Difference between revisions of "SIMBig 2017"

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EXTENDED CALL FOR PAPERS - SIMBig 2017 - June 09, 2017
 
EXTENDED CALL FOR PAPERS - SIMBig 2017 - June 09, 2017
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SIMBig 2017 - 4th Annual International Symposium on Information Management and Big Data
 
SIMBig 2017 - 4th Annual International Symposium on Information Management and Big Data
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http://simbig.org/SIMBig2017/
 
http://simbig.org/SIMBig2017/
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OVERVIEW
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=== OVERVIEW ===
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Several domains have been affected by Big Data. Therefore there is a need to understand and exploit all kind of data. This process can be carried out thanks to "Data Science", which is based on methodologies of Data Mining, Natural Language Processing, Semantic Web, Statistics, etc. SIMBig seeks to present new methods of Data Science and related fields.
 
Several domains have been affected by Big Data. Therefore there is a need to understand and exploit all kind of data. This process can be carried out thanks to "Data Science", which is based on methodologies of Data Mining, Natural Language Processing, Semantic Web, Statistics, etc. SIMBig seeks to present new methods of Data Science and related fields.
  
KEYNOTE SPEAKERS
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=== KEYNOTE SPEAKERS ===
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Regina BarzilayRegina Barzilay, Massachusetts Institute of Technology, USA
 
Regina BarzilayRegina Barzilay, Massachusetts Institute of Technology, USA
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SIMBig 2017 proceedings will be indexed by Springer.  
 
SIMBig 2017 proceedings will be indexed by Springer.  
  
IMPORTANT DATES
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=== IMPORTANT DATES ===
 
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Latest revision as of 19:17, 6 July 2017

SIMBig 2017
4th Annual International Symposium on Information Management and Big Data
Event in series SIMBig
Dates 2017/09/04 (iCal) - 2017/09/06
Homepage: http://simbig.org/SIMBig2017/
Location
Location: Lima, Peru
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Table of Contents


EXTENDED CALL FOR PAPERS - SIMBig 2017 - June 09, 2017

SIMBig 2017 - 4th Annual International Symposium on Information Management and Big Data 4-6 September 2017 - Universidad del Pacífico - Lima, PERU

http://simbig.org/SIMBig2017/

OVERVIEW

Several domains have been affected by Big Data. Therefore there is a need to understand and exploit all kind of data. This process can be carried out thanks to "Data Science", which is based on methodologies of Data Mining, Natural Language Processing, Semantic Web, Statistics, etc. SIMBig seeks to present new methods of Data Science and related fields.

KEYNOTE SPEAKERS

Regina BarzilayRegina Barzilay, Massachusetts Institute of Technology, USA Jiawei HanJiawei Han, University of Illinois, USA Mark MusenMark Musen, Stanford University, USA Ravi KumarRavi Kumar, Google, USA Clement JonquetClement Jonquet, University of Montpellier (France) - Stanford University (USA) PUBLICATION


SIMBig 2017 proceedings will be indexed by Springer.

IMPORTANT DATES


June 09, 2017-> Long and short paper submission deadline 30 June 2017 -> Acceptance notifications 4 - 6 September 2017 --> Conference held in Lima, Peru TOPICS OF INTEREST


SIMBig2017 has a broad scope. We invite contributions on theory and practice, including but not limited to the following technical areas:

Data Science Big Data Data Mining Natural Language Processing Semantic Web Bio NLP, Healthcare Informatics Text Mining Information Retrieval Machine Learning Ontologies, Knowledge Representation, Linked Open Data Social Networks, Social Web, and Web Science Information visualization OLAP, Data warehousing, Business intelligence Spatiotemporal Data Agent-based Systems Constraints, Satisfiability, and Search CONTACT


SIMBig2017 General Chairs

Juan Antonio Lossio-Ventura, University of Florida, USA (jlossioventura@ufl.edu) Hugo Alatrista-Salas, Universidad del Pacífico, Peru (h.alatristas@up.edu.pe)

SIMBig2017

Facts about "SIMBig 2017"
End dateJune 9, 2017 +
Event in seriesSIMBig +
Event typeconference +
Has coordinates-12° 3' 44", -77° 2' 11"Latitude: -12.062105555556
Longitude: -77.036525
+
Has location cityLima +
Has location countryCategory:Peru +
Homepagehttp://simbig.org/SIMBig2017/ +
IsAEvent +
Start dateApril 9, 2017 +
Title4th Annual International Symposium on Information Management and Big Data +