Accepted Special Sessions and Workshops

       Prof. Shouyang Wang and Prof. Xiaoguang Yang, madis@amss.ac.cn (Management Science, The Academy of Mathematics and System Science, Chinese Academy of Sciences, China)

       This special session is targeted on economic forecasting and economic policy making with new data sources, new economic models, or new computational technologies. Topics of this special session are, but not limited to researches on:
   1) Economic forecasting or early warning using social media data;
   2) Economic policy simulation using large scale models or multi-agent based models,
   3) Visualization of big data based economic modeling for economic policy-making. Interdisciplinary discussions of economics, information science and computer science are encouraged.

       Prof. Alexander Vasin, vasin@cs.msu.su (Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics, Operations Research department, Russia)
Prof. Alexander Belenky, abelenky@hse.ru (HSE, Moscow, Russia)

       The topics and areas include, but not limited to:
   1) analysis and design of electricity markets;
   2) optimal development of electric networks;
   3) capacity markets and distributed generation;
   4) electric power generation and environmental problems.

       Prof. Feliks I. Ereshko, fereshko@ccas.ru (Head of Department of Information Systems, Dorodnicyn Computing Centre, Russian Academy of Science, Moscow, Russia)
Prof. Alexander M. Karminsky, akarminsky@hse.ru (Chief Research Fellow of International Laboratory of Quantitative Finance, HSE, Moscow, Russia)

       The topics and areas include, but not limited to:
   1) financial engineering;
   2) mathematical models and computational techniques in financial sphere;
   3) algorithmic trading.

       Prof. Enrique Herrera-Viedma, viedma@decsai.ugr.es (Granada University, Spain)
Prof. Hamido Fujita, HFujita-799@acm.org (Iwate Prefectural University, Japan)
Prof. Francisco Chiclana, chiclana@dmu.ac.uk (De Montfort University, U.K.)
Prof. Francisco Javier Cabrerizo, cabrerizo@issi.uned.es (UNED, Spain)
Prof. Ignacio Javier Pérez, ignaciojavier.perez@uca.es (University of Cadiz)

       The preference modelling deals with the representation and modelled of the preferences provided by the experts in the problems. The fuzzy logic provides an adequate framework to deal with the uncertainty presented in the user opinions. The fuzzy preference modelling has been satisfactory applied in decision making and consensus context. The objective of the proposed session is to highlight the ongoing research on fuzzy preference modelling in consensus and decision making under uncertainty. Focusing on theoretical issues and applications on various domains, ideas on how to solve consensus processes in decision making under fuzzy preference modelling, both in research and development and industrial applications, are welcome. Papers describing advanced prototypes, systems, tools and techniques and general survey papers indicating future directions are also encouraged.

       Topics appropriate for this special session include, but are not limited to:
   1) Fuzzy preference modelling in group decision-making
   2) Decision support system applications based on fuzzy preference modelling
   3) Consensus in fuzzy multi-agent decision making
   4) New models of fuzzy preference modelling
   5) Fuzzy preference modelling in Web 2.0 decision frameworks
   6) Fuzzy preference relations
   7) Aggregation of fuzzy preferences
   8) Dynamic decision making

       Prof. Dmitry A. Novikov, novikov@ipu.ru (Deputy director on scientific research of Institute of Control Sciences, Russian Academy of Science ,Moscow, Russia)

       Prof. Panos M. Pardalos, ppardalos@hse.ru (University of Florida)
Leading Research Fellow Mikhail Batsyn, mbatsyn@hse.ru (Laboratory of Algorithms and Technologies for Network Analysis, HSE)

       The main goal of this session is to consider real-life logistics and optimization problems and present modern algorithms for solving such problems. These problems include, but not limited to: rich vehicle routing problems, warehouse optimization problems, manufacturing optimization problems, production scheduling problems. The main feature of real-life optimization problems is a large number of variables and constraints in its mathematical programming formulations. In most cases this makes it impossible to find a global optimum of a real-life optimization problem. Fortunately, in practice it is usually enough to find a solution which is significantly better than a solution which can be obtained by hand applying some simple greedy considerations. Due to the fast development of modern computers and meta-heuristic algorithms nowadays it is possible to build millions of heuristic solutions in reasonable time intensively and diversely exploring the solution space. This helps to find very good solutions in practice.

       Prof. Florin Gheorghe Filip, ffilip@acad.ro (Romanian Academy, Romania)
Prof. Ioan Dzitac, rector@univagora.ro (Agora University of Oradea, Romania)

       Artificial and Computational Intelligence provide the background for the development of smart management systems. Today, such intelligent systems may take many forms, encompass a variety of approaches and include many design challenges. The goal of this special session is to bring together researchers interested in applications of soft computing algorithms and procedures in quantitative management, in order to exchange ideas on problems, solutions, and to work together in a friendly environment.
Topics of interest include, but are not limited to:
   1)  Ant colony optimization algorithms
   2)  Artificial intelligence methods for web mining
   3)  Computational intelligence methods for data mining
   4)  Decision support systems for quantitative management
   5)  Decision making with missing and/or uncertain data
   6)  Fuzzy and neuro-fuzzy modelling and simulation
   7)  Fuzzy-sets-based models in operation research
   8)  Knowledge Discovery in Databases
   9)  Machine learning for intelligent support of quantitative management
  10)  Neural networks in decision making tools
  11)  Smarter decisions

       Prof. Sergei O. Kuznetsov, skuznetsov@hse.ru (HSE, Moscow, Russia)
Prof. Dominik Slezak, slezak@infobright.com (Warsaw University, Poland)      

       Prof. Peter Wolcott, pwolcott@unomaha.edu (Department of Information Systems & Quantitative Analysis, College of Information Science & Technology, University of Nebraska at Omaha, USA)     

       The growth of on-line learning, massive open on-line courses (MOOCs), technology use within the classroom, and systems that track student behavior during – and sometimes outside of – learning activities has created an abundance of data that could, in principle, be used to improve student learning and retention, personalize instruction, inform decision-making by students and educators, and lead to the development of improved learning systems and experiences.  Many challenges face those individuals and institutions that seek to leverage the mounds of data generated during the learning process into the desired benefits.  Linking raw data and valid analysis to effective learning adaptations and educational interventions to improved outcomes remains an uncertain endeavor.  This session invites contributions that advance our understanding of the use of “big data” to improve learning outcomes and educational processes.  Topics include, but are not limited to, learning analytics, educational data mining, evidence centered design, adaptive learning systems, data visualization, personalized instruction systems, and student performance prediction.

       Prof. Luiz Flavio Autran Monteiro Gomes, autran@ibmecrj.br (Ibmec/Rio de Janeiro, Brazil)
Prof. Alexander E. Lepskiy, alepskiy@hse.ru (HSE, Moscow, Russia)

This workshop will comprise from four to five papers that are expected to approach technical aspects related to structuring, analyzing, and solving multicriteria problems with imprecise information and criteria interaction. Some of the tools that may be present in those papers are fuzzy, fuzzy intuitionistic, grey, probabilistic, rough, and verbal decision analytical modelling; and different forms to treat interaction between criteria in multicriteria analysis, such as the uses of the polar and the bipolar Choquet integrals as well as other fuzzy integrals. Papers following others streams of thought are very welcome, as long as they cover either imprecise information, criteria interaction or both.

       Prof. Xingsen Li, lixingsen@126.com, Haolan Zhang, Bin Shen and Yanbin Liu (NIT, Zhejiang University, China)

       With the rapid development of information technology, knowledge acquisition through data mining becomes one of the most important directions of scientific decision-making; however, Utilizing computer and Internet to solve contradictory problems and carry out exploration and innovation is still an ideal for human beings. Extenics is a new inter-discipline of mathematics, information, philosophy and engineering including Extension theory, extension innovation methods and extension engineering. It is dedicated to exploring the theory and methods of solving contradictory problems uses formalized models to explore the possibility of extension and transformation of things and solve contradictory problems intelligently. The intelligent methods aim to provide targeted decision-making on the transformation of the practice which is facing the challenges of data explosion. Artificial intelligence and intelligent systems offer efficient mechanisms that can significantly improve decision-making quality. Through ITQM, participants can further discuss the state-of-art technology in the Intelligent Decision Making and Extenics based Innovation field as well as the problems or issues occurred during their research.

       The topics and areas include, but not limited to:
   1) Intelligent Information Management and Problem Solving
   2) Knowledge Mining on E-business
   3) Intelligent Systems and its Applications
   4) Intelligent Logistics Management and Web of Things
   5) Web Marketing and CRM
   6) Intelligent Data Analysis and Financial Management
   7) Intelligent technology and Tourism Management
   8) Innovation theory and Methods
   9) Extenics based Applications
 10) Extension data mining and its Applications
 11) Web Intelligence and Innovation
 12) Knowledge based Systems and decision-making theory

       Prof. Jifa Gu, zll933@163.com and Lingling Zhang, zhangll@ucas.ac.cn (Management School of Graduate University of Chinese Academy of Sciences)

       Knowledge or hidden patterns discovered by data mining from large databases has great novelty, which is often unavailable from experts’ experience. Its unique irreplaceability and complementarity has brought new opportunities for decision-making and it has become important means of expanding knowledge bases to derive business intelligence in the information era. The challenging problem, however, is whether the results of data mining can be really regarded as “knowledge”. To address this issue, the theory of knowledge management should be applied. Unfortunately, there appears little work in the cross-field between data mining and knowledge management.
Intelligent Knowledge Management is the management of how rough knowledge and human knowledge can be combined and upgraded into intelligent knowledge. Intelligent Knowledge Management aims to bridge the gap between these two fields. This study not only promotes more significant research beyond data mining, but also enhances the quantitative analysis of knowledge management on hidden patterns from data mining.
The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of data mining, expert mining, pattern refinement and intelligent knowledge management, to generate new methods to evaluate the mined patterns and determine directions for further research. Papers should present modeling approaches/perspectives to intelligent knowledge. The workshop is interested in topics related to all aspects of patterns evaluation, expert mining and intelligent knowledge.

    1) Intelligent Knowledge Management
-  Knowledge synthesis
-  Expert Mining
-  Pattern Refinement
-  Interestingness Measures for Knowledge Discovery
-  Knowledge Presentation and Visualization
-  Knowledge Evaluation
-  KDD Process and Human Interaction

    2) Intelligent Knowledge Management System
-  Intelligent Systems and Agents
-  Multi Agent-based KDD Infrastructure
-  Meta-synthesis and Advanced Modeling
-  Knowledge Reuse and Ontology
-  Knowledge Management Support Systems 

       Prof. Jianping Li, ljp@casipm.ac.cn (Chinese Academy of Sciences, China)
Prof. Yi Peng, pengyicd@gmail.com (University of Electronic Science and Technology of China, China)
Prof. Xiaodong Lin, lin@business.rutgers.edu (Rutgers University, USA)
Prof. Rongda Chen, rongdachen@163.com (Zhejiang University of Finance & Economics)
Associate Professor Henry Penikas, penikas@hse.ru (HSE, Moscow, Russia) 

       The analysis of inter-risk correlation and risk aggregation is an important factor to risk measurement, such as the interaction of market risk, credit risk and operational risk. Correlation analysis and risk measurement can be viewed as a Multiple Criteria Decision Making problem in a certain extent, which is the trade-off among different aspects, such as the “project triangle”（cost, quality and schedule）.Some mathematical models such as Copula models are used for measuring risk correlation, but risk management must extend far beyond the use of standard measurement in practical operations and applications. An important aspect is to emphasize on the correlation analysis of risks and thus effectively measure all kinds of financial risks.
The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of risk correlation and measurement, to assess the state of knowledge of risk correlation and measurement, to generate new results in this relatively under-researched area, and determine directions for further research, Papers should present modeling approaches/perspectives to risk correlation and measurement. The workshop is interested in topics related to all aspects of risk correlation and measurement.

       Topics of interest include, but are not limited to, the following:
   1) Foundation of risk correlation and dependency
   2) Correlation analysis of financial risks
   3) Correlation analysis of software risks
   4) Correlation analysis of project risks
   5) Risk correlation modeling
   6) Risk analysis by multiple criteria
   7) Risk integrated management and risk correlation
   8) Credit scoring, Credit rating
   9) Portfolio management
  10) New techniques to risk measurement

       Prof. Yingjie Tian, tyj@ucas.ac.cn (Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, Beijing, China)
Prof. Zhiquan Qi, qizhiquan@ucas.ac.cn (Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, Beijing, China)
Prof. Yong Shi, yshi@unomaha.edu (College of Information Science and Technology, University of Nebraska at Omaha, USA and Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, Beijing, China)
Prof. Boris Mirkin, bmirkin@hse.ru (HSE, Moscow, Russia)

       The fields of data mining and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most data mining approaches. For last several years, the researchers have extensively applied quadratic programming into classification, known as V. Vapnik’s Support Vector Machine, as well as various applications. However, using optimization techniques to deal with data separation and data analysis goes back to more than many years ago. According to O.L. Mangasarian, his group has formulated linear programming as a large margin classifier in 1960’s. In 1970’s, A. Charnes and W.W. Cooper initiated Data Envelopment Analysis where a fractional programming is used to evaluate decision making units, which is economic representative data in a given training dataset. From 1980’s to 1990’s, F. Glover proposed a number of linear programming models to solve discriminant problems with a small sample size of data. Then, since 1998, the organizer and his colleagues extended such a research idea into classification via multiple criteria linear programming (MCLP) and multiple criteria quadratic programming (MQLP), which differs from statistics, decision tree induction, and neural networks. So far, there are more than 100 scholars around the world have been actively working on the field of using optimization techniques to handle data mining and web intelligence problems. This workshop intends to promote the research interests in the connection of optimization, data mining and web intelligence as well as real-life applications.

       Prof. Peng Zhang, zhangpeng@iie.ac.cn (Chinese Academy of Sciences, China)
Prof. Svetlana Maltseva, smaltseva@hse.ru (HSE, Moscow, Russia)

       Social media, such as Facebook, Flickr, Twitter, have become important mediums for information sharing and spreading, with rapidly increasing users over the past few years. Through the powerful effect of word-of-mouth, social media play a critical role in affecting people's opinions and behaviors. Social media analysis is an inherently interdisciplinary academic field emerged from social psychology, sociology, statistics and graph theory. The workshop aims to draw together empirically-grounded and theoretically-informed researchers to discuss the key issues in contemporary social network analysis and mining methods across disparate fields and methodologies. The workshop also solicits high-quality original research papers in any aspect of data mining and social network analysis. Contributions are invited that address a range of related issues.

       Areas for consideration could include, but are not limited to:
  1) Social Web Search
  2) Graph data and networks
  3) Algorithms and Systems for Social networks
  4) Distributed and Parallel Algorithms
  5) Big Data Search Architectures, Scalability and Efficiency
  6) Social Data Acquisition, Integration, Cleaning, and Best Practices
  7) Visualization Analytics for Social Network Data
  8) Computational Modeling and Data Integration
  9) Large-scale Recommendation Systems for Social Media 
10) Cloud/Grid/Stream Data Mining
11) Link and Graph Mining
12) Semantic-based Data Mining and Data Pre-processing
13) Mobility and Social Network Data
14) Multimedia and Multi-structured Data Analysis

       Prof. Fuad Aleskerov, alesk@hse.ru (HSE, Moscow, Russia)
Prof. Vassil Alexandrov, vassil.alexandrov@bsc.es (ICREA Research Professor in Computational Science at Barcelona Supercomputing Centre, Spain)
Associate Prof. Ying Liu, yingliu@ucas.ac.cn (University of Chinese Academy of Sciences, China)

       Big data is an emerging and active research topic in recent years. There is a clear need to analyze huge amounts of unstructured and structured complex data, historic data as well as data coming from real time feeds (e.g. Business data, meteorological ones from sensors, etc.). This is beyond the capability of traditional data processing techniques and tools. The challenges include data capture, storage, search, sharing, transfer, analysis, and visualization. In order to meet the requirement of big data analysis, Computational science and high performance computing methods and algorithms are in real demand to solve the above challenges, including scalable mathematical methods and algorithms, parallel and distributed computing, cloud computing, etc. This workshop will focus on the issues of high performance data analysis. Theoretical advances, mathematical methods, algorithms and systems, as well as diverse application areas will be in the focus of the workshop.

       This year the workshop aims at organizing a special theme session exploring emerging trends in high performance data analysis. We welcome papers on all aspects of high performance data analysis, including, but not limited to:
  1) Data processing exploiting hybrid architectures and accelerators (multi/many-core, CUDA-enabled GPUs, FPGAs)
  2) Data processing exploiting dedicated HPC machines and clusters
  3) Data processing exploiting cloud
  4) High performance data-stream mining and management
  5) Efficient, scalable, parallel/distributed data mining methods and algorithms for diverse applications
  6) Advanced methods and algorithms for big data visualization
  7) Parallel and distributed KDD frameworks and systems
  8) Theoretical foundations and mathematical methods for mining data streams in parallel/distributed environments
  9) Applications of parallel and distributed data mining in diverse application areas such as business, science, engineering, medicine, and other disciplines

       Prof. Guo Li, guoli@iie.ac.cn (IIE, Chinese Academy of Sciences, China)
Prof. Boris Mirkin, bmirkin@hse.ru (HSE, Moscow, Russia)
Prof. Hu Yue, huyue@iie.ac.cn (IIE, Chinese Academy of Sciences, China)
Dr. Zhou Xiaofei, zhouxiaofei@iie.ac.cn (IIE, Chinese Academy of Sciences, China)

       Semantic knowledge discovery, extraction and analysis are becoming more widespread and significant in data mining, artificial intelligence and knowledge management fields. Semantic is latent essential knowledge, feature, structure and relations behind the big information data, such as text semantic, multimedia semantic and semantic graph etc. This workshop aims to provide an international forum to discuss semantic learning and data intelligent mining for computational science, knowledge engineering and management decision discipline fields.

       The topics covered will include, but not limited to:
  1) Semantic extraction and expression
  2) Text semantic analysis
  3) Semantic learning of event ontologies
  4) Multimedia semantic learning
  5) Semantic graph
  6) Semantic knowledge in management decision
  7) Semantic representations of spatio-temporal data
  8) Social agents for intelligent awareness
  9) Situational semantic awareness
 10) Data mining technique: classification, clustering, regression etc.
 11) Text mining
 12) Information filter
 13) Object recognition
 14) Content retrieval
 15) Probabilistic models
 16) Topic Models
 17) Graph Computing