[Liste-proml] Extended deadline: CFP Big Data Mining and Searching : BigDMS 2016 (ICCCI 2016) Halkidiki, Greece, September 28-30, 2016

Rim FAIZ Rim.Faiz at ihec.rnu.tn
Lun 18 Avr 12:51:52 CEST 2016


Dear colleagues,

Due to the number of requests, the submission deadline is extended to 1st
May 2016 for Big Data Mining and Searching (BigDMS 2016).

 

-----------

 

CFP BigDMS 2016

Special Session on Big Data Mining and Searching

https://conferences.cwa.gr/iccci2016/wp-content/uploads/2016/04/ICCCI-2016-S
pecial-Session-BigDMS-2016-CFP-v.4.0.pdf

 

At the 8th International Conference on Computational Collective Intelligence
Technologies and Applications (ICCCI 2016)

Halkidiki, Greece, September 28-30, 2016

Conference website: http://conferences.cwa.gr/iccci2016/

 

 

Important dates

Submission of papers: May 1,2016

Notification of acceptance: June 1, 2016

Camera-ready papers: June 15, 2016

Registration & payment: June 15, 2016

Conference date: September 28-30, 2016

 

Special Session Organizers

Prof. Rim Faiz

Department of Computer Science

IHEC, University of Carthage, Tunisia

Laboratory LARODEC

E-mail:  <mailto:Rim.Faiz at ihec.rnu.tn> Rim.Faiz at ihec.rnu.tn

 

Prof. Nadia Essoussi

Department of Computer Science

FSEG Nabeul, University of Carthage, Tunisia

E-mail: nadia.essoussi at isg.rnu.tn

 

Objectives and topics

The exceptional growth of data sizes available on the Web, especially with
the wide use of social networks has changed data processing. Then,
traditional technologies are unable to handle this massive data. They
presented often multidimensional constraints; Data come from different
sources with diverse formats, should be treated in real time, and may be
subject to different interpretations depending on the status of the end
user. This is what has led to the emergence of Big Data.

The main challenge of Big Data management lies in both an increasing volume
of streaming data having petabytes size, heterogeneous, structured (from
data warehouse and graph database) or unstructured (from Web 2 contents,
text documents and social networks). It is in the context of unstructured
data that fits the Big Text Mining proposing new approaches and techniques
to transform this massive data into useful data.

Big Data Mining is part of the effort to improve the process of seeking new
knowledge from large volumes of data. It has become ubiquitous in
understanding and solving complex problems in different fields such as
engineering, healthcare, social networks, commerce, government, education,
medicine, security, computational biology, all search-based applications...

The objective of this session is to give an overview of the main lines of
this interesting area with its various opportunities and challenges.

The scope of the BigDMS 2016 includes, but is not limited to the following
topics:

•          Foundations and computation (Models and Frameworks for Big Data,
Graph Algorithms and Big Data, Computational Intelligence)

•          Algorithms for Big data (Simulation and Modeling, Natural
Language Processing, Multidimensional Big Data ...) 

•          Infrastructure and platforms

•          Cloud Computing/ Stream Computing for Big Data 

•          Big Data Management

•          Big Text Mining and Information Extraction

•          Big Data Analytics and Information Retrieval

•          Web IR and Social Media Search

•          Big data Visualization

•          Data Science

•          Recommendation System with Big data

•          Machine learning for Big data

•          Open data and unstructured data

•          Data preservation and provenance

•          Web search and information retrieval

•          Search and Mining

•          Machine learning and AI for big data

•          Search Engine Architectures and Scalability

•          Computational Modeling and Data Integration

•          Link and Graph Mining

•          Mobility and Big Data

•          Multimedia and Multi-structured Data

•          Big Data and Social Media

•          Big Data Privacy and Security

•          Big Data Analytics in e-Government and Society

•          Big Data Applications: Bioinformatics, Multimedia, Smartphones,
etc.

 

Submission

All contributions should be original and not published elsewhere or intended
to be published during the review period. Authors are invited to submit
their papers electronically in pdf format, through EasyChair. All the
special sessions are centralized as tracks in the same conference management
system as the regular papers. Therefore, to submit a paper, please activate
the following link and select the track: BigDMS 2016: Special Session on Big
Data Mining and Searching.

 

https://www.easychair.org/conferences/?conf=iccci2016 

Authors are invited to submit original previously unpublished research
papers written in English, of up to 10 pages, strictly following the
LNCS/LNAI format guidelines. Authors can download the Latex (recommended) or
Word templates available at Springer's web site. Submissions not following
the format guidelines will be rejected without review. To ensure high
quality, all papers will be thoroughly reviewed by the BigDMS 2016 Program
Committee. All accepted papers must be presented by one of the authors who
must register for the conference and pay the fee. The conference proceedings
will be published by Springer in the prestigious series LNCS/LNAI (indexed
by ISI CPCI-S, included in ISI Web of Science, EI, ACM Digital Library,
dblp, Google Scholar, Scopus, etc.).

 

Program Committee

Ajith Abraham, Machine Intelligence Research Labs (MIR Labs), Unites States

Thierry Badard, Laval University / Centre for Research in Geomatics, Canada

Hassan Badir, ENSAT Tangier, Morocco

Chiheb Ben N'cir, Université de la Manouba, Tunisia

Ismaïl Biskri, Université du Québec à Trois-Rivières, Canada

Guillaume Cleuziou, Université d'Orléans, France

Ernesto Damiani, University of Milan, Italy

Gayo Diallo, University of Bordeaux, France

Aymen Elkhlifi, University of paris Sorbonne, France

Nadia Essoussi, FSEG Nabeul, University of Carthage, Tunisia

Rim Faiz, IHEC, University of Carthage, Tunisia

Sami Faiz, ISAMM, University of Manouba, Tunisia

Riadh Farah, ISAMM, University of Manouba, Tunisia

Faiez Gargouri, ISIMS, Université de Sfax, Tunisia

Lamia Hadrich Belguith, FSEGS, University of Sfax, Tunisia

Frédéric Hubert, Laval University, Canada

Ahmed Moussa, ENSA, Abdelmalek Essaadi University, Morocco

Maria Malek, EISTI, France

Gabriella Pasi, University of Milan Bicocca, Italy

 

- -

Rim Faiz (PhD)

Full Professor in Computer Science

Head of Master «Business Intelligence»

Head of Master «E-Commerce & Technological Innovation»

IHEC - University of Carthage

Laboratory LARODEC

Head of Team «IR, Big Data, Text Mining and Semantic Web»

2016 Carthage Presidency, Tunisia

Phone : +216 98 337 248 / +216 71 775 948 - Fax : +216 71 775 944 

 <mailto:Rim.Faiz at ihec.rnu.tn> Rim.Faiz at ihec.rnu.tn

 <http://www.larodec.com/> http://www.larodec.com/

 

 

 

 

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