead of full datasets, then query processing speed is improved, but analysis accuracy deteriorates due to sampling errors
Solution Approach 1:
The system dynamically adapts the sampling strategy based on the query sequence and identified user intent. The sampling configuration is not fixed but changes in response to the analytical context, allowing the system to optimize between speed and accuracy for different types of queries within the same session.
Solution Approach 2:
The system changes sampling parameters (such as sample size, sampling method, or stratification) based on the detected intent and query characteristics. By adjusting these parameters dynamically, the system can maintain analysis accuracy while achieving faster processing speeds when appropriate.
2Loss of time
If sampling is used to reduce latency, then response time is improved, but cognitive flow is hampered due to errors in query responses
Solution Approach 1:
The system incorporates feedback loops where query responses are evaluated for accuracy and reliability. When sampling errors are detected or when the analytical context suggests higher precision is needed, the system can adjust subsequent sampling strategies or even switch to processing against fuller datasets to maintain cognitive flow.
Solution Approach 2:
The system performs preliminary analysis of the query sequence to identify user intent before executing queries. This preliminary action allows the system to pre-determine appropriate sampling strategies that will maintain accuracy for the anticipated analytical path, preventing cognitive disruptions before they occur.
3Productivity
If automated sample selection is implemented, then productivity is improved, but system complexity increases due to intent recognition requirements
Solution Approach 1:
The system introduces an intent recognition module as an intermediary between the user's query sequence and the sample selection mechanism. This intermediary translates complex analytical patterns into simplified intent categories, which then guide sample selection. This layered approach manages complexity by breaking down the decision-making process into manageable stages.
Data Source
AI summary
Some techniques described herein relate to utilizing a machine-learning (ML) model to select respective samples for queries of a query sequence. In one example, a method includes receiving a query in a query sequence, where the query is directed toward a dataset. Samples are available as down-sampled versions of the dataset. The method further include applying an agent to select, for the query, a sample from among the samples of the dataset. The agent includes an ML model trained, such as via intent-based reinforcement learning, to select respective samples for queries. The query is then executed against the sample to output a response.