Recursive SQL for Data Mining

Maximilian Emanuel Schüle; Alfons Kemper; Thomas Neumann

doi:10.1145/3538712.3538746

Recursive SQL for Data Mining

Maximilian Emanuel Schüle, Alfons Kemper, Thomas Neumann

Technical University of Munich

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

Abstract

To implement algorithms within database systems beyond the design of SQL as a data query language, library functions or external tools were used that require the extraction of data first. To eliminate the need of data extraction out of database systems, we argue that SQL-92 plus recursive tables is capable of expressing user-defined algorithms. To underline this claim, we transform selected algorithms out of graph mining, clustering and association rule analysis into recursive common table expressions (CTEs). We compare their performance to the one of user-defined functions and external tools. Our evaluation shows a competitive performance when using recursive CTEs to library functions either when using a disk-based database systems or a modern in-memory engine.

Original language	English
Title of host publication	Scientific and Statistical Database Management - 34th International Conference, SSDBM 2022 - Proceedings
Editors	Elaheh Pourabbas, Yongluan Zhou, Yuchen Li, Bin Yang
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450396677
DOIs	https://doi.org/10.1145/3538712.3538746
State	Published - 6 Jul 2022
Event	34th International Conference on Scientific and Statistical Database Management, SSDBM 2022 - Copenhagen, Denmark Duration: 6 Jul 2022 → 8 Jul 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	34th International Conference on Scientific and Statistical Database Management, SSDBM 2022
Country/Territory	Denmark
City	Copenhagen
Period	6/07/22 → 8/07/22

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10.1145/3538712.3538746

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Schüle, M. E., Kemper, A., & Neumann, T. (2022). Recursive SQL for Data Mining. In E. Pourabbas, Y. Zhou, Y. Li, & B. Yang (Eds.), Scientific and Statistical Database Management - 34th International Conference, SSDBM 2022 - Proceedings Article 21 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3538712.3538746

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title = "Recursive SQL for Data Mining",

abstract = "To implement algorithms within database systems beyond the design of SQL as a data query language, library functions or external tools were used that require the extraction of data first. To eliminate the need of data extraction out of database systems, we argue that SQL-92 plus recursive tables is capable of expressing user-defined algorithms. To underline this claim, we transform selected algorithms out of graph mining, clustering and association rule analysis into recursive common table expressions (CTEs). We compare their performance to the one of user-defined functions and external tools. Our evaluation shows a competitive performance when using recursive CTEs to library functions either when using a disk-based database systems or a modern in-memory engine.",

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Schüle, ME, Kemper, A & Neumann, T 2022, Recursive SQL for Data Mining. in E Pourabbas, Y Zhou, Y Li & B Yang (eds), Scientific and Statistical Database Management - 34th International Conference, SSDBM 2022 - Proceedings., 21, ACM International Conference Proceeding Series, Association for Computing Machinery, 34th International Conference on Scientific and Statistical Database Management, SSDBM 2022, Copenhagen, Denmark, 6/07/22. https://doi.org/10.1145/3538712.3538746

Recursive SQL for Data Mining. / Schüle, Maximilian Emanuel; Kemper, Alfons ; Neumann, Thomas.
Scientific and Statistical Database Management - 34th International Conference, SSDBM 2022 - Proceedings. ed. / Elaheh Pourabbas; Yongluan Zhou; Yuchen Li; Bin Yang. Association for Computing Machinery, 2022. 21 (ACM International Conference Proceeding Series).

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

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T1 - Recursive SQL for Data Mining

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PY - 2022/7/6

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N2 - To implement algorithms within database systems beyond the design of SQL as a data query language, library functions or external tools were used that require the extraction of data first. To eliminate the need of data extraction out of database systems, we argue that SQL-92 plus recursive tables is capable of expressing user-defined algorithms. To underline this claim, we transform selected algorithms out of graph mining, clustering and association rule analysis into recursive common table expressions (CTEs). We compare their performance to the one of user-defined functions and external tools. Our evaluation shows a competitive performance when using recursive CTEs to library functions either when using a disk-based database systems or a modern in-memory engine.

AB - To implement algorithms within database systems beyond the design of SQL as a data query language, library functions or external tools were used that require the extraction of data first. To eliminate the need of data extraction out of database systems, we argue that SQL-92 plus recursive tables is capable of expressing user-defined algorithms. To underline this claim, we transform selected algorithms out of graph mining, clustering and association rule analysis into recursive common table expressions (CTEs). We compare their performance to the one of user-defined functions and external tools. Our evaluation shows a competitive performance when using recursive CTEs to library functions either when using a disk-based database systems or a modern in-memory engine.

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Recursive SQL for Data Mining

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