Special Sessions

Note: Submissions to a special session should be submitted by choosing the respective special session when you Submit a Paper to the main conference. Review will be coordinated by the special session chairs and final decisions will be made by the program co-chairs. Accepted special session submissions will be included into the main conference proceedings to be published by IEEE and included into the IEEE Xplore Digital Library. Top quality papers to special sessions may be recommended by the special session chairs for considerations to the special issues.

Submit your special session papers here.

Special Session: Trends & Controversies in Data Science


Longbing Cao
Hiroshi Motoda
Hiroshi Motoda
George Karypis
George Karypis
Bart Goethals
Bart Goethals






Aims and scope

As an emerging area, data science is facing great opportunities as well as challenges.Often arguments exist: What is data science? Why data science? We have information science already, why do we need data science? Do we need analytics science? Is analytics new? What is the difference between statistics and data analytics? What makes a data scientist?

We believe that a special session on Trends and Controversy about data science and advanced analytics could bring insights from different mindsets for the healthy development of the science and society. Accordingly, this T&C special session will host talks by invitation to outline different views about today and future of data science. Invited speakers can contribute a paper (in the same format as the main conference submissions but could be less than 7 pages) to the special session, which will be handled by program co-chairs and accepted into the main conference proceeding probably by addressing comments from the program co-chairs.

Topic of interests

We expect insightful talks about forward-thinking, big thinking, original research, critical reflection and questioning on existing theories and tools, and/or innovative insights about data science, big data, advanced analytics.

Nominations to this T&C special sessions are welcome, please contact dsaa2014@gmail.com.

Special Session: Statistical and Mathematical Tools for Data Mining


Massih-Reza Amini
Massih-Reza Amini
Clausel Marianne
Clausel Marianne
(University of Grenoble, France)






Aims and scope

Huge amounts of data are now easily and legally available on the Web. This data is generally heterogeneous and merely structured. Data mining and Machine learning models which have been developed to automatically retrieve, classify or cluster observations on large yet homogeneous data collections have to be rethought. Indeed, many challenging problems, inevitably associated to Big Data, have manifested the needs for tradeoffs between the two conflicting goals of speed and accuracy. Thishas led to some recent initiatives in both theory and practice and has highly motivated the interest of the Machine Learning community. Further theoretical challenges include how to tackle problems with large number of target classes, appropriate optimization techniques to handle big data problems. Structured/sequential prediction models for big data problems such as prediction in hierarchy of classes has also gained importance in recent years.

Topic of interests

The goal of this special issue is to bring together research studies aiming at developing new data mining and machine learning tools to handle new challenges associated to Big Data mining. We are especially interested on the following topics:

  •  Distributed on-line learning
  •  Transfer Learning for big data
  •  Optimization techniques for large-scale learning
  •  Handling large number of target classes in big data
  •  Structured prediction models in big data
  •  Speed/Accuracy tradeoffs in big data
  •  Statistical inference for big data
  •  Noise in Big data

Program Committee

  • Massih-Reza Amini, Laboratoire d’Informatique de Grenoble, University of Grenoble, France
  • Marianne Clausel, Laboratoire Jean Kunzmann, University of Grenoble, France
  • Éric Gaussier, Laboratoire d’Informatique de Grenoble, University of Grenoble, France
  • Anatoli Idoutski, Laboratoire Jean Kunzmann, University of Grenoble, France
  • Hanmin Jung, Korea Institute of Science and Technology Information
  • Cyril Goutte, Interactive Language Technology group, National Research Council Canada
  • Julien Mairal, Institut national de recherche en informatique et en automatique (Inria), France
  • Young-Min Kim, Korea Institute of Science and Technology Information, Korea

Special Session: Warehousing and Intelligent Analysis Of Complex Network Big Data (WIBIG 2014)


Giuseppe Manco
Giuseppe Manco
Elio Masciari
 Domenico Saccà
(High Performance Computing
and Networks (ICAR-CNR) of
the National Research Council
of Italy and University of Calabria)







Submission Due: 22 July, 2014

Aim and Scope

Nowadays, data generation has deeply changed with respect to last decades. In particular, data generated by web based system, government and companies exhibits a complex structure (e.g. multi-table data, XML data, web data, time series and sequences, graphs and trees, social network posts, tweets) that lead to a new paradigm of Big Data. This fact poses new challenges for current information systems for storing, managing and mining these complex data effectively. The aim of this special session is to bring together researchers and practitioners of data mining, data warehousing and data specialists who are interested in the advances and latest developments in this new area of modern computer science. It aims also at integrating recent results from existing fields such as data mining, statistics, machine learning and relational databases to discuss and introduce new algorithmic foundations and representation formalisms. We are interested in advanced techniques that while preserving the informative richness of data, allow us to efficiently and effectively identify complex information units present in such data.

Topic of Interest

WIBIG 2014 calls for international contributions related to foundations, challenges and research opportunities raised by real-world learning and data mining problems in which the data as well as patterns are complex and heterogeneous. The goal of the workshop is to promote and publish research in the field of complex pattern mining. Suggested topics include (but not limited to) the following:

  • Behavioural Analysis in Social Networks
  • Big Data Warehousing
  • Foundations on pattern mining, pattern usage, and pattern understanding
  • Mining stream, time-series and sequence data
  • Mining networks and graphs
  • Mining biological data
  • Mining dynamic and evolving data
  • Mining environmental and scientific data
  • Mining heterogeneous and ubiquitous data
  • Mining multimedia data
  • Mining multi-relational data
  • Mining semi-structured and unstructured data
  • Mining spatio-temporal data
  • Social Media Analytics
  • Ontology and metadata
  • Privacy preserving mining
  • Recommender Systems
  • Semantic Web and Knowledge Databases
  • Structured Output Prediction

Program Committee

  • Saso Dseroski, Jozef Stefan Institute
  • Sergio Flesca, Università della Calabria
  • Pedro Furtado, Universidade de Coimbra
  • Joao Gama, University of Porto
  • Aristides Gionis, Aalto University
  • Barzan Mozafari, University of Michigan
  • Zbigniew Ras, University of North Carolina
  • Domenico Talia, Università della Calabria
  • Mohammed Zaki, Rensselaer Polytechnic Institute
  • Alicja Wieczorkowska, Polish-Japanese Institute of Information Technology
  • Carlo Zaniolo, UCLA


Special Session: Environmental and geo-spatial data analytics (EnGeoData)


Mathieu Roche
(Cirad, TETIS, France)
Maguelonne Teisseire
Maguelonne Teisseire
(Irstea, TETIS, France)






Aim and Scope

Environmental and more generally geo-spatial information is now provided by crowdsourcing but also by public administrations in the context of the open data policies. Analyses of such data are still challenging. Firstly because of their heterogeneity (structural, semantic, spatial and temporal), and secondly because of the difficulty in choosing the “best” knowledge discovery process to apply, according to the needs of the experts in the field. This special session aims at discussing and assessing some of these strategies covering all or part of the issues mentioned above, from a theoretical or experimental point of view.

Topics of Interest

  • Pre and Post Data processing
  • Data Quality, Result Evaluation
  • Data Mining or Data Warehousing Applications
  • Text-Mining
  • Visual Analytics
  • KDD real use-cases

Dedicated to environmental and geo-spatial Data

Program Committee (to be confirmed)


Special Session: Bioinformatics, Biomedical, Health and Medical Analytics (BHMA)


Aim and Scope

This special session aims to address big data analytics in the health and medical domains, and issues related to bioinformatics and biomedical aspects.

Topics of Interest

  • Healthcare analytics
  • Medical data analysis
  • Bioinformatics
  • Biomedical analysis
  • Relevant algorithms, tools and applications
  • Applications

Program Committee (to be confirmed)


Special Session: Exploratory Computing (EC)


Paolo Paolini
Paolo Paolini
Gabriella Pasi
Letizia Tanca
Letizia Tanca





submission due: 22 July, 2014

Aim and  Scope

Most current approaches to address the management of Big Data attack the problem from one specific side, for instance by proposing efficient querying or analysis techniques that summarize data or that reduce their dimensionality. We need however a more comprehensive approach that supports rapid sense-making of big data, or, as the DSAA  conference advocates, “real-time decision-making, collaboration, and ultimately value co-creation”*.  At the crossroads among several different disciplines – Data Analysis, Exploratory Search, Data Exploration, Information Retrieval, Design and Human-Computer Interaction -, a rich exploratory experience should allow a wide range of different operations including data analysis, exact and approximate query-answering, generation of quick and summarized answers, personalized and context-aware answers, recommendation, data exploration, data visualization etc., synergically combined to provide users with the necessary feedback to progress towards another  exploratory step.

“Exploratory Computing” makes reference both to the variegated user scenarios described above and to the powerful computation tools that are needed in order to make the exploration effective.

 The special session on Exploratory Computing aims at gathering ground-breaking contributions to pave the way towards a new paradigm for dealing with structurally and semantically complex data.

This is a multi-faceted challenge, which encompasses all the phases of the creation process of an EC system, from computational issues to user experience design.

Topics Of Interest Topics of interest includes (but are not limited to):

  • Exploratory search
  • Faceted Search
  • Data Analytics
  • Aggregated Search
  • Data Exploration, Visualization
  • Exploratory interfaces (HCI)

Program Committee (to be completed)

Special Session: Computational Intelligence and Applications in Big Data Analysis


Kun Yue
(Yunnan University)
Xiaofeng Wang
(Institute of Computing Technology
 Chinese Academy of Science)


Aim and Scope

With the rapid development of data acquisition/processing mechanisms, cloud computing and Web2.0 like novel information services, analyzing big data has become the subject of great interest and the most important task in data science. Processing, analyzing, understanding and utilizing big data make knowledge discovery mechanisms be improved rapidly. The emerging big data based knowledge discovery method has greatly promoted the development of computational intelligence and computational cognition. It has been demonstrated in many application areas that big data based techniques can effectively improve the performance of practical intelligent systems. The era of big data will make more rapid development of artificial intelligence, since big data can bridge the gap between sufficient data and intelligent machine learning.

This special session aims to facilitate the collaboration between researchers in data science and artificial intelligence areas by presenting cutting edge research topics and methodologies about knowledge discovery, computational intelligence and applications in big data analysis.

Topics of Interest

We encourage papers that propose novel techniques of Knowledge discovery and Intelligence processing in big data analysis for the tasks such as (but not limited to):

  • Knowledge discovery from big data
  • Advanced Machine Learning Algorithms in big data analysis
  • Computational intelligence in big data analysis
  • Computational cognition in big data analysis
  • Information service driven big data analysis
  • Benchmarking for knowledge discovery in big data analysis
  • Challenges and visions for new services and applications in big data analysis
  • Experience with real-world applications in big data analysis

Program Committee (To be confirmed)

  • Liang Chang (Guilin University of Electronic Technology)
  • Pan Du (Institute of Computing Technology, Chinese Academy of Sciences)
  • Sheng Huang (IBM Research-China)
  • Junzhong Ji (Beijing University of Technology)
  • Yunchuan Sun (Beijing Normal University)
  • Bin Wang (Northeastern University)
  • Lizhen Wang (Yunnan University)
  • Xiaoling Wang (East China Normal University)
  • Hao Wu (Yunnan University)
  • Yang Xu (University of Electronic Science and Technology of China)
  • Ying Yan (Microsoft Search Technology Center)
  • Jiadong Zhang (City University of Hong Kong)
  • Bin Zhao (Nanjing Normal University)

Important Dates

  • Paper submission deadline: August 23, 2014
  • Notification of acceptance: September 15, 2014
  • Registration due: September 22, 2014
  • Workshop date: October 30, 2014
  • Camera ready: November 15, 2014

Submission Instructions

The workshop could possibly be a post-conference publication as Springer CCIS series.