Multi-SQL: An Automatic Multi-model Data Management System

Yu Yan; Hongzhi Wang; Yutong Wang; Zhixin Qi; Jian Ma; Chang Liu; Meng Gao; Hao Yan; Haoran Zhang; Ziming Shen

doi:10.1007/978-3-031-25201-3_36

Multi-SQL: An Automatic Multi-model Data Management System

Yu Yan
, Hongzhi Wang^*
, Yutong Wang
, Zhixin Qi
, Jian Ma
, Chang Liu
, Meng Gao
, Hao Yan
, Haoran Zhang
, Ziming Shen

^*Corresponding author for this work

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Nowadays, data in applications become diverse and large in scale. In order to meet the increasing demand for multi-model data management, multi-model databases have evolved into huge systems with many knobs. However, such a large system brings great challenges for users to efficiently tune. In extreme cases, it is difficult even for experienced DBAs to be the masters in multi-source engines and provide effective solutions for multi-model data. Meeting this challenge, we firstly propose an automatic multi-model data management system (Multi-SQL) based on machine learning. We design an intelligent middleware in which some artificial intelligence(AI) technologies are embedded, including automatic multi-model storage selection and automatic multi-model index recommendation methods. In this paper, we clarify the architecture, core techniques and key scenarios of Multi-SQL.

Original language	English
Title of host publication	Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings
Editors	Bohan Li, Chuanqi Tao, Lin Yue, Xuming Han, Diego Calvanese, Toshiyuki Amagasa
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	451-455
Number of pages	5
ISBN (Print)	9783031252006
DOIs	https://doi.org/10.1007/978-3-031-25201-3_36
State	Published - 2023
Event	6th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022 - Nanjing, China Duration: 25 Nov 2022 → 27 Nov 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13423 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022
Country/Territory	China
City	Nanjing
Period	25/11/22 → 27/11/22

Keywords

Artificial intelligence
Data management
Multi-model

Access to Document

10.1007/978-3-031-25201-3_36

Cite this

Yan, Y., Wang, H., Wang, Y., Qi, Z., Ma, J., Liu, C., Gao, M., Yan, H., Zhang, H., & Shen, Z. (2023). Multi-SQL: An Automatic Multi-model Data Management System. In B. Li, C. Tao, L. Yue, X. Han, D. Calvanese, & T. Amagasa (Eds.), Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings (pp. 451-455). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13423 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-25201-3_36

Yan, Yu ; Wang, Hongzhi ; Wang, Yutong et al. / Multi-SQL : An Automatic Multi-model Data Management System. Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings. editor / Bohan Li ; Chuanqi Tao ; Lin Yue ; Xuming Han ; Diego Calvanese ; Toshiyuki Amagasa. Springer Science and Business Media Deutschland GmbH, 2023. pp. 451-455 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Nowadays, data in applications become diverse and large in scale. In order to meet the increasing demand for multi-model data management, multi-model databases have evolved into huge systems with many knobs. However, such a large system brings great challenges for users to efficiently tune. In extreme cases, it is difficult even for experienced DBAs to be the masters in multi-source engines and provide effective solutions for multi-model data. Meeting this challenge, we firstly propose an automatic multi-model data management system (Multi-SQL) based on machine learning. We design an intelligent middleware in which some artificial intelligence(AI) technologies are embedded, including automatic multi-model storage selection and automatic multi-model index recommendation methods. In this paper, we clarify the architecture, core techniques and key scenarios of Multi-SQL.",

keywords = "Artificial intelligence, Data management, Multi-model",

author = "Yu Yan and Hongzhi Wang and Yutong Wang and Zhixin Qi and Jian Ma and Chang Liu and Meng Gao and Hao Yan and Haoran Zhang and Ziming Shen",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022 ; Conference date: 25-11-2022 Through 27-11-2022",

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doi = "10.1007/978-3-031-25201-3\_36",

language = "英语",

isbn = "9783031252006",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "451--455",

editor = "Bohan Li and Chuanqi Tao and Lin Yue and Xuming Han and Diego Calvanese and Toshiyuki Amagasa",

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Yan, Y, Wang, H, Wang, Y, Qi, Z, Ma, J, Liu, C, Gao, M, Yan, H, Zhang, H & Shen, Z 2023, Multi-SQL: An Automatic Multi-model Data Management System. in B Li, C Tao, L Yue, X Han, D Calvanese & T Amagasa (eds), Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13423 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 451-455, 6th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022, Nanjing, China, 25/11/22. https://doi.org/10.1007/978-3-031-25201-3_36

Multi-SQL: An Automatic Multi-model Data Management System. / Yan, Yu; Wang, Hongzhi; Wang, Yutong et al.
Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings. ed. / Bohan Li; Chuanqi Tao; Lin Yue; Xuming Han; Diego Calvanese; Toshiyuki Amagasa. Springer Science and Business Media Deutschland GmbH, 2023. p. 451-455 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13423 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-SQL

T2 - 6th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

AU - Yan, Yu

AU - Wang, Hongzhi

AU - Wang, Yutong

AU - Qi, Zhixin

AU - Ma, Jian

AU - Liu, Chang

AU - Gao, Meng

AU - Yan, Hao

AU - Zhang, Haoran

AU - Shen, Ziming

PY - 2023

Y1 - 2023

N2 - Nowadays, data in applications become diverse and large in scale. In order to meet the increasing demand for multi-model data management, multi-model databases have evolved into huge systems with many knobs. However, such a large system brings great challenges for users to efficiently tune. In extreme cases, it is difficult even for experienced DBAs to be the masters in multi-source engines and provide effective solutions for multi-model data. Meeting this challenge, we firstly propose an automatic multi-model data management system (Multi-SQL) based on machine learning. We design an intelligent middleware in which some artificial intelligence(AI) technologies are embedded, including automatic multi-model storage selection and automatic multi-model index recommendation methods. In this paper, we clarify the architecture, core techniques and key scenarios of Multi-SQL.

AB - Nowadays, data in applications become diverse and large in scale. In order to meet the increasing demand for multi-model data management, multi-model databases have evolved into huge systems with many knobs. However, such a large system brings great challenges for users to efficiently tune. In extreme cases, it is difficult even for experienced DBAs to be the masters in multi-source engines and provide effective solutions for multi-model data. Meeting this challenge, we firstly propose an automatic multi-model data management system (Multi-SQL) based on machine learning. We design an intelligent middleware in which some artificial intelligence(AI) technologies are embedded, including automatic multi-model storage selection and automatic multi-model index recommendation methods. In this paper, we clarify the architecture, core techniques and key scenarios of Multi-SQL.

KW - Artificial intelligence

KW - Data management

KW - Multi-model

UR - https://www.scopus.com/pages/publications/85151049014

U2 - 10.1007/978-3-031-25201-3_36

DO - 10.1007/978-3-031-25201-3_36

M3 - 会议稿件

AN - SCOPUS:85151049014

SN - 9783031252006

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 451

EP - 455

BT - Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings

A2 - Li, Bohan

A2 - Tao, Chuanqi

A2 - Yue, Lin

A2 - Han, Xuming

A2 - Calvanese, Diego

A2 - Amagasa, Toshiyuki

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 25 November 2022 through 27 November 2022

ER -

Yan Y, Wang H, Wang Y, Qi Z, Ma J, Liu C et al. Multi-SQL: An Automatic Multi-model Data Management System. In Li B, Tao C, Yue L, Han X, Calvanese D, Amagasa T, editors, Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 451-455. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-25201-3_36

Multi-SQL: An Automatic Multi-model Data Management System

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