Delve: A Data Set Retrieval and Document Analysis System

Uchenna Akujuobi; Xiangliang Zhang

doi:10.1007/978-3-319-71273-4_39

Delve: A Data Set Retrieval and Document Analysis System

Uchenna Akujuobi, Xiangliang Zhang^*

^*Corresponding author for this work

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

Abstract

Academic search engines (e.g., Google scholar or Microsoft academic) provide a medium for retrieving various information on scholarly documents. However, most of these popular scholarly search engines overlook the area of data set retrieval, which should provide information on relevant data sets used for academic research. Due to the increasing volume of publications, it has become a challenging task to locate suitable data sets on a particular research area for benchmarking or evaluations. We propose Delve, a web-based system for data set retrieval and document analysis. This system is different from other scholarly search engines as it provides a medium for both data set retrieval and real time visual exploration and analysis of data sets and documents.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
Editors	Michelangelo Ceci, Saso Dzeroski, Donato Malerba, Yasemin Altun, Kamalika Das, Jesse Read, Marinka Zitnik, Jerzy Stefanowski, Taneli Mielikäinen
Publisher	Springer Verlag
Pages	400-403
Number of pages	4
ISBN (Print)	9783319712727
DOIs	https://doi.org/10.1007/978-3-319-71273-4_39
State	Published - 2017
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, Macedonia, The Former Yugoslav Republic of Duration: Sep 18 2017 → Sep 22 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10536 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
Country/Territory	Macedonia, The Former Yugoslav Republic of
City	Skopje
Period	09/18/17 → 09/22/17

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-71273-4_39

Cite this

Akujuobi, U., & Zhang, X. (2017). Delve: A Data Set Retrieval and Document Analysis System. In M. Ceci, S. Dzeroski, D. Malerba, Y. Altun, K. Das, J. Read, M. Zitnik, J. Stefanowski, & T. Mielikäinen (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings (pp. 400-403). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10536 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-71273-4_39

Akujuobi, Uchenna ; Zhang, Xiangliang. / Delve : A Data Set Retrieval and Document Analysis System. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. editor / Michelangelo Ceci ; Saso Dzeroski ; Donato Malerba ; Yasemin Altun ; Kamalika Das ; Jesse Read ; Marinka Zitnik ; Jerzy Stefanowski ; Taneli Mielikäinen. Springer Verlag, 2017. pp. 400-403 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Academic search engines (e.g., Google scholar or Microsoft academic) provide a medium for retrieving various information on scholarly documents. However, most of these popular scholarly search engines overlook the area of data set retrieval, which should provide information on relevant data sets used for academic research. Due to the increasing volume of publications, it has become a challenging task to locate suitable data sets on a particular research area for benchmarking or evaluations. We propose Delve, a web-based system for data set retrieval and document analysis. This system is different from other scholarly search engines as it provides a medium for both data set retrieval and real time visual exploration and analysis of data sets and documents.",

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Akujuobi, U & Zhang, X 2017, Delve: A Data Set Retrieval and Document Analysis System. in M Ceci, S Dzeroski, D Malerba, Y Altun, K Das, J Read, M Zitnik, J Stefanowski & T Mielikäinen (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10536 LNAI, Springer Verlag, pp. 400-403, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017, Skopje, Macedonia, The Former Yugoslav Republic of, 09/18/17. https://doi.org/10.1007/978-3-319-71273-4_39

Delve: A Data Set Retrieval and Document Analysis System. / Akujuobi, Uchenna; Zhang, Xiangliang.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. ed. / Michelangelo Ceci; Saso Dzeroski; Donato Malerba; Yasemin Altun; Kamalika Das; Jesse Read; Marinka Zitnik; Jerzy Stefanowski; Taneli Mielikäinen. Springer Verlag, 2017. p. 400-403 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10536 LNAI).

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

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T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017

AU - Akujuobi, Uchenna

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N2 - Academic search engines (e.g., Google scholar or Microsoft academic) provide a medium for retrieving various information on scholarly documents. However, most of these popular scholarly search engines overlook the area of data set retrieval, which should provide information on relevant data sets used for academic research. Due to the increasing volume of publications, it has become a challenging task to locate suitable data sets on a particular research area for benchmarking or evaluations. We propose Delve, a web-based system for data set retrieval and document analysis. This system is different from other scholarly search engines as it provides a medium for both data set retrieval and real time visual exploration and analysis of data sets and documents.

AB - Academic search engines (e.g., Google scholar or Microsoft academic) provide a medium for retrieving various information on scholarly documents. However, most of these popular scholarly search engines overlook the area of data set retrieval, which should provide information on relevant data sets used for academic research. Due to the increasing volume of publications, it has become a challenging task to locate suitable data sets on a particular research area for benchmarking or evaluations. We propose Delve, a web-based system for data set retrieval and document analysis. This system is different from other scholarly search engines as it provides a medium for both data set retrieval and real time visual exploration and analysis of data sets and documents.

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

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BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings

A2 - Ceci, Michelangelo

A2 - Dzeroski, Saso

A2 - Malerba, Donato

A2 - Altun, Yasemin

A2 - Das, Kamalika

A2 - Read, Jesse

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PB - Springer Verlag

Y2 - 18 September 2017 through 22 September 2017

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Akujuobi U, Zhang X. Delve: A Data Set Retrieval and Document Analysis System. In Ceci M, Dzeroski S, Malerba D, Altun Y, Das K, Read J, Zitnik M, Stefanowski J, Mielikäinen T, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Springer Verlag. 2017. p. 400-403. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-71273-4_39

Delve: A Data Set Retrieval and Document Analysis System

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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