Delve: A Data Set Retrieval and Document Analysis System

Handle URI:
http://hdl.handle.net/10754/626737
Title:
Delve: A Data Set Retrieval and Document Analysis System
Authors:
Akujuobi, Uchenna Thankgod ( 0000-0002-7102-7994 ) ; Zhang, Xiangliang ( 0000-0002-3574-5665 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Citation:
Akujuobi U, Zhang X (2017) Delve: A Data Set Retrieval and Document Analysis System. Lecture Notes in Computer Science: 400–403. Available: http://dx.doi.org/10.1007/978-3-319-71273-4_39.
Publisher:
Springer International Publishing
Journal:
Machine Learning and Knowledge Discovery in Databases
Conference/Event name:
Machine Learning and Knowledge Discovery in Databases
Issue Date:
29-Dec-2017
DOI:
10.1007/978-3-319-71273-4_39
Type:
Conference Paper
ISSN:
0302-9743; 1611-3349
Additional Links:
https://link.springer.com/chapter/10.1007%2F978-3-319-71273-4_39
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAkujuobi, Uchenna Thankgoden
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2018-01-11T07:29:54Z-
dc.date.available2018-01-11T07:29:54Z-
dc.date.issued2017-12-29en
dc.identifier.citationAkujuobi U, Zhang X (2017) Delve: A Data Set Retrieval and Document Analysis System. Lecture Notes in Computer Science: 400–403. Available: http://dx.doi.org/10.1007/978-3-319-71273-4_39.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-319-71273-4_39en
dc.identifier.urihttp://hdl.handle.net/10754/626737-
dc.description.abstractAcademic 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.en
dc.publisherSpringer International Publishingen
dc.relation.urlhttps://link.springer.com/chapter/10.1007%2F978-3-319-71273-4_39en
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-71273-4_39en
dc.titleDelve: A Data Set Retrieval and Document Analysis Systemen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalMachine Learning and Knowledge Discovery in Databasesen
dc.conference.dateSeptember 18–22, 2017en
dc.conference.nameMachine Learning and Knowledge Discovery in Databasesen
dc.conference.locationSkopje, Macedoniaen
dc.eprint.versionPost-printen
kaust.authorAkujuobi, Uchenna Thankgoden
kaust.authorZhang, Xiangliangen
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