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Zhao Tan
Griffith University
Y
Yiji Zhao
Yunnan University
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Founder's Pitch
"Neuro-symbolic framework for intuitive natural language querying of time series databases with significant scalability improvements."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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4/4 signals
Series A Potential
2/4 signals
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arXiv Paper
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Why It Matters
This research addresses a critical usability gap in accessing insights from time series databases which are extensive in various industries. Without such advancements, non-expert users face significant difficulty in extracting meaningful patterns and trends from large datasets.
Product Angle
The product could be a user-friendly dashboard or API that interprets and processes natural language queries into data insights, suitable for firms managing large amounts of time-series data and lacking the resources to handle specialized querying internally.
Disruption
It could replace existing SQL querying methods and text-to-SQL tools that are not optimized for handling time-series data, especially for non-experts.
Product Opportunity
The market for time series databases is rapidly growing across sectors such as finance, IoT, and energy. Companies in these sectors have large datasets and a demand for intuitive data querying tools; they would pay for tools that make this process efficient and accessible.
Use Case Idea
Develop a service that allows operations teams in industries like finance or IoT to input complex natural language queries and receive insights from their time-series databases without needing SQL expertise.
Science
Sonar-TS proposes a 'Search-Then-Verify' pipeline for querying time series databases using natural language. It employs SQL to identify candidate data windows and uses Python programs to further verify these against user queries, bypassing the limitations of existing Text-to-SQL and time series models that struggle with complex morphological intents and large datasets.
Method & Eval
Sonar-TS was tested using the NLQTSBench, a benchmarking suite that includes four levels of complexity to evaluate querying precision, pattern recognition, semantic reasoning, and insight synthesis. It outperforms traditional methods by effectively navigating complex queries over large, segmented time series data.
Caveats
The framework could face challenges in real-world usability if the natural language processing aspect does not accurately interpret user intents. Additionally, initial adoption might be slow if users are accustomed to traditional querying methods.
Author Intelligence
Shirui Pan
LEAD Griffith University
s.pan@griffith.edu.au
Ming Jin
LEAD Griffith University
ming.jin@griffith.edu.au
Zhao Tan
Griffith University
Yiji Zhao
Yunnan University
Shiyu Wang
Chang Xu
Microsoft Research Asia
Yuxuan Liang
The Hong Kong University of Science and Technology (Guangzhou)
Xiping Liu
Jiangxi University of Finance and Economics