A Framework for Learned Approximate Query Processing for Tabular Data with Trajectory

Conference Title: 2023 14th International Conference on Information and Communication Technology Convergence (ICTC)Conference Start Date: 2023, Oct. 11 Conference End Date: 2023, Oct. 13 Conference Location: Jeju Island, Korea, Republic ofApproximate query processing has been well established for en...

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Published inThe Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings
Main Authors Nam, Kihyuk, Sung-Soo, Kim, Choon Seo Park, Taek Yong Nam, Lee, Taewhi
Format Conference Proceeding
LanguageEnglish
Published Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.01.2023
Subjects
Big Data
Circuits
Queries
Query processing
Spatiotemporal data
Tables (data)
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Summary:Conference Title: 2023 14th International Conference on Information and Communication Technology Convergence (ICTC)Conference Start Date: 2023, Oct. 11 Conference End Date: 2023, Oct. 13 Conference Location: Jeju Island, Korea, Republic ofApproximate 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 spatio-temporal 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-of-the-art ML models, on top of which resides a probabilistic circuit for efficiency and efficacy with error bounds.
DOI:10.1109/ICTC58733.2023.10392323