Accepted Special Sessions and Workshops


Index:

Special Sessions:

  • Special Session 01: 4rd Intelligent Decision Making and Extenics based Innovation

    Xingsen Li, NIT, Zhejiang University, China (lixs@nit.zju.edu.cn)
    Chunyan Yang, Guangdong University of Technology, China ( fly_swallow@126.com)
    Chaoyi Pang, NIT, Zhejiang University, China ( chaoyip@netscape.net)
    Yanbin Liu, NIT, Zhejiang University, China (lyb.nbt@gmail.com)

    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:

    * Extenics based Information technology
    * Intelligent knowledge management based on Extenics
    * Intelligent Information Management and Problem Solving based on Extenics
    * Knowledge Mining on E-business
    * Intelligent Systems and its Applications
    * Intelligent Logistics Management and Web of Things
    * Web Marketing and CRM
    * Intelligent Data Analysis and Financial Management
    * Intelligent technology and Tourism Management
    * Innovation theory and Extenics based Methods
    * Extenics based Applications
    * Extension data mining and its Applications
    * Web Intelligence and Innovation
    * Knowledge based Systems and decision-making theory
    * Soft power and soft technology
    * Big data technology and decision making based on Extenicss


  • Special Session 02: Modeling and Data collection for decision making in Finance

    Alexander Karminsky, Higher School of Economics, Department of Finance (karminsky@mail.ru)

    Financial decisions are the whole show in up-to-date economic processes. To be more effective they require data and model support. So the aim of our special session will be concentrated at Big Data collection as well as using them for empirical model development.
    We propose to consider proposals for such questions as:

    - construction Big Data information systems in Banking and Financial markets;
    - creation of Risk Management models for financial decisions;
    - formation of rating system in Business and Finance;
    - using credit ratings and there models as creditworthiness measure;
    - consider efficiency models to compeer bank and financial companies usefulness
    - Etc.

    We think that the topic of instruments for decision making for banking and financial markets may be interesting approach of information technology opportunities and be interesting for different colleagues from developing and developed countries. To propose for such session we have in mind 3-4 potential papers in progress from France, Finland, Russia, Belgium etc.

  • Special Session 03: Optimization of Electricity, Natural Gas and Oil Markets.

    Alexander Belenky, HSE/Moscow, MIT/Cambridge ( abelenky@hse.ru)
    Alexander Vasin, MSU/Moscow (vasin@cs.msu.su)

    Natural gas, oil, and electricity are key energy resources for the national economy of every country. Developing these resources and providing them to the country*s industrial enterprises and population imply an interaction between the public and private sectors that affects the structure and the functioning of domestic and international markets of these resources. A wide spectrum of QM and IT problems associated with analyzing and optimizing these structures and with designing economic mechanisms relating to the functioning of the markets present a challenge for researchers in the field. The most theoretically complicated and practically important problems of these kinds will be addressed in the course of the proposed special session. Particularly, auction design problems, methods for optimally choosing electricity generators, algorithms for optimizing transmission systems for all the above-mentioned energy resources with respect to the social welfare maximization will be presented by the speakers and discussed by the section participants. Problems associated with finding both the optimal market architecture and economic regulations that take into account the strong market power of large producers and consumers of the resources will also be the subject of the discussion at the special section. Finally, a set of public-private partnership problems associated with developing large-scale projects of national importance dealing with the exploration and the use of energy resources and approaches to their solving, including finding equilibria acceptable to the project participants, will be considered.
    Coordinators of the session:
    Prof. Alexander Vasin, Operations Research Department, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia, and
    Prof. Alexander Belenky, Faculty of Economic Sciences, Department of Mathematics, National Research University Higher School of Economics,  Moscow, Russia, and  Center for  Engineering Systems Fundamentals, Massachusetts Institute of Technology, Cambridge, MA, USA;


  • Special Session 04: Technology Transfer and Commercialization for Sustainable Economic Growth and Job Creation.

    Tae-Young Park, School of Business at Hanyang University (pa0616@hanyang.ac.kr)

    Most of advanced countries emphasize an importance of technology transfer and commercialization than ever for sustainable economic growth and job creation. Especially, Korea realizes the importance much more than any other countries because Korea*s R&D expenditure over GDP is the highest among OECD countries but its performance in terms of royalty from technology transfer and the number of technology commercialization is lowest among them. Also, technology transfer is recently considered as an effective tool of creating jobs thanks to entrepreneurial activities through technology transfer. Therefore, this session aims at exploring and examining all of ideal mechanisms of technology transfer and commercialization for giving positive influences on sustaining economic growth and employment. The papers submitted to this session would be allowed to use a variety of theoretical lens and both quantitative and qualitative methodologies.
    Particular areas of interest but not confined to include:

    * Forecasting models for energy prices (oil, coal, gas, electricity)
    * Management of intellectual property
    * Knowledge spillover among public, university, and private organizations
    * Public-private partnership or cooperation
    * Academic entrepreneurship
    * Alliance and network among stakeholders
    * Entrepreneurial and public finance
    * Technology transfer and entrepreneurial activity
    * Policy of technology transfer and commercialization
    * Technology commercialization from sociotechnical system perspective
    * Human resource management and technology commercialization
    * Open innovation and technology commercialization
    * Successful case of technology transfer or technology commercialization


  • Special Session 05: The Expansion of Electronic Business Through Emerging Business Platforms.

    Hyungjoon Kim, College of Economics & Business Administration at Hanbat National University (hjkim@hanbat.ac.kr)

    The objective of this session is to discuss emergent electronic business which has a vital role in the global economy. The impact of global borderless networks and internet based platforms enables electronic businesses to reach customers all over the world. As a growing domain it will continue to change its paradigms and principles including customer behavior. There have been numerous platforms, new business models, new practices, and related theories for electronic businesses such as electronic buying and selling, distribution channels, communication channels, transaction processing, gathering consumer data, the use and analysis of big data and so forth. These fields have become important areas for exploration; this session aims to unite leading academic scientists and research scholars to exchange their experiences and research results about the diverse aspects of the field of electronic businesses. The papers submitted to this session may utilize various theoretical approaches including quantitative and qualitative methodologies.
    Areas of interest but not limited to:

    * New platforms, new practices, business models, related theories for e-business
    * The innovation drivers, cloud computing, big data, IoE, mobility, social media, etc.
    * Emerging trends in electronic business
    * E-commerce and M-commerce
    * Collaboration and e-service, usability, social networks, collaborative systems
    * E-business policy, strategy and governance
    * Internationalization and social, ethnical, and cultural factors
    * Innovative business models and organizational change
    * Consumer behavior and marketing
    * Adoption of web enablement models and technologies
    * Other related topics in electronic business and commerce


  • Special Session 06: Management of Technology and Innovation with Emerging Information Technologies.

    Byounggu Choi, College of Business Administration, Kookmin University (h2choi@kookmin.ac.kr)

    Management of technology (MoT) has contributed to expand our knowledge on innovation and success in business over the last three decades. In addition, business environments are rapidly changing with emerging technologies such as IOT (internet of things), fintech, social media, and big data tools. Managers are looking for opportunities to gain competitive advantage through integration of existing knowledge on MoT with the emerging technologies. We can clearly see that the possibilities of the technologies continue to evolve, and thus there is no question that MoT should accommodate the technologies to provide opportunities to organizations in the modern economy. Therefore, it is essential to identify how the emerging technologies can be used to address many of the issues currently facing MoT in both academia and industrial sectors.
    This special session aims to bring together scholars who investigate organizational performance and sustainable competitive advantage in the domains of MoT, innovation, and information technologies. It aims to draw together empirically-grounded and theoretically informed analyses of the key issues in contemporary forms of MoT and innovation from across various fields and methodologies. Applied research in these areas is also welcome.
    We invite submissions that are theoretically and empirically rigorous on topics that may include but are not limited to:

    * Management of technology
    * Strategic management of technology with IOT, fintech, social media, and big data tools
    * Management of technology strategies
    * Data, text, and Web mining for management of technology
    * Innovation strategies
    * Innovation in inter-organization contexts
    * Open innovation
    * Innovation with IOT, fintech, social media, and big data tools
    * Social analytics and social mining for innovation
    * Opinion mining and sentiment analysis for innovation
    * Creating new business models with IOT, fintech, social media, and big data tools
    * Role of IOT, fintech, social media, and big data tools in business value creation


  • Special Session 07: Enabling Technologies for the Data Driven World.

    Geun-Duk Park, School of Computer and Information Engineering, Hoseo University (gdpark@hoseo.edu)

    Internet makes trivial knowledge valuable and promising by communicating each other all day and night. Now, human don*t need to wander to collect information required any more, but have to sort and integrate data gathered from Internet. However, data is too rapidly growing to manipulate within a reasonable time. Thus, data handling becomes an essential technology to dominate future competitiveness. That is to say, we are facing with the advent of new era called data driven world. In this world, most decision-making comes from data-driven facts and there will be no more opportunities to fudge or blur facts by impulses, feelings, and experiences. This session deals with enabling technologies able to realize and lead data driven world in a wide range. The papers related to high-performance machine learning and data analytics platform focused specifically on Big Data will be welcome, but not be limited.
    Particular areas of interest but not confined to include:

    * Big Data infrastructures
    * Big Data storage platform
    * Big Data mining and inspection
    * Machine learning on Big Data
    * H/W and S/W platforms and models for data collection management and analysis
    * Big Data interchange and communication technology
    * Large-scale data handling methodology
    * Solution for highly integrated chips for Big Data
    * Silicon technologies considering data storage
    * Other advances in Big Data H/W and S/W technologies


  • Special Session 08: New challenges faced by Convergence Content and Digital Signage under the ※Creative Economy§ paradigm.

    Jeong-Ho Kwak, School of Business at Hoseo University ( jhkwak@hoseo.edu)

    Recently, ※convergence content§ has emerged as a buzzword in the content industry, which is generally referred to as a representative industry of the Creative Economy, one of the goals of the current Korean government. Convergence content is said to push ahead with either a combination between existing cultural art and cultural content and heterogeneous industries, or convergence between technologies and platforms and between technologies and industrial genres, as content that embeds technology into existing content industries or genres.
    For example, 3D/4D film content, interactive content, social content, hands-on-experience-type tourism content, convergence performance, hologram, and virtual reality content and so forth all fall under the category of convergence content. Academic attempts to define these new varieties of content have made little progress. It is now time for a broad exchange of opinions and ideas among those parties who are interested in such matters as the actual nature of the use/user environment of all these varieties of content, whether they offer clear competitiveness and differentiation compared to existing ones, how the existing ecological system is changing amid the new paradigm of combination and convergence, whether the policy environment is capable of accommodating such a change, and what varieties of content will come under the spotlight in the future.
    Simultaneously, Smart Signage, which used to be viewed merely as a form of outdoor ad, has been newly appraised as a new medium through which convergence is grafted. Particularly in the North American, Asian, and European markets, Smart Signage is making rapid progress. The same is true of the Korean, Chinese, and Japanese markets. It is now necessary to carry out industrial and policy research on the relevant contents, platforms, networks, and terminal devices with the aim of invigorating Smart Signage. Under such circumstances, we intend to conduct a comprehensive review of the present and future of the convergence content and Smart Signage-related industries, and the related policies, as representative media of convergence, and to hold in-depth discussions on the future content consumption environment that will be formed by those industries and policies at this session.
    Topics of interest include, but are not limited to, the following:

    * Convergence content and services
    * Content business ecosystem
    * Policy in convergence content
    * Business and management issues in convergence content
    * CT (Cultural technologies)
    * Broadcasting services and content
    * Game and mobile applications and services
    * Digital media or social network service user
    * Security and privacy
    * Smart Signage usage and BM models
    * Smart Signage-based ad service models and their usage
    * Legal system for Smart Signage invigoration
    * Standardization of Smart Signage technologies


  • Special Session 09: Convergence Technologies for IoT and Big Data.

    Jin-Ho Ahn, School of Electronic Display, Hoseo University (jhahn@hoseo.edu)

    The Internet of Things (IoT) is already here and affecting our everyday lives. Multiple machines, devices, sensors and appliances connected to the Internet through multiple networks are providing us with innovative new services. In addition to IoT infra technologies and new applications of this area, there are increasing needs to better index, store, process and analyze big data generated from that environment, because data sets collected by these objects will grow in size explosively, as the number of objects participated in IoT increases. Therefore, the goal of this session is to provide an opportunity for exchanging ideas and information on current studies, challenges, research results, system developments, and practical experiences in IoT, big data and convergence technologies for these fields.
    Particular areas of interest but not confined to include:

    * Hardware/software infrastructure for IoT
    * Models and tools for IoT and big data
    * Services and application for IoT and big data
    * Security and privacy for IoT and big data
    * Information-sensing mobile devices
    * Mobile communications and wireless networks
    * Visualization for big data
    * Big data analytics and social media
    * Cloud and grid computing
    * Big data applications: Internet search, Bioinformatics, Business informatics, etc


  • Special Session 10: Business Process Intelligence and its Application.

    Young Sik Kang, School of Business Administration at Myongji University (yskang@mju.ac.kr)

    Business Process Intelligence (BPI) is an area that spans process discovery, conformance checking, process mining, predictive analytics and many other techniques that are all gaining interest and importance in industry and research. BPI refers to the application of various measurement and analysis techniques in the area of business process management. In practice, BPI is embodied in tools for managing process execution quality by offering several features such as analysis, prediction, monitoring, control, and optimization.
    The goal of this session is to provide a better understanding and a more appropriate support of company processes at design time and the way they are handled at runtime, focusing on processes in isolation as well as the interplay between many parallel processes both within and between companies.
    We aim to bring together practitioners and researchers from different communities such as business process management, information systems research, business administration, software engineering, artificial intelligence, process mining, and data mining who share an interest in the analysis of business processes and process-aware information systems. The session aims at discussing the current state of ongoing research and sharing practical experiences.
    Particular areas of interest but not confined to include:

    * Mining of business processes from event logs
    * Multi perspective process mining
    * Predictive analytics
    *Mining of non-process aware systems/event streamsPublic-private partnership or cooperation
    * Recommender systems
    * Machine-learning and business processes
    * Simulation of business processes
    * SAP ERP Analytics

    Applications of such analysis techniques and case studies in:

    * Performance Measurement of business processes
    * Conformance and risk management for business processes
    * Process discovery
    * Monitoring of business processes
    * Static and dynamic optimization
    * Process Mining Methodology and its Extension
    * Dynamic composition of business processes


  • Special Session 11: Social media analytics and agent-based modeling for managerial decision making.

    Seong Wook Chae,Department of Business Administration at Hoseo University (swchae@hoseo.edu)

    Social media is becoming an integral part of life, online as social websites and applications proliferate. The amount of information seen during a single day gives a more startling indication of social media*s enormous influence. Therefore, the main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the field of business analytics and agent based modeling for social media. Especially, this workshop is interested in how those analytics and simulation techniques can be applied to complicated managerial decision making issues in the context of social media environment which is a newly emerging business research area. Examples of techniques include sentiment analysis/opinion mining, social network analysis, trend analysis, topic modeling, agent based modeling, many intelligent techniques like neural network, fuzzy logic, and other data mining techniques etc. Any papers with empirical analyses adopting Structural Equation Modeling and/or behavioral research models are also encouraged to submit to this workshop.
    Possible topics of interest may include (but are not limited to):

    * Social media analytic challenges
    * Information overload/bias/diffusion in social media
    * Behavioral (agent based) modeling in social media
    * Reality mining in social media
    * Social media metrics
    * Social media return on investment
    * Collaborative filtering in social media
    * Earned media management and prediction
    * Creativity/open innovation and social media
    * Search Engine Optimization and Social Media Optimization
    * Social commerce and ecommerce
    * Virtual team and social network
    * Emotion and decision making
    * Agent based modeling for business cases


  • Special Session 12: Social media and Digital environment.

    Lee, Hyoung-Yong,School of Business, Hansung University, Korea (leemit@hansung.ac.kr)

    Social media describes the collection of web and mobile-based technologies that expand the scope of the Digital environment via social networks and that enable individuals, groups and communities to exchange, communicate and share information, to collaborate. In Digital environment, social media are digitized content (text, graphics, audio/video) that can transmitted and proliferate over multiple networks and digital devices. Also, to analyze and crate knowledge in Digital environment is our augmented range, which include collective intelligence, crowd sourcing, and online communities supported by social media for work, learning, socializing, economic and/or political processes, and/or that address theory, design, practices, use or evaluation of such social media use.
    Topics of interest include, but are not limited to, the following:

    * Social network services
    * Collective intelligence
    * Crowd: Structure, role, and identity
    * Ethical issue of Digital environment
    * Social media and learning
    * Online communities
    * Digital environment technologies


  • Special Session 13: Regulatory Strategy for Management of Technology.

    Ji Min Park,American Law Department at Hallym University of Graduate Studies (jmpark@hallym.ac.kr)

    In a market-based economy, private companies seek to serve the best interests of themselves, sometimes paying little attention to the social goals such as social equity and environmental protection. Private actor*s pursuit of pure self-interest creates a need for government to intervene. Thus, governments intervene in business and infringe on autonomy of management decision making through regulations. Government invasion of management occurs on every front, largely three stages of a company*s activities: the planning, acting, or output stages (Coglianese & Lazar 2003). In other words, governments decide what should be produced, how it should be produced, who should produce it, or where it should be produced.
    Although the key element in making a company work and succeed is the entrepreneurship or entrepreneurial nature of the private enterprise system, regulations easily dilute the motivating elements for companies. In addition, arbitrary changes in regulatory policy and inflexibility in enforcing regulations deprive companies of their ability to cope with problems or to plan for the future. As Management of Technology provides companies with new opportunities to leap forward, it is an opportune time to propose regulatory strategy to help business grow for both private and social goals.
    Particular areas of interest but not confined to include:

    * Regulatory Strategy
    * Management of technology
    * Social goals and entrepreneurship
    * Private management and public goals
    * Effects of rulemaking on private actors
    * Market-based regulation
    * Framing policy instrument choice
    * The role of regulations for management


  • Special Session 14: Creative Tourism.

    Choongseok Lee,Korea Polytech University (CLee@kpu.ac.kr)

    Tourism and tourist industry have been chaned and and are being changed dramatically by information technologies, Internet, and Internet of tings (IoT), big data and so on.
    This session will provide a forum for researchers to discuss the design, use and impact of creative tourism in various contexts.
    In general, topics of interest for this session include:

    * Business Model of creative tourism
    * Supply chain and value chain for creative tourism
    * Technologies, applications and business issues related to the emergence of the ICT and related technologies in creative tourism
    * Empirical studies
    * Case studies
    * Prototype development


  • Special Session 15: Intelligent Decision Making and Consensus.

    Francisco Chiclana, De Montfort University, U.K (chiclana@dmu.ac.uk)
    Francisco Javier Cabrerizo, UNED, Spain (cabrerizo@issi.uned.es)
    Yucheng Dong, De Montfort University, U.K (ycdong@scu.edu.cn)
    Zeshui Xu, Sichuan University, Chengdu, China (xuzeshui@263.net)
    Enrique Herrera-Viedma ,Granada University, Spain (viedma@decsai.ugr.es)

    Intelligent decision making processes are developed by automatic decision-making systems that support individual or organisational decision making processes using different Information Technologies (as the Web and social networks) and Artificial Intelligence tools (as Computational Intelligence tools). The intelligent decision making processes involve the use of preference modelling and consensus processes. The preference modelling deals with the representation and modelled of the preferences provided by the experts in the problems. The fuzzy logic is a computational intelligence tool that provides an adequate framework to deal with the uncertainty presented in the user opinions. The fuzzy preference modelling has been satisfactory applied in intelligent decision making. On the other hand, consensus is an important area of research in intelligent decision making. Consensus is defined as a state of mutual agreement among members of a group where all opinions have been heard and addressed to the satisfaction of the group. A consensus reaching process is a dynamic and iterative process composed by several rounds where the experts express, discuss and modify their preferences.
    The objective of the proposed session is to highlight the ongoing research on intelligent decision making, fuzzy preference modelling and consensus processes under uncertainty. Focusing on theoretical issues and applications on various domains, ideas on how to solve consensus processes in intelligent 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:

    * Fuzzy preference modelling in intelligent decision making
    * Intelligent decision making system applications
    * Consensus in fuzzy multi-agent decision making
    * New models of fuzzy preference modelling
    * Intelligent decision making system for big data
    * Intelligent decision making in Web 2.0 frameworks
    * Intelligent decision making in presence of incomplete information
    * Aggregation of preferences
    * Intelligent decision making in dynamic contexts


  • Special Session 16: Soft computing methods in quantitative management and decision making processes.

    Florin Gheorghe Filip,Romanian Academy, Romania (ffilip@acad.ro)
    Ioan Dzitac,Agora University of Oradea & Aurel Vlaicu University of Arad, Romania (rector@univagora.ro, ioan.dzitac@uav.ro)

    Humans have a remarkable capability to reason and make decisions in an environment of imprecision, uncertainty and partiality of knowledge, truth and class membership. It is this capability that is needed to achieve human-level machine intelligence. Achievement of human-level machine intelligence is beyond the reach of existing Artificial Intelligence (AI) techniques and more of these are based on fuzzy sets theory (L.A. Zadeh, 1965) and fuzzy logic.
    The term Soft Computing (SC) was coined by Lotfi A. Zadeh in the early 90*s. In according with Zadeh*s definition, Soft Computing is based on Fuzzy Logic, Neural Networks, Support Vector Machines, Evolutionary Computation, Machine Learning and Probabilistic Reasoning. Soft computing can deal with ambiguous and noisy data. Soft computing is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for Soft Computing is the human mind. AI and Computational Intelligence based on soft computing 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 and decision making, 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:

    * Ant colony optimization algorithms
    * Artificial intelligence methods for web mining
    * Bayesian networks and decision graphs
    * Computational intelligence methods for data mining
    * Decision support systems for quantitative management
    * Decision making with missing and/or uncertain data
    * Fuzzy and neuro-fuzzy modelling and simulation
    * Fuzzy numbers applications to decision making
    * Fuzzy-sets-based models in operation research
    * Knowledge Discovery in Databases
    * Machine learning for intelligent support of quantitative management
    * Neural networks in decision making tools
    * Smarter decisions
    * Support Vector Machine in SC applications.


  • Special Session 17: NeuroManagement - NeuroMarketing.

    Felisa M. C車rdova, University of Santiago de Chile, Santiago, Chile (felisa.cordova@gmail.com)
    Hern芍n D赤az, University of Santiago de Chile, Santiago, Chile
    Ana Titos, University of Granada, Spain

    Human decision making systems can depend on many factors, some of them are very rooted in ancestral phylogeny and some others are the result of our present history life and depend on our trained or dynamic changing preferences.
    Trying to engineer on neurocognitive processes assumes the knowledge about those elements or components upon we want to (re)engineer. Until now many of these components has been revealed thanks to the new technology involving brain stimulation and scanning, functional brain images, image analysis and brain lesions.
    One of the spin-off consequences of the development of neuroeconomy, the neurobiology of decision making, was neuromarketing, the use of electrophysiological devices to capture human physiological activity during buying decisions to learn about their preferences, probabilities of choice and neural processes involved. Until know it has been established that few seconds before a risky decision specific nuclei of the brain start evaluating actual conditions until surpass a threshold since where it is possible to predict the following output.
    We present here a research joint venture that associate neurocognitive research of human behavior with neuromarketing empirical findings on decision making. The first objective of this enterprise is to develop novel and diverse ways to analyze, visualize and interpret human physiological data with the purpose to characterize functional processes of the brain at different timescales during performing different tasks.
    While the hitherto framework of neuromarketing has been the stimulus-response paradigm, the neurocognitive engineering approach is in search of answers in the mid- and long term behavioral change timescale. It means that is deeply interested in process like teaching and learning as central processes and procedures of human communication, education and culture.


  • Special Session 18: A New Energy Approach to Building and Managing Intelligent Sustainable Hybrid Energy Systems (SHES).

    Franco F. Yanine, Technical University Santa Mar赤a, Valpara赤so, Chile (fyanine@uc.cl)
    Felisa M. C車rdova, University of Santiago de Chile, Santiago, Chile (felisa.cordova@gmail.com)
    Enrique L車pez, University of Concepci車n, Concepci車n, Chile
    Maria do Carmo Duarte Freites, Federal University of Parana, Curitiba, Brazil

    A new research on SHES approaches the intelligent microgrid concept differently, from a systematic and cybernetics standpoint, much like a complex living organism. The system comprises the smart microgrid which is to be designed and configured as a complex adaptive system (CAS), a living organism that is coupled with a sustainable block? (a group of energy consumers somewhere, whether residential or industrial/commercial) and the utility grid. The key to a building affordable, highly efficient, versatile and flexible SHES lies not in the microgrid performance alone or in a particular technology but in the interrelation and interaction (timely information being one of them) among the three systems involved: the smart microgrid, the sustainable block and the utility grid. Homeostasis means maintaining relatively stable internal conditions of a living organism despite continuous environmental changes. Homeostasis aims for a dynamic, adaptive, self-regulated steady-state, which is maintained by the contribution of all organ systems which comprise the living organism. Homeostatic control (HC) mechanisms involve a continuous monitoring and regulation of all the factors that can be changed (variables), along with communications necessary for the monitoring and regulation. Thus the homeostatic control system for microgrids is based on the nervous and endocrine systems actions, and emulates such systems and their operation. The HC system effectively regulates energy supply and demand, managing communications via set-points and specific command and functions of the algorithms behind the control mechanisms: receiver (sensor) 每 environmental 每 load monitoring responds to stimuli (e.g. something that causes changes in the controlled variables). The control center determines the set point which must remain variable - receives input from the receiver - determines appropriate responses. The effector receives output from the control center and provides the means to respond via power supply to the loads according to specific HC strategies. Here the effector responds, either by reducing (negative feedback) or increasing power supply or delivering credit to a consumer that has restrained himself/herself in energy consumption to allow others who consume more to have more energy available in the system for supply. Energy balance is tightly regulated by energy demand and energy expenditure versus power supply, which is critical for the individual consumer and for the community (whether it is a residential community or an industrial/commercial park) as a whole. Because changes in environmental conditions and sometimes in operations and load conditions are somewhat unpredictable, the HC system responsible for the regulation of energy intake, storage and expenditure in the microgrid system must be able to adapt quickly to such changes. Peripheral units such as battery bank, Fuel-Cell systems and microturbines, along with the central monitoring system are in constant cross-communication to ensure energy supplies for the different loads against periods of energy scarcity in the system (low levels of energy stored) or irregular, intermittent energy generation by the renewable energy sources. It is through the stored energy, and a growing energy efficiency and thriftiness on the part of consumers that the benefit for the whole occurs, especially during periods of scarcity due to low renewables production or dwindling power supply from the utility grid.
    The role of the homeostatic regulator? emulates the hypothalamus function in the human body. The hypothalamus is an area of the brain with high levels of plasticity and adaptability, and it is able to adapt quickly and very effectively to changes in the environment and to changes in other organs. Finally the role of the energy storage is vital to our model in that we postulate that for the system to achieve higher levels of energy efficiency and thriftiness, the size of the energy storage system must be augmented, oversized to the 50% installed capacity level, unlike the industry norm which ranges between 25% and 30%. This will create the sensation of having ※enough back-up§ just like the body builds fat deposits to feel safe and secure and people stack large amounts of non-perishable foods and beverages in the cellar. The benefit drawn from this augmented storage strategy built into the microgrid albeit its larger investment cost (50% of installed power plant capacity) is similar to the behavior observed in humans when they are able to restraint themselves in their consumption and to be thriftier and more efficient in the use of resources knowing that they have enough back-up to fall on to. It is the same feeling that you get when you know you have enough in the refrigerator or in the cellar, and therefore can afford to go without food for a while or to forget about meals and be less apprehensive about energy intake. There are several important functions and indices operating in the HC model for microgrid, such as the energy savings index, the exergy index which shows the available capacity of the system to supply energy and the quality of such energy in the system. There is also Grid_frac which tells the users and the microgrid remote operator how much energy is being drawn by each user from the grid; it is a measure of exergy and efficiency. This strategy allows the user to be thriftier and more efficient in spite of having more in storage. In summary, our model of energy homeostasis proposes that long-term higher levels of energy storage will trigger positive changes in behavior and thus will make the whole HC system of the microgrid more effective. This is quite similar to what occurs in the human body with signals such as insulin and leptin levels which influence the neuronal activity of central effector pathways that serve as controllers of energy balance. Because these hormones circulate at concentrations that are proportionate to fat mass (energy storage) and energy balance, a change in body fat (energy storage level) stored is sufficient to alter the delivery of these hormones to the brain inducing the central effector pathway responses that promote the return of adiposity (normal levels of energy intake or consumption) to its original value. The bottom line result with the HC approach for microgrids is that at least 30% savings to the consumer in his/her energy bill (compared to the no-project alternative) will be realized. This result stems from a double benefit: a very limited use of the utility grid, of not more than 10 % on average monthly (the rest is all provided by the microgrid), and the higher energy efficiency and thriftiness in energy supply and consumption associated with the HC and energy management strategies incorporated in the microgrid.


  • Special Session 19: Special Session Proposal of Data Acquisition and Management for Traceability Analytics (IDAMTA).

    Jing He, Victoria University, Australia (jing.he@vu.edu.au)
    Bo Mao, Nanjing University of Finance and Economics, China(bo.mao@njue.edu.cn)
    Hai Liu, School of Computer, South China Normal University, China(liuhai@scnu.edu.cn)

    1. Overview
    In the era of wireless technology, robotics, web service, there are many computing technologies being introduced. With the recent development and progress of IoT (Internet of Things), it is possible to get information about how a system is operation and its real-time status in details. For example, RFID can track the distribution of goods, different sensors can monitor the environment, and GPS can send the location and time back. Based on the information, we could have a log for the monitored system and implement the trace-ability analysis. Trace-ability is the ability to verify the history, location, or application of an item. It is especially critical for some industries such as food processing, logistics, supply chain and e-business. The two key technologies for the trace-ability analysis are data acquisition and management. In the age of cloud computing, they are two promising fields. Although there are several solutions already in place, many challenges remain to be investigated and tackled.
    The purpose of this special session is to not only discuss the existing topics in data acquisition and management for traceability analysis, but also focus on the new rapidly growing area from the integration of big data analytics and traceability analysis for significant mutual promotion. We intend to discuss the recent and significant developments in the general area and to promote cross-fertilization of techniques. The participants in this special session will benefit as they will learn the latest research results of data acquisition and management of IoT and big data analytics based trace-ability system, as well as the novel idea of merging them.
    2. History of this workshop
    We have successfully organized one workshop at the 2nd ITQM conference at Moscow. Seven authors have shown up and given the presentation at Higher School of Economics and one special seesion at the 3rd ITQM conference at Brazil.
    3. Goal
    The special session is interdisciplinary and provides a platform for researchers, industry practitioners and students from engineering, sociology, computer science, information systems share, exchange, learn, and develop new research results, concepts, ideas, principles, and methodologies, aiming to bridge the gaps between paradigms, encourage interdisciplinary collaborations, advance and deepen our understanding of IoT, big data analytics, traceability and the related data management method.
    There are two major topics of interest for this workshop: (1) Traceability data acquisition, (2) Data management and mining for the generated IoT data. Comprehensive tutorials and surveys are also expected. The general topics include, but are not limited to:
    Traceability Data Management
    o Visualization of IoT based Traceability system
    o Intelligent Data Fusion and Aggregation
    o Storage Management Technologies
    o Deep Learning
    o Big (Sensor) Data
    o Pattern Discovery
    o Multiple Representation Structure
    o Spatiotemporal Data Management and Analysis
    IoT based Traceability Data Acquisition
    o RFID Related Technologies
    o Wireless Sensor Network
    o Online Quality Estimation
    o Data Acquisition based on Smart Phones
    o User Analysis based on Social Network
    More specially, details about recommended topics include, but are not limited to, the following:
    * Advanced Cloud Computing Solutions for Traceability Systems
    * Agent-based approaches to Cloud Services for Traceability Systems
    * Self-Organizing Agents for Service Composition and Orchestration in Trace-ability Systems
    * Self-service cloud and self-optimization in Traceability Systems
    * Trust in Cloud computing for Traceability Systemsg
    * Trace-ability Systems related Workflow Design and Optimization
    * Emerging Areas of Trace-ability Applications in the frontier of web and cloud computing
    * Advanced Cloud Computing Solutions for Traceability Systems
    * Agent-based approaches to Cloud Services for Traceability Systems
    * Self-Organizing Agents for Service Composition and Orchestration in Traceability Systems
    * Self-service cloud and self-optimization in Traceability Systems
    * Cloud resource allocation approaches
    * Privacy Preserving in Cloud Computing for Traceability Systems
    * Trust in Cloud computing for Traceability Systems
    * Trace-ability Systems related Workflow Design and Optimization
    * Advanced IT Solutions for Traceability Systems
    * Agent-based approaches to ICT Services for Traceability Systems
    * Self-Organizing Agents for Service Composition and Orchestration in Traceability Systems
    * Self-service cloud and self-optimization in Traceability Systems
    * Information resource allocation approaches
    * Privacy Preserving for Traceability Systems
    * Trust in Cloud Computing for Traceability Systems
    * Trace-ability Systems related Workflow Design and Optimization
    * Emerging Areas of Traceability Applications in the frontier of web and cloud computing
    4. Special issues The selected paper will be recommended to International Journal of Information Technology & Decision Making (SCI) and the journal of computers (EI).
    5. Short Bio for co-chairs
    Dr. Jing He Victoria University
    Dr. Jing He is currently a full Professor in the College of Engineering and Science, Victoria University. She has been awarded a PhD degree from Academy of Mathematics and System Science, Chinese Academy of Sciences in 2006. Prior to joining to Victoria University, she worked in University of Chinese Academy of Sciences, China during 2006-2008. She has been active in areas of Data Mining, Web service/Web search, Spatial and Temporal Database, Multiple Criteria Decision Making, Intelligent System, Scientific Workflow and some industry field such as E-Health, Petroleum Exploration and Development, Water recourse Management and e-Research. She has published over 40 research papers in refereed international journals and conference proceedings including ACM transaction on Internet Technology (TOIT), IEEE Transaction on Knowledge and Data Engineering (TKDE), Information System, The Computer Journal, Computers and Mathematics with Applications, Concurrency and Computation: Practice and Experience, International Journal of Information Technology & Decision Making, Applied Soft Computing, and Water Resource Management. She received research fund from ARC early career researcher award (DECRA), ARC discovery, ARC Linkage, National Science Foundation of China, Youth Science Fund of Chinese Academy of Sciences, Grant-in aid for Scientific Research of Japan. She served on three program committees of international conferences: International Conference on Computational Science (ICCS), The IEEE International Conference on Data Mining (ICDM), and International Symposium on Knowledge and Systems Science (KSS), as well as the workshop co-chair on APWeb 2008, WI 2009, MCDM 2009. In addition, she has been serving as external reviewers for several international journals and conferences, such as Management Science, The Computer Journal, IEEE Transaction on Systems, Man, Cybernetics, International Journal of Information Technology and Decision Making, Journal of Management Review (in Chinese), Decision Support System, Science (in China), ICDE, ICCS, ICDM, KSS, WISE, HIS, APWeb etc.
    Dr Bo Mao, Nanjing University of Finance and Economics
    Dr. Bo Mao is currently an Associate Professor Nanjing University of Finance and Economics, China. He has been awarded a PhD degree from Royal Institute of Technology-KTH, Sweden in 2012. He has been active in areas of 3D City model generalization, Online Visualization, Data Mining, Spatial and Temporal analysis, and some industry field such as Food trace-ability system and e-business. He has published over 30 research papers in refereed international journals and conference proceedings including ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS J), Computers, Environment and Urban Systems (CEUS), Science China Earth Sciences, World Wide Web Journal (WWWJ), International Conference on Geographic Information Science (GIScience), ACM conference on Recommender systems (RecSys). He received research fund from National Science Foundation of China and Jiangsu Doctor Convergence Program. He served on program committees of International conference on Advanced Data Mining and Applications (ADMA). In addition, He has been serving as external reviewers for several international journals and conferences, such as ISPRS J, CEUS, IJGIS, ADMA etc.
    Hai Liu, School of Computer, South China Normal University
    Dr. Hai Liu is now a researcher at south china normal university. My research interests include Machine learning, Data mining, Ontology Engineer (Description Logic), Classification Clustering, Matrix Factorization, Topic modeling, and Recommender Systems.
    Education:
    M.S.2001.Computer Science and Engineering,South China Normal University.
    Ph.D.2010.Computer Science and Engineering,SUN YAT-SEN University
    Research Field:
    Description Logic, Data Mining (Machine Learning), Personized Recommendation.


    Workshop:

  • Workshop 01: The Ninth International Workshop on Computational Methods in Energy Economics (CMEE 2016)

    Lean Yu, School of Economics and Management, Beijing University of Chemical Technology, China (yulean@amss.ac.cn)
    Kaijian He, School of Economics and Management, Beijing University of Chemical Technology, China (kaijian.he@my.cityu.edu.hk)
    Ling Tang, School of Economics and Management, Beijing University of Chemical Technology, China (tangling@mail.buct.edu.cn)

    As is known to all, energy economics is a subfield of economics that focuses on energy relationships as the foundation of all other relationships. The field can arise from a number of disciplines, including economic theory, financial economics, computational economics, statistics, econometrics, operational research and strategic modeling. A wide interpretation of the subject includes, for example, issues related to forecasting, financing, pricing, investment, development, conservation, policy, regulation, security, risk management, insurance, portfolio theory, taxation, fiscal regimes, accounting and the environments. In these listed issues there are a large number of computational problems to be solved for the energy systems, particular for energy risk measurement and management. This will be the eighth workshop for such a subject that provides a premier and open forum for the dissemination of innovative computational methods as well as original research results in energy economics and energy risk management.
    In order to provide an academic exchange platform, the First International Workshop on Computational Methods in Energy Economics (CMEE 2007) was held in Beijing on May 27-30, 2007. Subsequently, the Second, Third, Fourth, Fifth Sixth and Seventh International Workshop on Computational Methods in Energy Economics (CMEE 2008, CMEE 2009, CMEE 2010, CMEE 2011, CMEE 2012, CMEE 2013 and CMEE 2015) were held in Nanjing, Sanya, Huangshan, Kunming, Harbin, rio De Janeiro (Brazil) on June 27-30, 2008, April 24-26, 2009, May 28-31, 2010, April 15-19, 2011, June 24-26, 2012, May 16-18, 2013, and July 21-24, 2015. To promote the idea-exchange and discussion of this field, the Eighth International Workshop on Computational Methods in Energy Economics (CMEE 2016) will be held in Asan, Korea, August 16-18, 2016. The organizers solicit all interested academic researchers and industrial practitioners to submit their recent research results to this workshop within the scope of the following topics.
    The workshop will provide an open forum for research papers concerned with the computational problems on energy economics and energy risk management, including economic and econometric modeling, computation, and analysis issues in energy systems. The workshop will focus on, but not limited to, the following topics:

    * Forecasting models for energy prices (oil, coal, gas, electricity)
    * Pricing models in energy markets (mean reversion, jump diffusion)
    * Investment analysis models in energy projects (portfolio theory)
    * Econometric modeling for energy demands
    * Energy and environment policy modeling
    * Modeling strategic behavior for energy security
    * Hybrid energy-economy models for energy policy simulation
    * Statistical analysis of energy cost, energy consumption and economic growth
    * Energy risk management (risk measurement, hedging strategy and instruments)

    All the submissions must be English with Microsoft Office Word97 or above version and papers must not exceed 5 pages in length, when typeset using the Elsevier*s Procedia Computer Science template. All papers must be submitted to workshop chair via email: yulean@amss.ac.cn(Lean Yu), kaijian.he@my.cityu.edu.hk (Kaijian He) and tangling@mail.buct.edu.cn(Ling Tang) with a subject ※CMEE 2016 Submission§.
    Authors of accepted papers must guarantee that their papers will be presented at the conference. Accepted papers will be published in the conference proceedings by Elsevier in their Procedia Computer Science series. Selected best papers will be published in special issues of high quality journals (Int. J. of Information Technology and Decision Making, Annals of Data Science, and others that are currently under negotiation).

  • Workshop 02: Workshop on Applied Soft Computing

    Maria Augusta Soares Machado, Ibmec-RJ and Fuzzy Consultoria Ltda, Brazil (mmachado@ibmecrj.br;fuzzy-consultoria@hotmail.com)
    Luiz F. Autran M. Gomes, Ibmec-RJ, Brazil ( autran@ibmecrj.br)

    Human beings are able to handle very complex processes based on inaccurate or approximate information. The strategy adopted by human operators is also imprecise nature and generally can be expressed in linguistic terms. If a human operator is able to coordinate its action strategy as a set of rules like if...then, this can be implemented in a computer algorithm. The characteristic of "intelligence" is usually attributed to humans. More recently, many products and items also claim to be "intelligent". Intelligence is directly linked to the reasoning and decision making. Many areas of knowledge have the opportunity to verify the effectiveness and results of new discoveries, or the behavior of a process when some of the variables that comprise it are changed or deleted before being adopted definitively. The soft computing field encompasses the study of neural networks, fuzzy logic, evolutionary and nature-inspired computing, and machine learning. Its most striking benefits are usually related to problems for which no satisfactory solution could be found by directly using "traditional" paradigms also based upon rigorous and firmly established mathematical results. Plenty of examples of very successful applications can be found, for instance, in the fields of stochastic global optimization and pattern recognition, to cite a few. In this fashion, its scope includes problems related to logic, reasoning, planning, natural language understanding, rule based machine learning, business, finance, commerce, marketing, economics, decision making, data mining, fuzzy inference systems, neural networks, neural pattern recognition, clustering, genetic algorithms, probabilistic and possibilistic reasoning and all related machine learning methods.
    Topics of interest include, but are not limited to:

    * Soft computing methods and applications related to Big Data;
    * Pattern recognition;
    * Signal processing;
    * Problems related to the higher cognitive functions;
    * Applications of fuzzy integrals;
    * Iphone applications aimed at decision making;
    * Simulated Annealing;
    * Applications of Fuzzy Logic;
    Submission of papers to this Workshop: Original papers are invited from prospective authors with interest on the related areas. Submitted papers must not substantially overlap papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Papers should be at most 8 pages including the bibliography and well-marked appendices. Papers must be received by the submission deadline of February 28, 2016.

  • Workshop 03: 2nd Workshop on Scientific data analysis and decision making

    Dengsheng Wu, Institute of Policy and Management, Chinese Academy of Sciences, China (wds@casipm.ac.cn)
    Yuanping Chen, Computer Network Information Center, Chinese Academy of Sciences, China (ypchen@cashq.ac.cn)
    Wenbin Jiao, Computer Network Information Center, Chinese Academy of Sciences, China (wbjiao@cashq.ac.cn)

    As E-science has emerged as a persistent and increasingly large part of the research enterprise, scientists are exploring new roles, services, staffing, and resources to address the issues arising from this new mode of research. Scientists use computer modeling and simulation programs to test and produce new theories and experimental techniques, often generating and accumulating vast amounts of data. Ideally, that data could be shared with other scientists, for reuse and re-analysis, ultimately speeding up the process of scientific discovery. The collection and utilization of scientific data are the two major features that characterize e-Science. The scientific data are generated by different aspects and departments in the management activities of research institutions, and are decentralized-managed and separated-stored, which generates the difficult to share and manage the scientific data. Furthermore, the global sharing of data has promoted interdisciplinary teamwork on complex problems and has enabled other researchers to use data for different purposes. 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 science and decision analysis for e-science. The workshop aims to create a communication platform for researchers to share the recent and significant developments in the general area.
    Topics of interest include, but are not limited to, the following:

    * Metadata standard of scientific data
    * Scientific data quality analyzing
    * Scientific data integration and sharing
    * ETL process for scientific data
    * Scientific data visualization
    * Decision analysis modeling from scientific data
    * Network analysis from scientific data
    * Bibliometrics analysis from scientific data
    * Scientometrics from scientific data

  • Workshop 04: Credit Evaluation and Management

    Zongfang Zhou, University of Electronic Science and Technology of China, China (zhouzf@uestc.edu.cn)

    Since the advent of the era of big data, various information technologies, such as semantic learning, collaborative filtering, and probabilistic models, have been significantly affected our lives and changed way we used in credit evaluation and management before. These data driving technologies, which can not only integrate different aspects of credit, but also provide more comprehensive interfaces for credit management embody a great advantage in both accuracy and efficiency compared with traditional approaches.
    This workshop focuses on the issues in contemporary method and technologies of credit evaluation and management, and aims to create a communication platform for researchers to share the recent and significant developments in the general area. Topics of interest in this workshop include, but not limited to:

    * Fundamental understanding of credit
    * Correlation and evolution of credit risk
    * Credit measurement
    * Credit risk integrated management
    * Multiple criteria decision making in a uncertain context
    * Metric system for credit evaluation
    * Credit rating
    * Portfolio management

  • Workshop 05: Outlier Detection in Financial Data Streams

    Aihua LI, Central University of Finance and Economics, China (aihuali@cufe.edu.cn)
    Zhidong Liu, China (liu_phd@163.com)

    Data stream is one of the important data types in the financial sector, and it is with the following characteristics, such as arriving quickly, unstable, huge and so on. Traditional analysis methods and theories cann't meet the requirements of financial data stream analysis due to these characteristics. This workshop focuses on how to detect abnormal pattern in data streams especially in finiancial data streams. In additon, theoritical system and methods of outlier dection would be needed to be proposed. This topic includes outlier dection theory, method and application for financial data stream based on domain knowledge, outlier dection for data stream and empirical analysis.
    The topics and areas include, but not limited to:

    * Outlier detection based on classification method
    * Outlier detection based on clustering method
    * Outlier detection based on domain knowledge
    * Data preprocessing method for data streams
    * Domain knowledge and risk management in finance
    * Data mining and knowledge discovery in finance
    * Outlier detection method in other field
    * Quantitative Management and decision making in fianc谷

  • Workshop 06: Exploration of Knowledge Intensity: Analytics and Empirical Analysis

    Jinyoung Min, Chosun University, KOREA (saharamin@chosun.ac.kr)
    Heeseok Lee, Korea Advanced Institute of Science and Technology, KOREA (hsl@business.kaist.ac.kr)

    The present realm of knowledge management in contemporary organizations has evolved from codifying and applying existing knowledge for efficiency to finding insights for the unforeseen opportunities. The proposed workshop aims to deliver insights on how organizations have dealt with this emerging issue in various fields using diverse methodologies. Possible studies will investigate the various fields such as IT infrastructure, entertainment, security, etc. For example, the studies in this workshop may deal with the following subjects.
    1) The investigation of the relationship between organizations* spending on IT infrastructure and organizational performance: through longitudinal and econometrical analysis.
    2) The exploration of fan responses to celebrities* social media postings: through empirical and text analysis.
    3) The way to alleviate employees* fatigue on security issues: through psychological investigation.
    The topics of interest for this workshop will shed light on how to manage and analyze external and internal knowledge intensity of an organization for sustainable and prosperous businesses.

  • Workshop 07: Session on Big Data and Data Analysis

    Sang-Tae Han, Department of Applied Statistics, Hoseo University, South Korea (sthan@hoseo.edu)
    Hyuncheol Kang, Department of Applied Statistics, Hoseo University, South Korea (hychkang@hoseo.edu)
    Kyupil Yeon, Department of Applied Statistics, Hoseo University, South Korea (kpyeon1@hoseo.edu)
    Seongyong Kim, Department of Applied Statistics, Hoseo University, South Korea (yaba96@hoseo.edu)
    Hyeuk Kim, Department of Applied Statistics, Hoseo University, South Korea (hkim@hoseo.edu)

    Big Data is a term that describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enables the capture, storage, distribution, management, and analysis of the information. Nowadays, Big Data settles in our life. All aspects of our life are producing increasingly large data streams in petabyte and exabyte scales daily. Big Data is transforming science, engineering, business, healthcare, and ultimately society itself. Big data analysis is the use of advanced analytic techniques for very large, diverse data sets from terabytes to zettabytes. Through better analysis of Big Data which become available, there is the potential for making the huge advances in many areas such as science, engineering, medicine, healthcare, business, and finance. This session aims to promote results obtained by researchers and practitioners. We solicit research, technical papers and industrial proposals in any aspect of Big Data. The submissions can be theoretical, practical and application oriented on the following session.
    Topics of interest include, but are not limited to, the following:

    * Classification
    * Clustering
    * Association Rule
    * Anomaly Detection
    * Text Mining
    * Collaborative Filtering
    * Network Analysis
    * Data Visualization
    * Application case studies

  • Workshop 08: Workshop on IoT Applications and their Value Creation in the Business

    Jae-Hyeon Ahn, KAIST Business School, Seoul, Korea (jahn@busines.kaist.ac.kr)
    Heeseok Lee,KAIST Business School, Seoul, Korea (hsl@business.kaist.ac.kr)

    The Internet of Things (IoT) is the network of physical objects that embedded with electronics, sensors, and all of things. These objects connected to the Internet which enables us to identify themselves to other objects and to collect and exchange data automatically. It is obvious that IoT is important changes not only in our lives but also in the industrial environment. Numerous IoT research and application projects have been studied by universities and in joint industry-university consortia in recent years. IoT applications in the business aspects are using IoT related technologies to improve manufacturing processes, create new service, offer an optimized infrastructure, reduce operational cost, collaborate for global IoT solutions, improve smart healthcare and smart agriculture, and so on. Using these applications, forming value creation in the business area is also important aspects. The business usage of IoT in coming years depends on how these value aspects will be improved.
    The workshop intends to bring people from academia and industry together to discuss, share experience on IoT applications and their value creations for business, and to address any related IoT topics.
    Topics of interest include, but are not limited to, the following:

    * Analysis of the successes, failures, winners, and losers in the IoT
    * Opportunities and challenges of wearable and embedded technologies
    * Devices Platforms, Sensors, Control and Actuators
    * Cognitive IoT
    * Big Data for IoT
    * Standards and Societal Impact
    * Social Internet of Things
    * Context and Location Aware Applications
    * IoT based e-Commerce
    * Innovative Applications

  • Workshop 09: Workshop on Privacy, Security, and controversies of IoT

    Jae-Hyeon Ahn, KAIST Business School, Seoul, Korea (jahn@busines.kaist.ac.kr)
    Heeseok Lee, KAIST Business School, Seoul, Korea (hsl@business.kaist.ac.kr)

    The Internet of Things (IoT) is not simply a computing concept but an emerging paradigm which promises to resolve many social and economic problems that the world is currently facing. Even experts estimate that IoT will consist of 30 billion objects to the Internet by 2020. It is obvious that IoT exercises a far-reaching influence on communication, community, environment, business, society, and many more. However this rapid growth of IoT requires great caution about the security risks and privacy concerns by users and suppliers. Also threats from outside are related to various field such as harming or stealing national security information, business trade secret, personal privacy or other valuable property. With great change of technology and pattern of usage, new social protocol related to IoT is required. Also these technology advances can cause other controversies such as job replacement by automation and computerization.
    The workshop aims to provide an opportunity for participants from academia, industry, government and other related parties to share the recent and significant developments on current topics.
    Topics of interest include, but are not limited to, the following:

    * Fault Tolerance and Survivability
    * Reliability, Safety, Security and Privacy
    * Detecting and mitigating insider threats
    * Security policy compliance research
    * Attack Strategies for IoT
    * Identity Management in IoT
    * Explorations of emerging issues related to the IoT
    * Social protocol change in IoT
    * Social controversy related to IoT

  • Workshop 10: High Performance Data Analysis

    Vassil Alexandrov, ICREA Research Professor in Computational Science at Barcelona Supercomputing Centre, Spain (vassil.alexandrov@bsc.es)
    Ying Liu, University of Chinese Academy of Sciences, China (yingliu@ucas.ac.cn)

    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.
    The 2016 workshop, which is the third in the series, aims at exploring emerging trends and focus on high performance data analysis. We welcome papers on all aspects of high performance data analysis, including, but not limited to:

    * Data processing exploiting hybrid architectures and accelerators (multi/many-core, CPUs, FPGAs)
    * Data processing exploiting dedicated HPC machines and clusters
    * Data processing exploiting cloud
    * High performance data-stream mining and management
    * Efficient, scalable, parallel/distributed data mining methods and algorithms for diverse applications
    * Advanced methods and algorithms for Big Data Visualisation
    * Parallel and distributed KDD frameworks and systems
    * Theoretical foundations and mathematical methods for mining data streams in parallel/distributed environments
    * Applications of parallel and distributed data mining in diverse application areas such as business, science, engineering, medicine, and other disciplines

  • Workshop 11: Intelligent Knowledge Management

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

    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.
    Topics of interest include, but are not limited to, the following:

    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
    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

  • Workshop 12: The Forth Workshop on Optimization-based Data Mining

    Yingjie Tian, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China (tianyingjie1213@163.com)
    Yong Shi, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China/University of Nebraska at Omaha, USA (yshi@unomaha.edu)
    Zhiquan Qi, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China (qizhiquan@gucas.ac.cn)

    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 thirty 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 200 scholars around the world have been actively working on the field of using optimization techniques to handle data mining and web intelligence problems. As the forth one at the ITQM conference series, this workshop intends to promote the research interests in the connection of optimization, data mining and web intelligence as well as real-life applications.

  • Workshop 13: The Third Workshop on Quantitative Finance (QF2016)

    Xianhua Wei, School of Economics and Management, University of Chinese Academy of Sciences, China (weixh@ucas.ac.cn)
    Guotai Chi, School of Business Management, Dalian University of Technology, China (chigt@dlut.edu.cn)
    Weixing Wu, School of Banking and Finance, University of International Business and Economics (wuweixing@hotmail.com)
    Yonghui Wang, Director-General of China QClub (wangyonghui@phfund.com.cn)

    Theme of this workshop is big data driven quantitative investment.
    Since Markowitz*s portfolio selection theory in 1950s, statistics and mathematics have been applied in finance and investment management. Lots of empirical studies showed that historical data analysis using proper mathematical models helped to test financial and economical theory, and improve investment performance in practice as well. Since the appearance of Black-Scholes model for pricing option, mathematics, information technology and finance have tended to infuse. The nature of complexity of financial instruments requires more sophisticated mathematical models and computer tools to extract information about risk and return from noisy data. Quantitative finance is a cross-disciplinary field relies on mathematical finance, intelligent methods and computer simulations to make trading, hedging and investment decisions, as well as facilitating the risk management of those decisions.
    This special workshop tends to promote the research interests both in academic community and industrial community in the connection of (i) mathematics and statistics, (ii) information technology, (iii) finance and economics.
    The expected full-day workshop is divided into two sessions: morning session for academic papers presentation and afternoon session for industrial practices exchange and discussion.
    Morning session
    The workshop calls for papers to the researchers and professors from universities and academic institutions in the above interface fields for their participation in the conference. The workshop welcomes both high-quality academic papers (theoretical or empirical) in the broad ranges of quantitative finance related topics including, but not limited to the following:

    Theory
    * Asset Pricing Theory
    * Portfolio Selection Theory
    * General Equilibrium Theory
    * Rational Expectation Theory
    * Term Structure Theory
    * Arbitrage Theory
    * Hedging and Trading Theory
    * Insurance and Actuarial Theory
    Applications
    * Portfolio Optimization
    * Asset Allocation
    * Asset Pricing and Valuation
    * Financial Time Series Forecasting
    * Credit Risk Modeling
    * Interest/Exchange Rates Determination
    * Financial Derivatives Pricing and Trading
    * Basel III, Solvency II and Risk Management
    * Emerging Markets Issues
    * Extreme Events and Volatility Modeling
    * Financial and Econometrics Modeling

    The workshop will accept 6-8 regular papers, and they will be presented at the workshop. Some good papers can also be accepted as posters.
    Afternoon session
    The special workshop provides a platform for exchanging thoughts, methods and models in portfolio management and security investments practices for the whole quantitative investment community.
    The workshop will invite those senior quantitative analysts, portfolio managers and fund managers from famous securities companies, fund management companies and asset management companies for their participation at the conference. The participants will discuss the development of quantitative asset management, share experience and lessons of their own practices, and also work on the solutions to domestic application problem of quantitative finance.
    The afternoon session will be arranged as a forum. 2-3 invited speakers will give their keynote speeches (to be decided) focusing on quantitative asset management. Free discussion follows after afternoon tea break.

  • Workshop 14: The first Workshop on Data Mining for Anti-Money Laundering

    Dr Peng Zhang, Alipay.com, China (hanyi.zp@alipay.com)
    Dr Feng Xue, Alipay.com, China (henry.xuef@alibaba-inc.com)
    Dr Ying Zhang, Alipay.com, China ( zhangying.zy@alipay.com)

    Money laundering poses severe threads to financial institutions. In China, most financial institutions have deployed their anti-money laundering solutions to prevent financial activities arranging from personal illegal gains to group drug trafficking and financing terrorism. However, existing anti-money laundering solutions are rule-based systems and therefore are not efficient enough especially for detecting unknown suspicious activities. Today, online payment businesses grow rapidly in China and money transfer becomes relatively convenient where money laundering patterns often evolve continuously in order to escape investigation. To address the fast-changing money laundering patterns problem, researchers are now focusing on using data mining and machine learning models to auto-detect the evolving patterns. This workshop aims to gather researchers and practitioners to discuss the recent progress on using data mining and machine learning methods for anti-money laundering. The topics cover but not limited to:

    * Data mining and machine learning models for anti-money laundering;
    * Graph data query, search and indexing on large money transfer networks;
    * Mining contextual information for money-laundering behavior;
    * Streaming and incremental data mining models;
    * Governance, Risk and Compliance for anti-money laundering;
    * Financial Crime Prevention, CDD and KYC.

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