공개 SW 활용

Part 3.

Development of Approximate DBMS Query Technology to Facilitate Fast Query Processing for Exploratory Data Analysis
(빅데이터 대상의 빠른 질의 처리가 가능한 탐사 데이터 분석 지원 근사질의 DBMS 기술 개발)
Choo-choo!
Please take a ride on TrainDB!
Toward Data-Less Big Data Analytics
Homepage
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GitHub
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TrainDB
User Guide
ENG KOR
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About TrainDB Project
TrainDB is a ML-based approximate query processing engine that aims to answer time-consuming analytical queries in a few seconds.
TrainDB will provide SQL-like query interface and support various DBMS data sources.
TrainDB is an open source project, mainly contributed by ETRI, RealTimeTech, BI Matrix, BigDyL in Yonsei University, and BigComLab in Kwangwoon University.
Features of TrainDB Project
  • 01
    Various Types of Approximate Queries

    10+ analytic/aggregate operations support

    Complex aggregate queries support (including groups and joins)

    Approximate query support for tabular/spatiotemporal data

    Query processing with a given error-bound or response time

    What-if queries on non-existing data with adjusted data distribution

  • 02
    Convenient User Environment

    Data-driven ML model approach which can be trained without query history

    Various DBMS data sources support

    Approximate query answering in cloud/portable environments even with lost connection to DBMS

    Incremental query processing for interactive user experience

  • 03
    Powerful Tools and Application Services

    Visualization tools for exploratory data analysis

    Target tables/attributes recommendation for automated ML model training

    ML model migration support

    Application services for demonstration in various business fields

This project is supported by IITP grant funded by the Korea government(MSIT) (No.2021-0-00231, Development of Approximate DBMS
Query Technology to Facilitate Fast Query Processing for Exploratory Data Analysis)