Implementing scalable structured machine learning for big data in the SAKE project

Simon Bin; Patrick Westphal; Jens Lehmann; Axel Ngonga

doi:10.1109/BigData.2017.8258073

Implementing scalable structured machine learning for big data in the SAKE project

Simon Bin
, Patrick Westphal
, Jens Lehmann
, Axel Ngonga

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

4 Citations (Scopus)

Abstract

Exploration and analysis of large amounts of machine generated data requires innovative approaches. We propose a combination of Semantic Web and Machine Learning to facilitate the analysis. First, data is collected and converted to RDF according to a schema in the Web Ontology Language OWL. Several components can continue working with the data, to interlink, label, augment, or classify. The size of the data poses new challenges to existing solutions, which we solve in this contribution by transitioning from in-memory to database.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Editors	Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Number of pages	8
Publisher	Institute of Electrical and Electronics Engineers Inc.
Publication date	01.07.2017
Pages	1400-1407
ISBN (Electronic)	9781538627143
DOIs	https://doi.org/10.1109/BigData.2017.8258073
Publication status	Published - 01.07.2017
Externally published	Yes
Event	5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States Duration: 11.12.2017 → 14.12.2017 Conference number: 5 https://cci.drexel.edu/bigdata/bigdata2017/

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Research areas and keywords

big data
classification algorithms
open source
rdf
semantic web
structured machine learning
Informatics

ASJC Scopus Subject Areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Information Systems and Management
Control and Optimization

Access to Document

10.1109/BigData.2017.8258073

Cite this

Bin, S., Westphal, P., Lehmann, J., & Ngonga, A. (2017). Implementing scalable structured machine learning for big data in the SAKE project. In J.-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 1400-1407). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258073

Bin, Simon ; Westphal, Patrick ; Lehmann, Jens et al. / Implementing scalable structured machine learning for big data in the SAKE project. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. editor / Jian-Yun Nie ; Zoran Obradovic ; Toyotaro Suzumura ; Rumi Ghosh ; Raghunath Nambiar ; Chonggang Wang ; Hui Zang ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Xiaohua Hu ; Jeremy Kepner ; Alfredo Cuzzocrea ; Jian Tang ; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1400-1407 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017).

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abstract = "Exploration and analysis of large amounts of machine generated data requires innovative approaches. We propose a combination of Semantic Web and Machine Learning to facilitate the analysis. First, data is collected and converted to RDF according to a schema in the Web Ontology Language OWL. Several components can continue working with the data, to interlink, label, augment, or classify. The size of the data poses new challenges to existing solutions, which we solve in this contribution by transitioning from in-memory to database.",

keywords = "big data, classification algorithms, open source, rdf, semantic web, structured machine learning, Informatics",

author = "Simon Bin and Patrick Westphal and Jens Lehmann and Axel Ngonga",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th IEEE International Conference on Big Data, Big Data 2017, IEEE ; Conference date: 11-12-2017 Through 14-12-2017",

year = "2017",

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language = "English",

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Bin, S, Westphal, P, Lehmann, J & Ngonga, A 2017, Implementing scalable structured machine learning for big data in the SAKE project. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1400-1407, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 11.12.17. https://doi.org/10.1109/BigData.2017.8258073

Implementing scalable structured machine learning for big data in the SAKE project. / Bin, Simon; Westphal, Patrick; Lehmann, Jens et al.
Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. ed. / Jian-Yun Nie; Zoran Obradovic; Toyotaro Suzumura; Rumi Ghosh; Raghunath Nambiar; Chonggang Wang; Hui Zang; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Xiaohua Hu; Jeremy Kepner; Alfredo Cuzzocrea; Jian Tang; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1400-1407 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

TY - CHAP

T1 - Implementing scalable structured machine learning for big data in the SAKE project

AU - Bin, Simon

AU - Westphal, Patrick

AU - Lehmann, Jens

AU - Ngonga, Axel

N1 - Conference code: 5

PY - 2017/7/1

Y1 - 2017/7/1

N2 - Exploration and analysis of large amounts of machine generated data requires innovative approaches. We propose a combination of Semantic Web and Machine Learning to facilitate the analysis. First, data is collected and converted to RDF according to a schema in the Web Ontology Language OWL. Several components can continue working with the data, to interlink, label, augment, or classify. The size of the data poses new challenges to existing solutions, which we solve in this contribution by transitioning from in-memory to database.

AB - Exploration and analysis of large amounts of machine generated data requires innovative approaches. We propose a combination of Semantic Web and Machine Learning to facilitate the analysis. First, data is collected and converted to RDF according to a schema in the Web Ontology Language OWL. Several components can continue working with the data, to interlink, label, augment, or classify. The size of the data poses new challenges to existing solutions, which we solve in this contribution by transitioning from in-memory to database.

KW - big data

KW - classification algorithms

KW - open source

KW - rdf

KW - semantic web

KW - structured machine learning

KW - Informatics

UR - https://www.scopus.com/pages/publications/85047846673

U2 - 10.1109/BigData.2017.8258073

DO - 10.1109/BigData.2017.8258073

M3 - Article in conference proceedings

AN - SCOPUS:85047846673

T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

SP - 1400

EP - 1407

BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

A2 - Nie, Jian-Yun

A2 - Obradovic, Zoran

A2 - Suzumura, Toyotaro

A2 - Ghosh, Rumi

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Zang, Hui

A2 - Baeza-Yates, Ricardo

A2 - Hu, Xiaohua

A2 - Kepner, Jeremy

A2 - Cuzzocrea, Alfredo

A2 - Tang, Jian

A2 - Toyoda, Masashi

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Big Data, Big Data 2017

Y2 - 11 December 2017 through 14 December 2017

ER -

Bin S, Westphal P, Lehmann J, Ngonga A. Implementing scalable structured machine learning for big data in the SAKE project. In Nie JY, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, editors, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1400-1407. (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017). doi: 10.1109/BigData.2017.8258073

Implementing scalable structured machine learning for big data in the SAKE project

Abstract

Bibliographical note

UN SDGs

Research areas and keywords

ASJC Scopus Subject Areas

Access to Document

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