Role-based Collaboration Research Articles

E-CARGO-based dynamic weight offload strategy with resource contention mitigation for edge networks

With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

Adaptive Collaboration With Training Plan Considering Role Correlation

Based on role-based collaboration (RBC), group role assignment (GRA) optimizes a team’s overall performance by assigning the most appropriate individual agents from the team’s viewpoint based on agents’ role-playing abilities. As an extension of GRA, GRA with a training plan (GRATP) deals with the impact of training on team management. Considering the correlation between roles, the training of one agent on one role also affects the performance of the agent in other roles. Moreover, in the adaptive collaboration (AC) problem, the training time also affects significantly the agent’s ability, as an agent’s ability changes over time. However, the existing GRATP models fail to consider these factors in the collaboration process. Therefore, we aim to address the role-correlation-based adaptive GRATP (RCA-GRATP) in this article. This article contributes two aspects to the literature on AC. 1) RCA-GRATP problem is abstracted based on RBC and GRA. To the best of the authors’ knowledge, this is the first article that explicitly considers role correlation in the RBC problems. 2) A comprehensive formalization of RCA-GRATP and two solving algorithms for diverse situations are proposed to solve the formalized problems. Experiments are carried out to verify the effectiveness of the proposed algorithms in diverse scenarios.

Collaborative Optimization of Learning Team Formation Based on Multidimensional Characteristics and Constraints Modeling: A Team Leader-Centered Approach via E-CARGO

With the massive popularization of e-learning, collaborative learning via learning teams has become indispensable to enhancing the learning efficiency and learning quality of overall learners. The team leader usually plays a key role in collaborative learning. However, the existing research ignores the key characteristics of learners and constraints relevant to e-learners when identifying appropriate team leaders and compatible members. A novel collaborative optimization approach to learning team formation is proposed based on a refined learner model and the environments—classes, agents, roles, groups, and objects (E-CARGO) model. With the proposed approach, a learner is modeled by combining 5-D characteristics (i.e., cognitive ability, leadership, sociability, learning style, and personality) and three types of constraints (e.g., conflicts, genders, and the number of members), and an assessment mechanism is designed to measure the comprehensive abilities of learners for identifying an ideal team leader and selecting the team members for a team. By innovatively introducing the role-based collaboration theory and E-CARGO model, the leader-centered learning team formation problem is formalized as a collaborative optimization problem. The mathematical model and the constraint relations are established for this problem, which is solved based on the IBM CPLEX package. Finally, a case study and experiments demonstrate that the proposed approach is efficient and feasible, in favor of improving the satisfaction degree of learners.

Resource Scheduling Method for Equipment Maintenance Based on Dynamic Pricing Model in Cloud Manufacturing

Cloud manufacturing, as a novel service mode in the manufacturing field with the features of flexible resource assignment, timely service, and quantity-based pricing, has attracted extensive attention in recent years. The cloud manufacturing industry uses a significant amount of smart equipment. In this context, equipment maintenance resource scheduling (EMRS) is an important subject that needs to be studied. Cloud manufacturing platforms must provide effective services for equipment maintenance in a timely manner. In order to improve the efficiency of cloud manufacturing platforms and meet the needs of users, an effective EMRS scheme is required. In this paper, we propose a dynamic resource allocation model for cloud manufacturing to meet the needs of users and maximize the benefit of a cloud manufacturing platform. The model takes into account the needs of users and the benefits of a cloud production platform. The contributions of this paper are divided into the following three aspects. First, the E-CARGO model using role-based collaboration theory is introduced to formally model EMRS activities, forming a solvable optimization model. Second, a dynamic pricing model with a center symmetric curve is designed to realize the flexible conversion between time, cost, and price. Third, the concept of satisfaction in fuzzy mathematics is introduced, in order to meet the different needs of users and platforms, in terms of time, price, and cost, while ensuring service quality and the platform’s benefits. Finally, an improved genetic algorithm is used to solve the cloud manufacturing resource scheduling problem, and good experimental results are obtained. These results demonstrate that the proposed dynamic pricing model is reasonable, and the allocation scheme obtained through a genetic algorithm is feasible and effective.

Equilibrium Means Equity? An E-CARGO Perspective on the Golden Mean Principle

In the team allocation problem (TAP), eliminating team disparities aims at keeping an equilibrium of the resource or ability among teams for equality. For this concern, existing literature merely utilized the golden mean principle to eliminate team disparities from a static perspective. Few of them reasonably investigate the pros and cons of this principle from a computational perspective. Moreover, maintaining equilibrium is a dynamic process and requires dynamic adjustment, especially after considering team members’ self-efforts and adaptivity. With respect to the environments—classes, agents, roles, groups, and objects (E-CARGO) model and its role-based collaboration (RBC) methodology, this article formalizes and solves the TAP, i.e., revised group role assignment (GRA) problem, from both the individual and team’s perspective. Based on the revised GRA, this article provides novel insight into the effectiveness of dynamically maintaining equilibrium, which may help decision-makers be proactive in building more sustainable teams. Relevant large-scale simulation experiments are conducted in this article to verify the proposed method. This article reveals a social paradox: even though considering all about the team members’ self-efforts and adaptivity, equilibrium still seems inequitable. Conversely, pursuing equilibrium may bring the Matthew effect.

Universal Optimization Framework: Leader-Centered Learning Team Formation Based on Fuzzy Evaluations of Learners and E-CARGO

Building the right learning teams is a key to the success of collaborative learning in online and offline learning environments. However, existing research on learning team formation (LTF) ignores the uncertainty of learners’ abilities and lacks a common problem modeling and optimization approach. Aiming at the characteristics of two typical types of leader-centered (LC) LTF problems, a universal optimization framework of LC-LTF is proposed by introducing role-based collaboration (RBC) theory. This framework evaluates the comprehensive ability of learners via a fuzzy description mechanism; applies the environments–classes, agents, roles, groups, and objects (E-CARGO) model to formulate the LC-LTF problem; and employs an optimization platform to obtain an optimal solution. A case study demonstrates the effectiveness and feasibility of the proposed framework.

Group Role Assignment With Constraints (GRA+): A New Category of Assignment Problems

This article systematically establishes a new category of assignment problems by reviewing and extending the problems related to group role assignment (GRA) from a novel vision. After reviewing seven related GRA with Constraints (GRA+) problems, this article specifies three new major assignment problems to make GRA+ problems complete and coherent. In addition, this article proves a series of new related theorems, proposes new conditions for the specified problems to have feasible solutions, and verifies the hardness of the newly specified problems. This article finally verifies the value of the presented theoretical work and provides a generalized formalization of this category of problems, i.e., the one highly abstract optimization problem, which is specified the first time. This article contributes to the literature of assignment problems with a novel category of well-defined problems, i.e., GRA+. The presented problem category is original, consistent, but far from complete. It will initiate further innovations in assignment research along with the presented directions. This article again demonstrates the power of the methodology role-based collaboration (RBC), and the environments—classes, agents, roles, groups, and objects (E-CARGO) model.

Data Analytics and Visualization of Adaptive Collaboration Simulations

Role-based collaboration (RBC) is an adaptive computational methodology that uses roles as underlying mechanisms to facilitate and analyze system behavior for entities that collaborate and coordinate their activities with or within these systems. In dynamic environments, including those that occur in large-scale simulations, visualization provides insights into complex systems behaviors. This article presents a visual analytics (VA) approach to studying dynamics involved in adaptive collaboration (AC) for large, multiagent simulation model using new open-source tools. The results show that time-varying systems can be steered for optimal performance and assessing adaptations using VA dashboards.

“Be a Lighting Programmer”: Supporting Children Collaborative Learning through Tangible Programming System

In order to cultivate children’s computational thinking, researchers have developed many excellent programming systems. Among them, the tangible programming systems combined with graphic output have been widely accepted because of the intuitive input method and diversified visual feedback. However, few of them support collaborative learning or programming. Little is known about children’s experiences, emotional states and behaviours on collaborative programming. Motivated by this gap, we present a novel tangible and collaborative enabled programming system named Lighters, which designed for children aged 7–10 and had two types of collaboration modes (block-based and role-based). We conducted an user experiment with 24 children, and collected physiological (EDA), questionnaire, video recording and interview data for analysis. Based on our experiment results, Lighters are effective in helping children learn to program collaboratively. In addition, Lighters can mobilize children’s positive emotions and enthusiasm to learn programming. Compared with block-based collaboration, role-based collaboration is more likely to stimulate children’s emotional states and has a better effect on learning programming.

Preface

The 2022 3rd International Conference on Signal Processing and Computer Science (SPCS 2022) was successfully held in Qingdao, China from August 19 to 21, 2022 in the form of online conference. Focusing on signal processing and computer science, SPCS 2022 provided a valuable opportunity for researchers, scholars and scientists in related fields to share the latest research results and exchange their ideas.We were delighted and honored to invite both Prof. Haibin Zhu from Nipissing University, Canada to serve as our Conference General Chair. There were 90 individuals and enterprises attending the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations, and online Q&A discussion. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors by turns.During the conference, six sophisticated professors were invited to address keynote speeches. Among them, Prof. Haibin Zhu, our Conference General Chair, delivered a speech on the title: E-CARGO and Role-Based Collaboration. In this speech, he examined the requirement of research on collaboration systems and technologies, talked about RBC and its model E-CARGO, reviewed the related research achievements on RBC and E-CARGO in the past years, discussed those problems that have not yet been solved satisfactorily, presented the fundamental methods to conduct research related to RBC and E-CRAGO, discovered related problems and analyzed their connections with other cutting-edge fields. Additionally, Prof. Lei Meng from Shandong University, China gave a report on the Cross-modal Inference and Heterogeneous Information Fusion for Open-domain Visual Understanding. Visual understanding aims to build a machine learning model that is able to recognize objects, scenes, and events from visual media. He presented to us the recent progress of the Multimedia Mining, Reasoning, and Creation (MMRC) Lab on using the multimodal descriptors of visual media for open-domain visual understanding tasks, ranging from image classification, product recommendation, video classification, to 2D/3D visual synthesis. The wonderful and dramatic speeches of each keynote speaker had triggered heated discussion. Moreover, every participant praised this conference for disseminating useful and insightful knowledge.After months of well preparation and hard work, the proceedings of SPCS 2022 covering a bunch of papers are smoothly published. These papers feature but are not limited to the following areas: Digital Signal Processing, Ad-Hoc and Sensor Networks, Communication Signal processing, Monte Carlo Method, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.On behalf of the conference organizing committee, we would like to express our heartfelt appreciation to all the keynote speakers, peer reviewers, and all the participants. In particular, we would like to acknowledge the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume. We firmly believe that all the attendees have had fruitful discussions and gained valuable knowledge, and will enjoy the opportunity for future collaborations.Committee of SPCS 2022List of Committee member is available in this pdf.

Social Development Paradox: An E-CARGO Perspective on the Formation of the Pareto 80/20 Distribution

The Pareto 80/20 principle is extensively cited in discussing social distribution and is usually applied to explain phenomena in economics. However, little in the literature investigates the driving force of such phenomena. The known driving force may help decision makers be proactive in administering a society before it becomes unsustainable. The environments-classes, agents, roles, groups, and objects (E-CARGO) model and the role-based collaboration (RBC) methodology assist the formalization of group role assignment (GRA) problem, which models and solves the optimization problem for a group of agents to play a set of roles from the team’s perspective. Based on GRA, this article proposes a new way to investigate social development/distribution problems, such as the Pareto 80/20 principle, with computational social simulations. The proposed method is verified by experiments. This article reveals a social paradox: Emphasizing individual differences inevitably leads to rapid social wealth accumulation and polarization and ignoring such disparities certainly causes slow social wealth accumulation.

Preface

It is over one year since COVID-19 broke out. The whole world is still struggling against the virulent pandemic COVID-19. It is still difficult to take international travel. Many conferences are held virtually, for the sake of protecting all the participants and conference staff from get infected by the virus. It is uncertain when the COVID-19 will end, so it remains unclear for postponement time, while many scholars and researchers wanted to attend this long-waited conference and have academic exchanges with their peers. Therefore, in order to actively respond the call of the government, and meet author’s request, 2021 2nd International Conference on Signal Processing and Computer Science (SPCS 2021), which was planned to be held in Qingdao, China from August 20 to 22, was changed to be held online through Zoom software. This approach not only avoids people gathering, but also meets their communication needs.The SPCS 2021 was organized by AEIC Academic Exchange Information Center. It dedicated to create a platform for academic communications between specialists and scholars in the fields of Signal Processing and Computer Science. The conference created a path to establish a research relation for the authors and listeners with opportunities for collaboration and networking among the universities and institutions for promoting research and developing technologies.The conference brings together about 90 well-known scholars in the field of Signal Processing and Computer Science at home and abroad. The reports were divided into keynote speeches, oral presentations, and poster presentations to share their latest research results and experiences in related research fields. In the first part, each keynote speakers were allocated 30 minutes to present their talks via Zoom. After the keynote talks, all participants joined in a WeChat communication group to discuss more about the talks and presentations.We were greatly honor to have invited seven distinguished experts as our keynote speakers. Prof. Roberto Montemanni, from University of Modena and Reggio Emilia, Italy was the first one to perform a thought-provoking speech: Artificial Intelligence Applications in Logistics. And then we had Assoc. Prof. Lingling An, from Xidian University, China. She presented an insightful speech: Enhancement Mechanism of Cerebellar Neural Network Dynamics. Prof. Xinsong Yang, from Sichuan University, China. He made a wonderful speech: Synchronization And Control for Differential Equations with Discontinuous State on the Right-Hand Sides. Prof. Xinhua Tang, from Southeast University, China. His research topics include GNSS receiver techniques, GNSS/INS integrated navigation system, Multi-source integration systems, Precision agriculture, Autonomous vehicles etc. Prof. Haibin Zhu, from Nipissing University, Canada. He delivered a speech: E-CARGO and Role-Based Collaboration. Assoc. Prof. Marcin Paprzycki, from Systems Research Institute, Polish Academy of Sciences, Poland. His original research interests were in the area of high performance computing / parallel computing / computational mathematics. Over time, they shifted towards intelligent systems, software agents and agent systems, and application of semantic technologies, among others. Our finale keynote speaker, Prof. Hoshang Kolivand, from Liverpool John Moores University, UK was invited to perform a speech: Current and Future of Directions of Augmented and Virtual Reality. Their insightful speeches had triggered heated discussion in the third session of the conference. The WeChat discussion lasted for about 30 minutes. Every participant praised this conference for disseminating useful and insightful knowledge.We are glad to share with you that we received lots of submissions from the conference and we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Signal Processing, Applications of Signal Processing, Communication and Broadband Networks, Computational Science and other relevant directions. All the papers have been through rigorous review and process to meet the requirements of international publication standard.We would like to thank the organization staff, the members of the program committees and reviewers. They have worked very hard in reviewing papers and making valuable suggestions for the authors to improve their work. We also would like to express our gratitude to the external reviewers, for providing extra help in the review process, and the authors for contributing their research result to the conference.Lastly, we would like to warmly thank all the authors who, with their presentations and papers, generously contributed to the lively exchange of scientific information that is so vital to the endurance of scientific conferences of this kind.Committee of SPCS 2021List of titles Committee member are available in this Pdf.

Resource Utilization-Aware Collaborative Optimization of IaaS Cloud Service Composition for Data-Intensive Applications

Recently, growing cloud services (CSs) have been leased by organizations for high-performance computation and massive data storage of data-intensive applications (DiAs). To improve the resource utilization of leased CSs, it has become a challenging task to optimize infrastructure as a service CS composition for DiAs (ICSCDs) from the user side. This paper proposes a resource utilization-aware collaborative optimization approach. Targeting the collaboration features of tasks in a DiA, the environments-classes, agents, roles, groups, and objects model is used to formalize the ICSCD problem from the perspective of role-based collaboration. Aiming at the dynamic characteristics of the cloud environment, an integrated method is presented to evaluate the qualification of CSs via the interval numbers with multiple parameters. Based on the exact qualification values, the ICSCD can be optimized for improving the resource utilization of the CSs. A solution using the IBM ILOG CPLEX optimization package is put forward to solve the problem. The experimental results demonstrate that the approach can provide high precision, performance, stability, resource utilization, and low usage cost for the resource utilization-aware ICSCD from the user side.

A Utility Method for the Matching Optimization of Ride‐Sharing Based on the E‐CARGO Model in Internet of Vehicles

The Internet of Vehicles (IoV) is the extension of the Internet of Things (IoT) technology in the field of transportation systems. Ride‐sharing is one of intelligent travel applications in IoV. Ride‐sharing is aimed at taking passengers with similar itineraries and time arrangements to travel in the same car according to a certain matching rule. To effectively integrate transport capacity resources and reduce the number of cars on the road, ride‐sharing has become a popular and economical way of travel. The matching and optimizing of drivers and passengers are the core contents of a ride‐sharing application system. This paper mainly studies the dynamic real‐time matching of passengers and drivers in IoV, considering the main factors such as travel cost, car capacity, and utility. The matching problem is formulated in a ride‐sharing system as a Role‐Based Collaboration (RBC). A new utility method for the matching optimization of ride‐sharing is present. In this paper, we establish a model for simulating the assignment of ride‐sharing with the help of the Environments‐Classes, Agents, Roles, Groups, and Objects (E‐CARGO) model. The objective function and formal definitions are proposed. The utility and time of optimal matching are obtained by using the Kuhn‐Munkres algorithm on the revenue matrix. The experimental results show that the proposed formal method based on the E‐CARGO model and utility theory can be applied in the ride‐sharing problem. Numerical experiments show that the matching time cost increases with the increase of the number of drivers and passengers participating in the ride‐sharing system. When the number of drivers and passengers is different, one‐to‐many matching takes the least time, and one‐to‐one matching takes more time. When the number of drivers and passengers is the same, the time cost of one‐to‐one matching increases sharply with a certain value (bigger than 800). Compared with other matching methods, the time spent by the one‐to‐many method is reduced by 30%. The results show that the proposed solution can be applied to the matching and pricing in a ride‐sharing system.

Computational Social Simulation With E-CARGO: Comparison Between Collectivism and Individualism

Computational social simulation is a long-term, cutting-edge topic in the interdisciplinary field where information technology, computer science, social science, and sociology overlap. In this article, we establish the fundamental requirements for social simulation and demonstrate that the Environments—Classes, Agents, Roles, Groups, and Objects (E-CARGO) model for role-based collaboration (RBC) and the subsequent group role assignment (GRA) optimization model are highly qualified to meet these requirements. Based on E-CARGO and GRA, we propose a new approach to social simulation and conduct a case study to verify this approach. This case study involves a comparison between collectivism and individualism. The contribution of this work is a novel approach to social simulation using E-CARGO and GRA. This approach reveals the exciting results that explain social phenomena, e.g., collectivism is better than individualism if the team manager is perfect in the evaluation process, and individualism can beat collectivism without much difficulty if the team manager is not perfect.

Group multi-role assignment with conflicting roles and agents

Group role assignment &#x0028 GRA &#x0029 is originally a complex problem in role-based collaboration &#x0028 RBC &#x0029 . The solution to GRA provides modelling techniques for more complex problems. GRA with constraints &#x0028 GRA &#x002B &#x0029 is categorized as a class of complex assignment problems. At present, there are few generally efficient solutions to this category of problems. Each special problem case requires a specific solution. Group multi-role assignment &#x0028 GMRA &#x0029 and GRA with conflicting agents on roles &#x0028 GRACAR &#x0029 are two problem cases in GRA &#x002B . The contributions of this paper include: 1 &#x0029 The formalization of a new problem of GRA &#x002B , called group multi-role assignment with conflicting roles and agents &#x0028 GMAC &#x0029 , which is an extension to the combination of GMRA and GRACAR; 2 &#x0029 A practical solution based on an optimization platform; 3 &#x0029 A sufficient condition, used in planning, for solving GMAC problems; and 4 &#x0029 A clear presentation of the benefits in avoiding conflicts when dealing with GMAC. The proposed methods are verified by experiments, simulations, proofs and analysis.

Criteria Making in Role Negotiation

Role negotiation is a pivotal step in the role-based collaboration (RBC) process. There are many factors to be considered when decision makers set up criteria to evaluate group performance and agents’ abilities. In this paper, we investigate the applications of three traditional multicriteria decision making methods into role negotiation of RBC, i.e., simple additive weighting, multiplication exponent weighting, and weighted distance. The goal is to acquire ideal and satisfactory methods to set up the evaluation criteria for the role negotiation task. The main contributions of this paper include: 1) a method of generating the matrix of agent qualifications, with a discrete, usually, limited, and finite set of prespecified attributes and values; 2) the upper and lower thresholds of a role’s ability requirements, which are critical parameters for a workable team and its performance; and 3) the potential influences of specialist agents and generalist agents on the group performance. The proposed methods are verified by simulation experiments. The simulation results show that the proposed methods are reasonable, feasible, and practical.

Avoiding Critical Members in a Team by Redundant Assignment

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments—classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues.

Collaborative Optimization of Service Composition for Data-Intensive Applications in a Hybrid Cloud

The multi-valued evaluations of quality of service (QoS), the complicated constraints between cloud services (CSs) and the collaborative resource assignments add many difficulties to the problem of CS composition for data-intensive applications (DiA) in a hybrid cloud (CSCD-HC). Solving the CSCD-HC problem has become a challenging task due to the uncertain QoS, the diverse hardware configurations and the flexible pricing about CSs. This paper proposes a collaborative optimization approach for CSCD-HC. This approach models a DiA as a role-based collaboration (RBC) system and employs the environments-classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD-HC problem with complicated constraints. To deal with the multi-valued QoS evaluations, this paper exploits the cloud model theory to analyze the performance of CSs, and presents a new method utilizing the Mahalanobis distance to improve the similarity calculation of QoS cloud models. Based on it, the qualification of candidate CSs can be precisely measured for supporting CS composition. A solution via the IBM ILOG CPLEX optimization package is put forward to solve the CSCD-HC problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing CSCD-HC.

Big Data and Collaboration Research: Creating Real Big Data

Does it make sense to make connections between big data and collaboration research? I was asked such a question during one of my presentations in China months ago when I mentioned that role-based collaboration (RBC) and the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model might contribute to innovative research topics, including big data. I hope to clarify this opinion and answer that question.