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Conference Paper
A Framework for Learned Approximate Query Processing for Tabular Data with Trajectory
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- Authors
- Kihyuk Nam, Sung-Soo Kim, Choon Seo Park, Taek Yong Nam, Taewhi Lee
- Issue Date
- 2023-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1122-1124
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Approximate query processing has been well established for enhancing performance of aggregation queries on ever-increasing big data by statistically equivalent approximations. Recent popularity of mobile devices creates tremendous spatio-temporal data that require different treatment than relational ones. Among spatiotemporal data, we focus on trajectories in a tabular form and analyzes the problem, its requirements, and suggest a general-purpose framework for learned approximate query processing by providing a common encoding/embedding layer for embracing diverse state-ofthe-art ML models, on top of which resides a probabilistic circuit for efficiency and efficacy with error bounds.
- KSP Keywords
- Approximate query processing, Big Data, Embedding Layer, Error bound, Mobile devices, Spatiotemporal data, Tabular Data