Linked open data mining for democratization of big data

Roberto Espinosa; Larisa Garriga; Jose Jacobo Zubcoff; Jose Norberto Mazon

doi:10.1109/BigData.2014.7004479

Linked open data mining for democratization of big data

Roberto Espinosa
, Larisa Garriga
, Jose Jacobo Zubcoff
, Jose Norberto Mazon

Universidad de Matanzas Camilo Cienfuegos
University of Alicante

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

17 Scopus citations

Abstract

Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge from big data could be of greater value if it is available for later consumption and reusing. In this paper, we present an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on big data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is an approach for democratizing big data through reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our work is based on a model-driven viewpoint in order to easily deal with the wide diversity of open data formats.

Original language	English
Title of host publication	Proceedings - 2014 IEEE International Conference on Big Data, Big Data 2014
Editors	Jimmy Lin, Jian Pei, Xiaohua Tony Hu, Wo Chang, Raghunath Nambiar, Charu Aggarwal, Nick Cercone, Vasant Honavar, Jun Huan, Bamshad Mobasher, Saumyadipta Pyne
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	17-19
Number of pages	3
ISBN (Electronic)	9781479956654
DOIs	https://doi.org/10.1109/BigData.2014.7004479
State	Published - 2014
Externally published	Yes
Event	2nd IEEE International Conference on Big Data, Big Data 2014 - Washington, United States Duration: 27 Oct 2014 → 30 Oct 2014

Publication series

Name	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Conference

Conference	2nd IEEE International Conference on Big Data, Big Data 2014
Country/Territory	United States
City	Washington
Period	27/10/14 → 30/10/14

Keywords

big data
data mining
linked open data

Access to Document

10.1109/BigData.2014.7004479

Cite this

Espinosa, R., Garriga, L., Zubcoff, J. J., & Mazon, J. N. (2014). Linked open data mining for democratization of big data. In J. Lin, J. Pei, X. T. Hu, W. Chang, R. Nambiar, C. Aggarwal, N. Cercone, V. Honavar, J. Huan, B. Mobasher, & S. Pyne (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, Big Data 2014 (pp. 17-19). Article 7004479 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004479

Espinosa, Roberto ; Garriga, Larisa ; Zubcoff, Jose Jacobo et al. / Linked open data mining for democratization of big data. Proceedings - 2014 IEEE International Conference on Big Data, Big Data 2014. editor / Jimmy Lin ; Jian Pei ; Xiaohua Tony Hu ; Wo Chang ; Raghunath Nambiar ; Charu Aggarwal ; Nick Cercone ; Vasant Honavar ; Jun Huan ; Bamshad Mobasher ; Saumyadipta Pyne. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 17-19 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

@inproceedings{8a864bf0ce064bc29a6832349ba3f760,

title = "Linked open data mining for democratization of big data",

abstract = "Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge from big data could be of greater value if it is available for later consumption and reusing. In this paper, we present an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on big data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is an approach for democratizing big data through reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our work is based on a model-driven viewpoint in order to easily deal with the wide diversity of open data formats.",

keywords = "big data, data mining, linked open data",

author = "Roberto Espinosa and Larisa Garriga and Zubcoff, \{Jose Jacobo\} and Mazon, \{Jose Norberto\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE International Conference on Big Data, Big Data 2014 ; Conference date: 27-10-2014 Through 30-10-2014",

year = "2014",

doi = "10.1109/BigData.2014.7004479",

language = "English",

series = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "17--19",

editor = "Jimmy Lin and Jian Pei and Hu, \{Xiaohua Tony\} and Wo Chang and Raghunath Nambiar and Charu Aggarwal and Nick Cercone and Vasant Honavar and Jun Huan and Bamshad Mobasher and Saumyadipta Pyne",

booktitle = "Proceedings - 2014 IEEE International Conference on Big Data, Big Data 2014",

address = "United States",

}

Espinosa, R, Garriga, L, Zubcoff, JJ & Mazon, JN 2014, Linked open data mining for democratization of big data. in J Lin, J Pei, XT Hu, W Chang, R Nambiar, C Aggarwal, N Cercone, V Honavar, J Huan, B Mobasher & S Pyne (eds), Proceedings - 2014 IEEE International Conference on Big Data, Big Data 2014., 7004479, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, Institute of Electrical and Electronics Engineers Inc., pp. 17-19, 2nd IEEE International Conference on Big Data, Big Data 2014, Washington, United States, 27/10/14. https://doi.org/10.1109/BigData.2014.7004479

Linked open data mining for democratization of big data. / Espinosa, Roberto; Garriga, Larisa; Zubcoff, Jose Jacobo et al.
Proceedings - 2014 IEEE International Conference on Big Data, Big Data 2014. ed. / Jimmy Lin; Jian Pei; Xiaohua Tony Hu; Wo Chang; Raghunath Nambiar; Charu Aggarwal; Nick Cercone; Vasant Honavar; Jun Huan; Bamshad Mobasher; Saumyadipta Pyne. Institute of Electrical and Electronics Engineers Inc., 2014. p. 17-19 7004479 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

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

TY - GEN

T1 - Linked open data mining for democratization of big data

AU - Espinosa, Roberto

AU - Garriga, Larisa

AU - Zubcoff, Jose Jacobo

AU - Mazon, Jose Norberto

PY - 2014

Y1 - 2014

N2 - Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge from big data could be of greater value if it is available for later consumption and reusing. In this paper, we present an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on big data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is an approach for democratizing big data through reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our work is based on a model-driven viewpoint in order to easily deal with the wide diversity of open data formats.

AB - Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge from big data could be of greater value if it is available for later consumption and reusing. In this paper, we present an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on big data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is an approach for democratizing big data through reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our work is based on a model-driven viewpoint in order to easily deal with the wide diversity of open data formats.

KW - big data

KW - data mining

KW - linked open data

UR - https://www.scopus.com/pages/publications/84921743824

U2 - 10.1109/BigData.2014.7004479

DO - 10.1109/BigData.2014.7004479

M3 - Conference contribution

AN - SCOPUS:84921743824

T3 - Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

SP - 17

EP - 19

BT - Proceedings - 2014 IEEE International Conference on Big Data, Big Data 2014

A2 - Lin, Jimmy

A2 - Pei, Jian

A2 - Hu, Xiaohua Tony

A2 - Chang, Wo

A2 - Nambiar, Raghunath

A2 - Aggarwal, Charu

A2 - Cercone, Nick

A2 - Honavar, Vasant

A2 - Huan, Jun

A2 - Mobasher, Bamshad

A2 - Pyne, Saumyadipta

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Big Data, Big Data 2014

Y2 - 27 October 2014 through 30 October 2014

ER -

Espinosa R, Garriga L, Zubcoff JJ, Mazon JN. Linked open data mining for democratization of big data. In Lin J, Pei J, Hu XT, Chang W, Nambiar R, Aggarwal C, Cercone N, Honavar V, Huan J, Mobasher B, Pyne S, editors, Proceedings - 2014 IEEE International Conference on Big Data, Big Data 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 17-19. 7004479. (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). doi: 10.1109/BigData.2014.7004479

Linked open data mining for democratization of big data

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this