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In-database analytics with ibmdbpy

Published: 09 July 2018 Publication History

Abstract

The increasing size of the available data and database volumes represents a real challenge for the data management community. In general, current approaches in data mining require the data to be first extracted from an underlying database. From a practical point of view, this presents many drawbacks. In this short article, we present a possible solution to bridge the gap between data repositories and end user analysis. We demonstrate the interestingness of this approach with ibmdbpy, an open source Python interface developed by IBM for database administration and data analytics.

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Continuum Analytics. 2018. The Blaze Ecosystem. http://blaze.pydata.org.
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Inc. Cloudera. 2018. Ibis Project Blog. http://www.ibis-project.org.
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IBM Corporation. 2018. ibmdbpy 0.1.4. https://pypi.python.org/pypi/ibmdbpy.
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Thomas M. Cover and Joy A. Thomas. 2006. Elements of Information Theory. Wiley-Interscience, New York, NY, USA.
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Dua Dheeru and Efi Karra Taniskidou. 2017. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml
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Franz Färber, Norman May, Wolfgang Lehner, Philipp Große, Ingo Müller, Hannes Rauhe, and Jonathan Dees. 2012. The SAP HANA Database - An Architecture Overview. IEEE Data Eng. Bull. 35, 1 (2012), 28--33.
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Pablo Tamayo, C Berger, Marcos Campos, Joseph Yarmus, Boriana Milenova, A Mozes, M Taft, Mark Hornick, R Krishnan, S Thomas, M Kelly, D Mukhin, B Haberstroh, Susie Stephens, and J Myczkowski. 2005. Oracle Data Mining. In Data mining and knowledge discovery handbook. 1315--1329.

Cited By

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  • (2024)SQL Query Recommendation Based on Matrix FactorizationInnovations in Computational Intelligence and Computer Vision10.1007/978-981-97-6992-6_15(183-197)Online publication date: 7-Dec-2024
  • (2019)In-Database Geospatial Analytics using PythonProceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities10.1145/3356395.3365598(17-24)Online publication date: 5-Nov-2019
  • (2019)Keep Your Host Language Object and Also Query itProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335798(133-144)Online publication date: 23-Jul-2019

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cover image ACM Other conferences
SSDBM '18: Proceedings of the 30th International Conference on Scientific and Statistical Database Management
July 2018
314 pages
ISBN:9781450365055
DOI:10.1145/3221269
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2018

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Author Tags

  1. SQL-pushdown
  2. data analytics
  3. data mining
  4. database

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  • Demonstration

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SSDBM '18

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SSDBM '18 Paper Acceptance Rate 30 of 75 submissions, 40%;
Overall Acceptance Rate 56 of 146 submissions, 38%

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Cited By

View all
  • (2024)SQL Query Recommendation Based on Matrix FactorizationInnovations in Computational Intelligence and Computer Vision10.1007/978-981-97-6992-6_15(183-197)Online publication date: 7-Dec-2024
  • (2019)In-Database Geospatial Analytics using PythonProceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities10.1145/3356395.3365598(17-24)Online publication date: 5-Nov-2019
  • (2019)Keep Your Host Language Object and Also Query itProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335798(133-144)Online publication date: 23-Jul-2019

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