Natural Language Data Sampling for Low-Latency Query Exploration
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Solution Overview
Problem
Existing EDA systems face challenges in running queries against large datasets due to latency issues and approximation errors introduced by sampling, which can divert the analysis flow and mislead users.
Innovation Solution
A query system combining NLP interface with a sample-based EDA system that uses a sampling machine learning model to select the most optimum sample for each query, preserving the user's intent through reinforcement learning, and provides interactive query recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If queries are run against the full dataset, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system applies partial action by executing queries on sampled subsets of the full dataset rather than the complete dataset. Multiple samples are generated and queried in parallel, with results aggregated to provide approximate answers that achieve acceptable precision while dramatically reducing processing time compared to full dataset queries.
Solution Approach 2:
The system performs preliminary action by pre-generating multiple samples of the dataset before query execution. These samples are prepared in advance and can be immediately used for query processing, eliminating the need to process the entire dataset at query time and thus reducing latency.
2Loss of time
If samples are used to reduce latency, then loss of time is reduced, but measurement precision deteriorates
Solution Approach 1:
The system merges results from multiple independent sample queries to produce a final answer. By combining insights from several samples rather than relying on a single sample, the system achieves higher measurement precision while maintaining the time efficiency benefits of sampling.
Solution Approach 2:
The system implements feedback by using the results from sample queries to refine and adjust subsequent sampling strategies. The aggregation and comparison of sample results provide feedback that helps improve the accuracy of approximate queries while maintaining reduced processing time.
3Productivity
If sampling is used to speed up analysis, then productivity is improved, but reliability deteriorates due to errors introduced by sampling
Solution Approach 1:
The system performs partial action by using multiple samples instead of one, and by using a portion of the full dataset distributed across samples. This approach maintains productivity through faster sample-based processing while improving reliability through result aggregation that compensates for individual sample errors.
Solution Approach 2:
The system applies beforehand cushioning by generating multiple samples in advance, each serving as a buffer or cushion against the limitations of any single sample. This pre-prepared diversity of samples protects against sampling errors and improves overall analysis reliability while maintaining high productivity.
Data Source
AI summary
In various examples, an exploratory data analytics tool obtains a natural language query and generates a structured data query for execution on a sample of a dataset based on the natural language query. In an example, an intent is determined for the query and the intent is used, at least in part, to determine the most appropriate sample. In addition, the intent, in some examples, is used to generate recommended queries. A user interface of the exploratory data analytics tool, for example, can display the recommended queries and/or the results of the structured data query on the sample.