Accepted Special Sessions

Special Session 01: Computer Supported Collaborative Decision-Making in The Digital Society: Methods, Tools, Systems, and Applications

Prof. F. G. Filip, The Romanian Academy, Bucharest, Romania, ffilip@acad.ro (https://acad.ro/cv/FilipF/FGF-CV-en.pdf)

Prof. Constantin Bala Zamfirescu, “Lucian Blaga”, University, Sibiu, Romania, zbc@acm.org (https://web.ulbsibiu.ro/constantin.zamfirescu/)

Prof. Cristian Ciurea, Academy of Economic Studies-ASE, Bucharest Romania, cristian.ciurea@ie.ase.ro (https://orcid.org/0000-0002-7327-0007)

In the context of new business models applied to the present-day enterprises and organizations, multi-participant decision-making activities have gained ever more traction. Over the latest decades, the vast majorities of decisions have been made by collaborative groups and collectivities, and even negotiation panels. The pandemic caused adaptation of the working style to a new situation. Recent trends for combining AI1 (Artificial Intelligence) with AI2 (Augmenting Intelligence of humans) within hybrid decision units can be noticed. Data collection, consensus building, solution selection, network and crowd working-based decisions and involving collaboratives robots in manufacturing and decision-making activities have been supported by modern Information and Communication Technologies (I&CT).

The session is meant to include papers that contain recent results obtained by research teams from academia and industry concerning but not limited to the following topics:
  • Network and crowd working methods and corresponding platforms.
  • Cobots, digital clones, and humans with augmented intelligence and their collaboration in hybrid decision units.
  • Computer supported collaboration and collaboration engineering.
  • Modern I&CT enablers for collaborative decision-making activities, such as: a) AI-based tools including service-oriented cognitive systems and multi-agent cooperative schemes, b) Data science and analytics, c) cloud, sky, and mobile computing, d) social networks, e) biometric systems for e-collaboration, co-simulation in design and so on.
  • Multi-person Decision Support Systems (DSS) and platforms.
  • Special cases of DSS, such as: recommender systems, and systems designed to support real-time decision-making in emergency situations.
  • Practical recent applications including pandemic related cases.
  • Green and trustworthy computing, ethical aspects, and digital humanism.
REFERENCES
  • Filip F.G., Zamfirescu C.B., Ciurea C. (2017) Computer-Supported Collaborative Decision-Making. Springer
  • Filip F.G. (2022) Collaborative Decision-Making: Concepts and Supporting Information and Communication Technology Tools and Systems. International Journal of Computers Communications & Control, 17 (2), DOI: https://doi.org/10.15837/ijccc.2022.2.4732

Special Session 02: Intelligent Decision Making and Consensus

Prof. Francisco Javier Cabrerizo University of Granada, Spain cabrerizo@decsai.ugr.es

Prof. Juan Antonio Morente-Molinera University of Granada, Spain jamoren@decsai.ugr.es

Prof. Ignacio Javier Pérez University of Granada, Spain ijperez@decsai.ugr.es

Prof. Carlos Porcel University of Granada, Spain cporcel@decsai.ugr.es

Prof. Enrique Herrera-Viedma University of Granada, Spain viedma@decsai.ugr.es

Intelligent decision-making processes are developed by automatic decision-making systems that support individual or organizational decision-making processes using different information technologies (as 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, such as global green digital economy, 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 systems for big data.
  • Intelligent decision-making in web.
  • Intelligent decision-making in global green digital economy.
  • Aggregation of preferences.
  • Intelligent decision-making in dynamic environments.

Special Session 03: Modeling Financial and Economic Sustainability for Decision Making and Governance

Prof. Alexander Karminsky Higher School of Economics, Russia karminsky@mail.ru

Prof. Massimiliano Caporin University of Padova, Italy, massimiliano.caporin@unipd.it

Prof. Mikhail Stolbov MGIMO-U, Russia stolbov_mi@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 mostly connected with sustainable development and ESG approaches.

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 and financial and economic sustainability;
  • Formation of rating system in Business and Finance;
  • Using credit ratings and there models as creditworthiness measure;
  • ESG ratings and there modeling;
  • Consider efficiency models to compeer bank and financial companies’ usefulness;
  • Evolution of the functions of financial and banking ecosystems;
  • Impact of banking innovations on the development of banking products and services, assessment of the level of competitiveness;
  • Evaluation of the results of financial digitalization of the financial sector and banks on the basis of business informatization;
  • Financial stability assessment and prudential regulation, including ESG sustainability governance: ESG systemic risks and there management; COVID pandemic and systemic risks.

Special Session 04: Blockchain Technology and Non-fungible Tokens in Scientific Research and Innovation

Assistant Prof. Seyed Mojtaba Hosseini Bamakan, Yazd University, Iran smhosseini@yazd.ac.ir

Prof. Qiang Qu, Chinese Academy of Sciences, China qiang@siat.ac.cn

Dr. Alex Liu, RMDS, United States. (https://www.rmdslab.com/dr-alex-liu/)

Associate Prof. Ahad Zareravasan, Masaryk University, Czech Republic. Zare.ahad@mail.muni.cz

Dr. Mehdi Gheisari, Harbin Institute of Technology, Shenzhen, China. gheisarim@mail.sustech.edu.cn

Blockchain technology and its application in dealing with various real-world problems have been attracting proliferating studies over recent years. A blockchain is a technical approach to collectively maintaining a reliable database. The main idea of Blockchain is to create blocks of multiple nodes using cryptographic techniques. Each block contains a hash to the next block in the chain, blocks' data, and creates digital fingerprinting to verify and validate block information. Blockchain technology ensures tamper-proof information sharing with its unique features like reliability, security, anonymity, and decentralization.

Blockchain technology makes it possible to categorize digital assets into fungible and non-fungible tokens (NFT). In this context, non-fungible tokens are tokens with unique properties that cannot be substituted. Currently, NFT's protocols, standards, and applications are proliferating. Using this technology, several digital artworks, games, and collectibles have been successfully created. However, its applications in real-world situations have not been appropriately considered. For example, researchers and scientists face many problems such as low rate of IP commercialization, difficulties in projects funding, costly and time-consuming IP registration process, fractionalizing the ownership of a patent, registering the ownership of workflows, data sets, codes and algorithms, etc.

This special issue addresses the challenges of establishing a distributed platform to facilitate scientific collaboration between universities, research institutions, and industries, with the goal of registering intellectual property assets, such as patents, IPs, and rewards as NFTs. Scientists from different domains can take advantage of NFTs in academic problems such as registration of ownership of clinical trial results, live-cell images, space and galaxies' images, personal funding, biotechnology methods and techniques, workflows, etc. Although, NFT will provide transparency, liquidity and open an innovative market to scientists who aim to commercialize their ideas and inventions efficiently, there are several challenges which needs to be addressed. NFT can be considered an academic and industrial future trend that many of its aspects need to be discussed.

This special issue focuses on academic research in related fields of NFT and invites researchers to submit their unpublished, novel, and state-of-the-art papers. The following topics are attended in this special issue (not limited to):
  • NFT as an ownership document;
  • NFT as a certificate;
  • NFT as a reputation;
  • NFT as a digital asset or investment;
  • NFT as company(ies) stocks;
  • NFT as a ticket;
  • NFT as a digital twin;
  • NFT forensic;
  • NFT and money laundering;
  • NFT and AI;
  • NFT regulatory and government controls;
  • NFT ownership transferring methods;
  • NFT applications in Smart Healthcare Systems, Metaverse, Art, Gaming and others;
  • Distributed and fair pricing methods for NFTs;
  • Security and privacy challenges of NFTs;
  • Securing NFTs against cyber-attacks;
  • Buyer/Seller privacy in NFTs transactions.
Survey and review papers in all the above topics are encouraged for publication and be valid for submission.

Special Session 05: Neuromanagement and Neuromarketing

Prof. Felisa M. Córdova, University San Sebastián, Chile, felisa.cordova@uss.cl

Dr. Hernán Díaz, University Finis Terrae, Chile, hernan.diaz@usach.cl

Ms. Robertino Pereira, University Finis Terrae, Chile, robertino.pereira@gmail.com

In Academia, the neuroscientific approach has created a reliable toolset to make attitudes and values, attention, perception and cognitive processes visible and measurable in the context of laboratory experiments, but also more recently, in natural interactions. Today the very same technologies that have propelled the advancements in scientific research are available to a wider public and offer the possibility to measure and understand human behavior and therefore the opportunity to create a bridge between theoretical, academic knowledge and the real world.

Tools like eye tracking, EEG, biometric sensors and emotion detection, amongst others, allow to uncover otherwise hidden factors in decision making processes and give valuable insights in how to concretely adjust and improve the interaction with processes, products, prices, marketing activities and sales channels.

The purpose of this Special Session is to bring attention to the need to observe and investigate how our brains may be responding to this new ecosystem of interactions, where the non-verbal communication, that which we usually call intuitive, loses preponderance, in favor of a decision making process based on agreements, opinions, or global recommendations coming from a digital world.

For this Special Session, the following topics of interest fall within the framework of the ideas previously exposed:
  • The new mind of the consumer: how volatile is the frame of reference for making new decisions?
  • Quantitative and qualitative methods used in new applications of neuromanagement and neuromarketing.
  • Tools for the search, detection, and classification of brain electrical activity (EEG), in front of stimuli with variable references.
  • Process of learning about customer purchasing preferences.
  • Mental health, adaptive, and complementary support in the transition toward the new matrix of meaning in the near-future culture digital configuration.

Special Session 06: Soft Computing and Optimization for Decision-Making

Prof. Bogdana Stanojević, Mathematical Institute of the Serbian Academy of Sciences and Arts, Serbia, bgdnpop@mi.sanu.ac.rs

Prof. Boris Delibašić, University of Belgrade, Serbia, boris.delibasic@fon.bg.ac.rs

We cordially invite papers addressing new methods and applications in the areas of soft computing and optimization methods. Despite the prevalent use of fully automated machine learning methods, expert-based methods and their combination with machine learning methods still play an important role in decision-making as they try to bind two worlds in decision-making, the expert based and the data based.

With soft computing methods and tools one can exploit domain knowledge about the problem at hand, model uncertainty, and imprecision in measurements robustly and with low computation cost. By doing this one enhances the context of the decision-making process and offers comprehensiveness (greater interpretability and explainability) of the decision being made.

This session aims to present state-of-the-art research on soft computing and optimization, and its application to either expert- or algorithmic-based decision making. Having this in mind, we solicit submissions in all aspects of soft computing and optimization for decision-making, from mathematical foundations of soft computing and optimization for decision-making, novel approaches and methods, and its applications for business purposes or for proposing solutions for social problems.

Topics of interest in this session are presented in the following non-exhaustive list:
  • Soft computing;
  • Fuzzy logic, fuzzy sets and systems and their extensions;
  • Fuzzy decision-making and decision support systems;
  • Fuzzy optimization;
  • Optimization methods and applications for decision-making;
  • Multi-criteria decision-making;soft computing and optimization in data mining and machine learning;
  • Soft computing and optimization for social good;
  • Multi-objective optimization;
  • Mathematical theory for modeling uncertainty;
  • Decision making under uncertainty;
  • Approximate reasoning, and others.

Special Session 07: The 9th Intelligent Decision Making and Extenics based Innovation

Prof. Xingsen Li, Guangdong University of Technology, China. lixingsen@126.com

Prof. Lin Lu, Guangxi Normal University, China. lulin@mengha.cn

Prof. Tao Wang, Beijing Institute of Technology, China. wangtao1020@126.com

Prof. Long Tang, Nanjing University of Information Science and Technology, China. tanglong@gdut.edu.cn

With the rapid development of information technology, knowledge acquisition through data mining becomes one of the most important directions of scientific decision-making. 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 beyond big data 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 methods and technology
  • Intelligent knowledge management based on Extenics
  • Intelligent Information management and Problem Solving on Extenics
  • Knowledge Mining on E-business
  • Intelligent Systems and its Applications based on Extenics
  • Web Marketing and CRM taking Extenics as methodology
  • Intelligent Data Analysis and Financial Management
  • Intelligent technology and Tourism Management
  • Innovation theory and Extenics based Methods
  • Extenics based Decision Making
  • Extension data mining and its Applications
  • Web Intelligence and Innovation on big data
  • Knowledge based Systems and decision-making theory on Extenics
  • Extenics based design technology and applications
  • Intelligent Logistics Management and Web of Things taking Extenics as methodology

Special Session 08: Multi-Criteria Decision Analysis with Behavioral Methods

Prof. Luiz Flavio Autran Monteiro Gomes, IBMEC, Brazil. luiz.gomes@professores.ibmec.edu.br

Prof. Alexandre Leoneti, USP, Brazil. ableoneti@usp.br

Multi-Criteria Decision Analysis methods have a particular rationality, when compared against other quantitative methods for decision support. While those are founded on a universal, optimization-based rationality, when dealing with multi-criteria methods one is able to incorporate diferente types of rationality, such as Simon’s bounded rationality. In this sense, multi-criteria methods have the advantage to take into consideration advances in the field of human judgement and decision-making in their structures, aiming to improve the degree of realism of recommendations. This characteristic of multi-criteria methods has recently led to formulations of classical methods, thanks to the inclusion of parameters coming from empirical studies in the behavioral sciences. As examples, one can cite Behavioral TOPSIS and ExpTODIM. The key objective of this Special Session is to present and discuss this new trend in Multi-Criteria Decision Analysis and its applications, raising its benefits to decison-making

Topics appropriate for this special session include, but are not limited to::
  • New behavioral models of MCDM methods.
  • Applications of behavioral models of MCDM methods.
  • Inter and intra comparisons among behavioral models of MCDM methods.

Special Session 09: Soft Computing Methods and Applications in Quantitative Management and Decision Making

Dr. Simona Dzitac, University of Oradea, Romania. simona@dzitac.ro

Dr. Sorin Nadaban, Aurel Vlaicu University of Arad, Romania. snadaban@gmail.com

Dr. Attila Simo, Politehnica University Timisoara, Romania. attila.simo@upt.ro

Prof. Domnica Dzitac, New York University Abu Dhabi, Abu Dhabi. domnica.dzitac@nyu.edu

L.A. Zadeh was the first one who used the Soft Computing term. According to his definition, the soft computing methods are those techniques used in computer science, machine learning and several engineering fields which, opposing to hard computing methods, are able to study, model and analyze a very complex reality that have not been efficiently managed by the traditional methods. Soft Computing can handle ambiguous data, and is tolerant of imprecision, uncertainty, partial truth, and approximation. The human mind is the crucial model for Soft Computing.
Soft Computing includes: (1) Fuzzy Logic; (2) Neural Computing: Perceptions, Artificial Neural Networks, Neuro-Fuzzy Systems; (3) Evolutionary Computation: Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Life (AL); (4) Machine Learning: Intelligent Agents, Expert Systems, Data Mining; (5) Probabilistic Reasoning: Bayesian Networks, Markov Networks, Belief Networks.

Soft Computing provides the background for the development of smart management systems and decisions in case of ill-posed problems.

The goal of this special session is to bring together researchers interested in applications of soft computing methods in quantitative management and decision making, in order to exchange ideas, discuss significant findings, share experiences, and work together in a friendly environment.

The topics of interest include, but are not limited to:
  • Artificial Intelligence Methods for Web Mining;
  • Computational Intelligence Methods for Data Mining;
  • Colony Optimization Algorithms;
  • Data Science Applications in Quantitative Management;
  • Fuzzy Systems for Computational Linguistics and Natural Language;
  • 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;
  • Support Vector Machine in SC applications.

Accepted Workshops

Workshop 01: Evaluation of the efficiency of decision making units using Data Envelopment Analysis

Prof. Fuad Aleskerov, HSE University, Russia. ( fa201204@gmail.com)

Dr. Sergey Demin, HSE University, Russia. ( sdemin@hse.ru)

Data Envelopment Analysis is one of the most popular methods for the efficiency assessment of similar objects. In addition, this method automatically calculates parameters' weight coefficients. There are many new modifications of basic models, which make them more applicable in real life. Therefore, this methodology is widely applied, e.g., regional hospitals and medical centers, different farms, investment portfolios, universities, NBA teams and many other samples are compared using Data Envelopment Analysis. As a result, taking into account modern trends towards ubiquitous optimization of efficiency in all spheres of our life, Data Envelopment Analysis meets a higher demand.

During the workshop specialists from efficiency assessment will give lectures on the following topics:
  • Efficiency evaluation of heterogeneous samples
  • Data Envelopment Analysis in case of non-precise data
  • Network modifications of Data Envelopment Analysis
REFERENCES
Aleskerov F. T., Demin S. DEA for the Assessment of Regions' Ability to Cope with Disasters, in: Dynamics of Disasters. Impact, Risk, Resilience, and Solutions. Issue 1. Springer, 2021. Ch. 2. pp. 31-37


Workshop 02: Networks in Managerial Decisions

Prof. Fuad Aleskerov, HSE University, Russia. ( fa201204@gmail.com)

Prof. Sergey Shvydun, HSE University, Russia. ( shvydun@hse.ru)

Networks represent many complex systems (social, economic, financial, technological, biological, etc.) while their theoretical and computational analysis give insights into many practical problems. However, the growth of the size of real networks as well as their heterogeneity make the analysis of their structure and dynamics both important and problematic. The aim of this session is to bring together researchers, academics, practitioners, and students who are working on theoretical, computational, and applied aspects that facilitate decision-making process.

The session includes the papers that contain recent results obtained by research teams from academia and industry concerning but not limited to the following topics:
  • Models of complex networks,
  • Big Data analysis on social networks,
  • Social influence and information diffusion models,
  • Epidemics,
  • Link prediction,
  • Machine Leargning on Graphs ,
  • Recommendation systems and networks,
  • Dynamics of complex networks;
  • Analysis of real-world complex networks.
REFERENCES
Aleskerov, F., Shvydun, S., & Meshcheryakova, N. (2021). New Centrality Measures in Networks: How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781003203421

Workshop 03: A spread of COVID-19 and the efficiency of quarantine measures

Prof. Fuad Aleskerov, HSE University, Russia. ( fa201204@gmail.com)

Prof. Alexey Myachin, HSE University, Russia. ( amyachin@hse.ru)

The outbreak of a new coronavirus infection that began at the end of 2019 has now affected most countries of the world. By March 2020 the pandemic situation was declared by the World Health Organization. The seriousness and scale of this problem determine the relevance and interest of the scientific community in various studies, including compiling predictive models of morbidity in various countries and studying the effectiveness of the quarantine measures. The aim of this session is to bring together scientists, researchers and students from different countries, engaged in theoretical work and empirical research related to COVID-19, including a prediction of the spread rate, taking into account various factors and methods for improving the epidemiological situation in the world.


Workshop 04: Risk scenario-based decision making: Methods and Applications

Associate Prof. Weilan Suo, Institutes of Science and Development, Chinese Academy of Sciences, China. ( suoweilan@casisd.cn)

Associate Prof. Gang Li, Northeastern University at Qinhuangdao, China. ( ligang@neuq.edu.cn)

Associate Prof. Mingxi Liu, Institutes of Science and Development, Chinese Academy of Sciences, China. ( liumingxi@casisd.cn)

Prof. Xiaolei Sun, Institutes of Science and Development, Chinese Academy of Sciences, China. ( xlsun@casisd.cn)

Preventing and resolving critical risks has been an important national strategic plan. To response the requirements of the national strategic plan, scientists are exploring new methods, models, and tools to address the issues arising from the various risk scenarios in different fields. Facing the major public health emergency (e.g., COVID-19), how to identify the multisource risks and clarify the risk evolution mechanism? Facing the urgent public requirements for emergency supplies under the COVID-19 pandemic, how to assess the resilience of emergency support system to respond the multisource risks? Facing the intensified geographic conflicts, how to improve the security guarantee system under the Belt and Road Initiative? Facing the increasing credit demand of real economy under the COVID-19 pandemic, how to conduct credit decision and credit risk management? Facing the increasing safety threat of critical infrastructures, how to prevent multi-source risks and improve critical infrastructure resilience? Facing the increasingly prosperous cryptocurrency market brought about by the rapid development of financial technology, how to look ahead and judge the risks of the volatile digital currency market?......

In the process of preventing and resolving critical risks, some general issues on risk management and decision-making have attracted much attention. For example, risk scenarios analysis is conducted from the perspectives of scenario elements, effect mechanism, and potential consequence. For another example, risk characterization is represented in the form of multi-source, interdependency, and dynamicity, which are modeled by some quantification tools. Recently, some intelligence technologies, such as deep learning (DL), ensemble learning (EL), artificial intelligence (AI) and text mining, are used to provide an efficient support for risk data collection and processing, visualize the judgment reference for the risk identification and diagnosis, improve the accuracy of credit decision, and solve the problems on warning and management for financial risks. On the other hand, some application issues derived from the process of preventing and resolving critical risks have been studied, such as scenario-based model for critical infrastructure risks, and ML/AI model for credit risks and Fintech risks. The main objective of this workshop is to facilitate the exchange of ideas and knowledge among practitioners, scholars, teachers, and others interested in applications of decision-analysis methods in various risk scenarios.

Topics of interest include, but are not limited to, the following:
  • Risk management and decision-making
  • Risk scenario analysis
  • Risk characterization and modeling
  • Risk-based intelligence decision analysis
  • Risk data collection and processing
  • Risk identification and diagnosis
  • Mechanism analysis for risk evolution
  • Resilience assessment for risk response
  • Scenario-based model for critical infrastructure risks
  • ML/AI in finance risk management
  • Credit decisions
  • Credit rating and credit scoring
  • Bayesian risk decision
  • Cryptocurrency market risks
  • Fintech risk management


Workshop 05: Business Intelligence

Prof. Chonghui Guo, Institute of Systems Engineering, Dalian University of Technology, Dalian, China. ( dlutguo@dlut.edu.cn)

Associate Prof. Kun Guo, Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, China; Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, China. ( guokun@ucas.ac.cn)

Prof. Yong Shi, Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, China; Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, China. ( yshi@ucas.ac.cn)

With the development of big data and artificial intelligence technology, the digital transformation of enterprises has become an inevitable trend. Business intelligence plays an important role in discovering the knowledge behind the data and providing the management decision support for enterprises. Especially in the era of big data, how to use the new artificial intelligence technology to improve the quantitative management ability of enterprises has become the key point to the digital transformation of enterprises and the improvement of management decision-making ability.

The session is meant to include papers that contain recent results obtained by research teams from academia and industry concerning but not limited to the following topics:
  • New Algorithm of Business intelligence
  • Business intelligence based on big data
  • Business intelligence for enterprise management
  • Business intelligence for financial management
  • Business intelligence in complexity system

Chonghui Guo is a professor of the Institute of Systems Engineering, Dalian University of Technology, Dalian, China. He is the Head of Center for Big Data and Intelligent Decision Making, Dalian University of Technology. He received a B.S. degree in mathematics from Liaoning University in 1995, an M.S. degree in operational research and control theory in 1999, and a Ph.D. degree in management science and engineering from Dalian University of Technology in 2002. He was a postdoctoral research fellow in the Department of Computer Science at Tsinghua University, Beijing, China. His studies concentrate on data mining and knowledge discovery. He has published over 160 peer-reviewed papers in academic journals and conferences, besides 5 text-books and 2 monographs. He has been the principal investigator on over 20 research projects from the government and the industry.

Kun Guo is an associate professor in Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences and Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences. She received her PhD in management science and engineering from UCAS in 2011. Her research covers financial market, big data application in economy analysis, fictitious economy and complex system. She has published over 30 peer-reviewed journal articles on Journal of Forecasting, Industrial Management & Data Systems, Applied Economics, Finance Research Letters, Knowledge-Based Systems, etc. She is also secretary general of Business Intelligence Society, Chinese Academy of Management.

Yong Shi is the Director of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences and Deputy Director of National Engineering Laboratory of Big Data Analysis System. He is State Council Counsellor, Academician of Academy of Sciences for Developing Countries and President of International Academy of Information Technology and Quantitative Management (IAITQM). His major research areas include data mining, big data analysis and management decision-making.


Workshop 06: The 7th workshop on Big Data and Management Science & Outlier Detection in Finance and Economics

Prof. Aihua Li, Central University of Finance and Economics, China. ( aihuali@cufe.edu.cn)

Prof. Zhidong Liu, Central University of Finance and Economics, China. ( liu_phd@163.com)

With the development of information technology, more and more data are stored in many fields and different industries. Big data is not only a definition for data but also for the technology and idea to deal with big data. For decision makers, there are still drowned in data but lack of knowledge. Management Science is a subject to solve the problem in management with qualitative and quantitative method. New idea and method in big data put new energy for management science. Thus, there are new methods and technology in the field big data and management science in recent years.

Outlier detection in economic and financial markets is a problem faced by managers. In the era of big data, data from various sources are collected to analyze outlier detection, and data fusion methods are often used. Therefore, outlier detection based on data fusion is widely concerned. This workshop focuses on how to detect abnormal pattern with data fusion especially in finance and economics fields. In addition, theoretical system and methods of outlier detection would be needed to be proposed. This topic includes outlier detection theory, method and application for financial and economic data based on domain knowledge, data fusion, and empirical analysis.

The topics and areas include, but not limited to:
  • Data fusion method and application in financial outlier detection problem;
  • Data fusion method and application in economics outlier detection problem;
  • 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 with data fusion in other field;
  • Quantitative management and decision making in finance;
  • Method, model and application in big data and management science;


Workshop 07: Quantitative Finance and Risk Management

Prof. Wei Chen, Quantitative Finance Research Center, Capital University of Economics and Business, China. ( chenwei@cueb.edu.cn)

Associate Prof. Zhensong Chen, Quantitative Finance Research Center, Capital University of Economics and Business, China. ( chenzhensong@cueb.edu.cn)

Dr. Yinhong Yao, Quantitative Finance Research Center, Capital University of Economics and Business, China. ( yaoyinhong@cueb.edu.cn)

Dr. Yanxin Liu, Quantitative Finance Research Center, Capital University of Economics and Business, China. ( lyxinnn@cueb.edu.cn)

With the development of multi-source heterogeneous big data, artificial intelligent methods are being more and more important in various fields within finance. Recently, stock market forecasting, algorithmic trading, risk assessment, portfolio allocation, asset pricing and derivatives market are among the areas where machine learning and deep learning researchers focus on developing models that can provide real-time working solutions for the financial industry. The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of quantitative finance and risk management, to promote in-depth research on frontier theoretical and practical issues in quantitative finance and risk management, to generate new results in this relatively under-researched area, and determine directions for further research.

The workshop is interested in topics related to all aspects of quantitative finance and risk management, which includes, but are not limited to, the following:
  • Quantitative investment strategy and technology
  • Quantitative risk management
  • Portfolio optimization and strategy
  • Machine learning based model for financial forecasting
  • Financial complex network
  • Financial distress prediction and bankruptcy prediction


Workshop 08: Multi-Source Data-Driven Financial Fraud Risk Analysis

Associate Prof. Xiaoqian Zhu, School of Economics & Management, University of Chinese Academy of Sciences, China. ( zhuxq@casipm.ac.cn)

Prof. Jianping Li, School of Economics & Management, University of Chinese Academy of Sciences, China. ( ljp@ucas.ac.cn)

Associate Prof. Gang Li, School of Economics, Northeastern University at Qinhuangdao, China. ( ligang@neuq.edu.cn)

In recent years, financial fraud events occurred frequently at home and abroad, which seriously threatened the stability of the financial system and affected the healthy development of the capital market. Existing literature has done good research on the identification, analysis, and measurement of financial fraud risk based on structured quantitative data. However, the quantitative data only contain limited information, so it is difficult to break through the bottleneck under incomplete information. The advent of the information age has brought us a huge amount of multi-source data, such as news reports, images, videos, financial analysts’ reports, and textual risk disclosures in financial statements. These big data contain a wealth of risk information, which can be used as an effective supplement to traditional quantitative data. Based on the comprehensive information from multi-source data, a more effective and accurate fraud risk analysis is expected.

Big data has the typical characteristics of massive, multi-source, and heterogeneous. How to obtain and deal with fraud-related big data? How to gather and fuse these multi-source data? How to extract the key risk information from multi-source data? How to use advanced technology for better financial fraud analysis? Especially, the current COVID pandemic results in new risks and new characteristics for financial fraud. What types of new data and new methods can be used to better identify and analyze financial fraud? This workshop intends to collect fraud risk analysis papers based on multi-source data, to promote in-depth exchanges among scholars, practitioners, and regulators in this field.

Topics of interest include, but are not limited to, the following:
  • Insurance fraud risk analysis
  • Financial statement fraud analysis
  • Credit fraud analysis
  • Credit rating and scoring
  • Risk data collection and processing
  • Risk data integration and fusion
  • Risk information extraction
  • Machine learning in fraud analysis
  • Artificial intelligence in fraud analysis
  • Knowledge graph analysis and reasoning
  • Big data analysis
  • Text mining method
  • Fintech risk management
  • Regtech


Workshop 09: IoT and Digital Transformation

Prof. Xiaodan Yu, University of International Business and Economics, China. ( xyu@uibe.edu.cn)

Prof. Reinhard Bernsteiner, MCI, Management Center Innsbruck, Austria. ( reinhardchristian.bernsteiner@mci.edu)

As the digital economy continually evolves, many companies have undertaken a digital transformation strategy in a response to the increasingly competitive business environment. The outbreak of COVID-19 has added even more challenges to companies’ digital transformation strategy. In the pandemic, companies became to know their vulnerabilities and the importance of being agile and resilience. The pandemic runs in waves and even when it ends, we will not return to the way things were before. IoT is a disruptive technology that makes fundamental differences in the scale, scope, and speed of data collection, analysis, and usage. IoT continues to be crucial in business digital transformation. In the pandemic and post-pandemic era, we have witnessed many successful digital transformation cases with IoT.

Accordingly, in this year, this workshop's objectives continue to focus on the fundamental issues with IoTs and how IoT-related opportunities can support digital transformation in varied areas, such as healthcare, retail, manufacturing, building, government, and smart home etc. We especially welcome research that discussed the relevance of studies in a post-COVID world.

Overall, we aim to discuss the design, development, deployment, and usage of IoT in broad areas. As many significant firms are developing platforms such that multitude of devices can be connected, we are also interested in the innovative and successful usage of these IoT platforms in real business cases as well as related teaching cases in the educational setting.

We welcome submissions on varied topics around IoT and digital transformation, such as (but not limited to):
  • IoT and healthcare
  • IoT user experience
  • IoT education
  • IoT blockchain
  • Formal verification and model-checking for Internet of Things applications
  • Knowledge representation models in the Internet of Things
  • Business information processing and business models in the Internet of Things
  • Software engineering in the Internet of Things
  • Enterprise knowledge management in the Internet of Things
  • Privacy protection and security issues of the Internet of Things
  • Intelligent applications of the Internet of Things in varied domains (healthcare, logistics, manufacturing, crisis management, government, project management)
  • Technologies of data management and integration in the Internet of Things
  • Other emerging issues of IoT and digital transformation


Workshop 10: The 9th Workshop on Optimization-based Data Mining

Prof. Yingjie Tian, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. ( tyj@ucas.ac.cn)

Prof. Zhiquan Qi, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. ( qizhiquan@ucas.ac.cn)

Prof. Yong Shi, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. College of Information Science and Technology, University of Nebraska at Omaha, USA. ( yshi@ucas.ac.cn)

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.

Program Committee



Workshop 11: The 12th International Workshop on Computational Methods in Energy Economics

Prof. Lean Yu, School of Economics and Management, Beijing University of Chemical Technology, China. ( yulean@amss.ac.cn)

Prof. Kaijian He, College of Tourism, Hunan Normal University, China. ( kaijian.he@my.cityu.edu.hk)

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 eleventh 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, Seventh, Eighth, Ninth, Tenth, Eleventh International Workshop on Computational Methods in Energy Economics (CMEE 2008, CMEE 2009, CMEE 2010, CMEE 2011, CMEE 2012, CMEE 2013, CMEE 2015, CMEE 2016, and CMEE 2017, and CMEE2021) were held in Nanjing, Sanya, Huangshan, Kunming, Harbin, rio De Janeiro (Brazil), Asan (Korea), New Dehli (India), and Chengdu 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, July 21-24, 2015, August 16-18, 2016, December 8-10, 2017 and July 9-11, 2021. To promote the idea-exchange and discussion of this field, the Eleventh International Workshop on Computational Methods in Energy Economics (CMEE 2022) will be held in Beijing, China, December 9-11, 2022. 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)


Workshop 12: Modelling and optimization of complex systems with application to decision making

Prof. Gang Kou, School of Business Administration, Southwestern University of Finance and Economics, China ( kougang@swufe.edu.cn)

Prof. Hui Xiao, School of Statistics, Southwestern University of Finance and Economics, China. ( msxh@swufe.edu.cn)

With the rapid development of technology, both the modern engineering systems and operation management systems are becoming more and more complex. The increasing complexity brings new challenges on modelling and analysing these systems. These challenges include but not limited to the complexity of system structure, the interaction among internal structures, the dynamic nature of system behaviours, and the uncertain external impacts. In the past decades, a variety of analytical models are proposed to model the complex systems. Besides, simulation is also frequently used to model the system dynamic behaviours and to evaluate the system performance. In the era of artificial intelligence, machine learning techniques provide new approaches for modelling and analysing complex systems.

This workshop aims to present state-of-the-art research on modelling and optimization of complex systems and its application to decision making. Having this in mind, we solicit submissions in all aspects of approaches from analytical methods and simulation approaches to machine learning techniques.

Topics of interest in this workshop include but not limited to:
  • Reliability modelling and optimization of complex systems
  • Maintenance decision for complex systems
  • Quality control of complex products
  • Multi-attribute decision making for complex systems
  • Simulation modelling and optimization
  • Multi-objective optimization
  • Mathematical theory for modelling uncertainty
  • Decision making under uncertainty
  • Group decision making
  • Complex supply chain systems
  • Defense and attack of complex systems
  • Data driven approaches to complex systems
  • Machine learning in complex systems


Workshop 13: Machine Learning and Intelligent Awareness

Prof. Xiaofei Zhou, Chinese Academy of Sciences, China. ( zhouxiaofei@iie.ac.cn)

Prof. Jia Wu, Macquarie University, Australia. ( jia.wu@mq.edu.au)

With the arrival of the era of big data, machine learning methods are being more and more important in the fields of artificial intelligence and related applications, such as natural language, multimedia, social networks, etc. Intelligent sensing and analysis based on big data is the core task of artificial intelligence in the future. This workshop aims to provide an international forum to discuss data intelligent mining methods, technologies and systems for computational science, artificial intelligence, knowledge engineering and management decision discipline fields.

The topics covered will include, but not limited to:
  • Machine Learning and data mining technique: classification, clustering, regression etc.
  • Neural network and deep learning
  • Graph data and networks
  • Knowledge extraction and expression
  • Text analysis and nature language process
  • Knowledge graph
  • Link and Graph Mining
  • Social agents for intelligent awareness
  • Recommendation Systems
  • Visualization technologies and analytics
  • Multimedia and Multi-structured Data Analysis
  • Intelligent knowledge in management decision
  • Biometric identification
  • AI Security
  • Algorithms and Systems for Social Media Analysis
  • Mobility and Social Network Data
  • Information filter
  • Object recognition
  • Distributed and Parallel Algorithms
  • Big Data Search Architectures, Scalability and Efficiency
  • Social Data Acquisition, Integration, Cleaning, and Best Practices
  • Cloud/Grid/Stream Data Mining


Workshop 14: Digital Technologies, Digital Transformation, and Business Value

Prof. Xiaobo Xu, Xi’an Jiaotong-Liverpool University, China. ( Xiaobo.Xu@xjtlu.edu.cn)

Prof. Fei Ma, Chang’an University, China. ( mafeixa@chd.edu.cn)

To be competitive and innovative in the global digital economy, organizations have to invest in digital technologies. The contemporary diffusion of digital technologies in terms of cloud computing, distributed computing, mobile computing, social media, artificial intelligence, business data analytics, FinTech, etc. have triggered digital transformation, resulting in the business value achievement.

It is evident that digital technologies generate business value by influencing organizational innovation, entrepreneurship, and transformation. Work is increasingly being digitalized or virtualized. Several new digital forms such as crowdsourcing, crowdfunding, have been emerged. Additionally, new business models of the sharing economy have disrupted traditional organizations and created new marketplaces.

While digital technologies and digital transformation have brought many advantages of efficiency, effectiveness, convenience, and competiveness, these advantages are only possible if the information systems of the organizations are aligned with these new digital technologies and new digital transformation.

Our track welcomes rigorous and relevant theoretical and empirical research to reassess traditional assumptions and create new theories about digital technologies, digital transformation, and business value. This research challenges require the joint effort from fields of information systems research, management science, organizational studies, business value or other disciplines.

Topics of interest in this workshop include but not limited to:
  • Digital technologies and digital transformation
  • Digital technologies and business value
  • Digital transformation and business value
  • Digital change and innovation management
  • Theories of FinTech and innovative applications for FinTech
  • Digital technologies enabled business model innovations
  • Digital entrepreneurship and new business models
  • Artificial intelligence and business value
  • Digital technologies project management
  • Perspectives and challenges associated with digital technologies, digital transformation, and business value


Workshop 15: Scientific data analysis and decision making

Associate Prof. Dengsheng Wu, Institutes of Science and Development, Chinese Academy of Sciences, China. ( wds@casipm.ac.cn)

Prof. Jianping Li, University of Chinese Academy of Sciences, China. ( ljp@ucas.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 re-use and re-analysis, ultimately speeding up the process of scientific discovery. The collection and utilization of scientific data are the two primary 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. Recently, the scientific data analysis for ecological animal husbandry has attracted much attention. Some empirical research, such as the big data management platform for ecological animal husbandry in Qinghai Province China, has generated significant benefits. The main objective of this workshop is to provide researchers and practitioners and opportunity to share the most recent advances in the area of data science and decision analysis for e-science, especial the ecological animal husbandry. The workshop aims to create a communication platform for researchers to share recent and significant developments in the general area.

Topics of interest include, but are not limited to, the following:
  • Metadata standard for scientific data
  • Scientific data quality analyzing
  • Scientific data integration and sharing
  • ETL process for scientific data
  • Disambiguation of scientific data
  • Scientific data visualization
  • Decision analysis modeling from scientific data
  • Network analysis from scientific data
  • Data integration for ecological animal husbandry
  • Analysis modeling for ecological animal husbandry
  • Bibliometrics analysis from scientific data
  • Scientometrics analysis from scientific data


Workshop 16: Intelligent Knowledge Management

Prof. Jifa Gu, Academy of Mathematics and Systems Science of Chinese Academy of Sciences, China. ( jfgu@amss.ac.cn)

Prof. Yanzhong Dang, Institute of Systems Engineering of Dalian University of Technology, China. ( yzhdang@dlut.edu.cn)

Prof. Lingling Zhang, School of Economics and Management, University of Chinese Academy of Sciences, China. ( 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.

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. In addition, Knowledge Graph can express knowledge through visualization method and explore and analyze the relationship between knowledge, reveal the dynamic development law of knowledge field, and then realize knowledge sharing and reusing, which can provide rich semantic knowledge network and auxiliary information source for knowledge management and knowledge recommendation. 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.

In order to promote intelligent knowledge management research, we want to organize a special workshop dedicated to the topic of “Intelligent Knowledge Management” under the ninth International Conference on Information Technology and Quantitative Management (ITQM 2022).

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, knowledge graph, recommendation system and intelligent knowledge management, to generate new methods to 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 expert mining and intelligent knowledge. Topics of interest include, but are not limited to, the following:
  • Intelligent Knowledge Management
  • Wuli-Shili-Renli System Methodology (WSR)
  • Knowledge Synthesis
  • Expert Mining
  • Knowledge Extraction
  • Knowledge Graph
  • Equipment Health Management (EHM)
  • Problem-driven Knowledge Management
  • Knowledge Graph Embedding (KGE)
  • Recommendation Systems
  • Link Prediction
  • Large-scale Engineering Projects Management
  • Data and Knowledge Management Related to the Large-scale Scientific Facilities
  • Knowledge Presentation and Visualization
  • Extension
  • Knowledge Sharing
  • Knowledge Spillover
  • Knowledge Evaluation
  • KDD Process and Human Interaction
  • Intelligent Systems and Agents
  • Knowledge Management Support Systems