Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the TEAM FORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the TEAM FORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.

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Finding a team of experts in social networks

How to select suitable partners is critical to virtual enterprise (VE) success and has attracted much attention for both researchers and practitioners. In many practical situations, there usually exists incomplete and uncertain information, and the decision makers cannot easily express their judgments on the candidates with exact and crisp values. Therefore, an extended technique for order preference by similarity to ideal solution (TOPSIS) method for group decision making with interval-valued intuitionistic fuzzy numbers is proposed to solve the partner selection problem under incomplete and uncertain information environment in this paper. And the feasibility and practicability of the extended TOPSIS method are further manifested through an illustrative example. Results show that this extended TOPSIS method for group decision making with interval-valued intuitionistic fuzzy numbers is more suitable to cope with the partner selection problem under incomplete and uncertain information environment.

An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection

We study the problem of discovering a team of experts from a social network. Given a project whose completion requires a set of skills, our goal is to find a set of experts that together have all of the required skills and also have the minimal communication cost among them. We propose two communication cost functions designed for two types of communication structures. We show that the problem of finding the team of experts that minimizes one of the proposed cost functions is NP-hard. Thus, an approximation algorithm with an approximation ratio of two is designed. We introduce the problem of finding a team of experts with a leader. The leader is responsible for monitoring and coordinating the project, and thus a different communication cost function is used in this problem. To solve this problem, an exact polynomial algorithm is proposed. We show that the total number of teams may be exponential with respect to the number of required skills. Thus, two procedures that produce top-k teams of experts with or without a leader in polynomial delay are proposed. Extensive experiments on real datasets demonstrate the effectiveness and scalability of the proposed methods.

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Discovering top-k teams of experts with/without a leader in social networks

EU industry accounts for 15% of European value added and the industrial sector plays the role of a key driver for research, innovation, productivity, job creation and exports. One important measure in order to stop the decline of Europe’s global industrial position is represented by Industry 4.0 aiming for the implementation of smart production and logistics. Industry 4.0 touches the entire supply chain comprising product design and development, operations management and logistics and by doing so new business models and structures are required. Currently, companies start to practice first steps in 3D printing, production in networks and smart logistics and begin to develop new organisational structures and business models to benefit more from the opportunities that the new technology offers. Empirical evidences of successful companies reveals that the new business models are oriented rather on service design, open innovation and network approaches than on the traditional concepts of industrial enterprises. Consequently, traditional industrial companies have to rethink and to renew their business structures and models in parallel with the ongoing implementation of Industry 4.0 to use the new opportunities. The main focus of Industry 4.0 is laid on the fusion of the virtual and the physical world so that also new concepts are required for managing information and business administration tasks in the context of Industry 4.0. One big step towards the implementation of such a concept is embodied by the Estonian concept of “e-Residency”, which might be an appropriate e-business approach especially when taking into account the needs of internationally operating entrepreneurs and SME’s. The paper addresses the research question of how new and sustainable business models and structures for Industry 4.0 might look like and in which direction existing traditional business concepts have to be developed to deploy a strong business impact of Industry 4.0. By focussing on the needs of entrepreneurs and SME sector the paper also discusses why e-residency might be the appropriate concept in the context of Industry 4.0.

Sustainable business models and structures for industry 4.0

Reusing APIs of existing libraries is a common practice during software development, but searching suitable APIs and their usages can be time-consuming [6]. In this paper, we study a new and more practical approach to help users find usages of APIs given only simple text phrases, when users have limited knowledge about an API library. We model API invocations as an API graph and aim to find an optimum connected subgraph that meets users' search needs.The problem is challenging since the search space in an API graph is very huge. We start with a greedy subgraph search algorithm which returns a connected subgraph containing nodes with high textual similarity to the query phrases. Two refinement techniques are proposed to improve the quality of the returned subgraph. Furthermore, as the greedy subgraph search algorithm relies on online query of shortest path between two graph nodes, we propose a space-efficient compressed shortest path indexing scheme that can efficiently recover the exact shortest path. We conduct extensive experiments to show that the proposed subgraph search approach for API recommendation is very effective in that it boosts the average F1-measure of the state-of-the-art approach, Portfolio [15], on two groups of real-life queries by 64% and 36% respectively.

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Searching connected API subgraph via text phrases

Appointing a good leader to the position of team manager and having competent workers collaborate as team members is a key to success in business activities of an enterprising institution. The traditional methodologies of human resource management have defined the required abilities for team managers and team members, and evaluated those abilities of employees. However, it is difficult to consider those abilities systematically in practice. In addition, the current management paradigm undergoes rapid transitions into knowledge management. In step with these trends, this study presents a framework for analyzing the knowledge of the candidates for managers and team members for the new team, and proposes a genetic algorithm and social network measures for choosing a team manager and team members. A prototype was built for testing the feasibility of the model. The testing data are from an R&D institute's human resource management department. The results show that our proposed approach is a quantitative and systematic method for selecting proper personnel for appropriate teams.

A team formation model based on knowledge and collaboration

Manufacturing enterprise collaboration based on a goal-oriented fuzzy trust evaluation model in a virtual enterprise

Self-evolution framework of manufacturing systems based on fractal organization

Virtual organization (VO) is a main organizational paradigm for enterprises to collaborate in the rapidly changing environment. Advances in information and communication technologies are accelerating collaboration among enterprises in different domains as well as unknown enterprises. In this environment, an enterprise faces more chances to cooperate with other enterprises through the internet. Trust is a key concept in many kinds of interactions, allowing an enterprise to act under uncertainty and with the risk of negative consequences. To minimize the operating risk and guarantee the success of a VO, trust evaluation of potential partners should be considered during the partner selection process. However, trust evaluation methods in the literature have limitations in applying them to small and medium enterprises because they are based on evaluation of human experts. In this paper, a goal-oriented trust model is proposed to evaluate the trust values of enterprises. In the context, trust is the probability that a trustee satisfies the goals when it completes assigned tasks. This paper introduces the concept of a goal-oriented trust model with the core constructs of a goal, a project, a task, and a capability. It further addresses an example of applying the proposed model to the partner selection process of a project-based VO creation.

A goal-oriented trust model for virtual organization creation

In this era of rapid changes in the project-oriented R&D organization's environment, some are actively pursuing joint research to gain a leading edge over other R&D organizations. The condition for joint research is the knowledge that an organization needs from other organizations and the capability of collaboration. This study presents a ProVO model using the concept of virtual organization and project team formation based on knowledge and collaboration. In this model, VO is represented by the capability of carrying out a project and the cost of employment. Capability consists of knowledge competence (KC) and collaboration competence (CC). KC, in turn, consists of individual knowledge and collective knowledge from social network, while CC consists of density, degree centrality, and closeness centrality. To verify the presented model, we conducted a case study on a research institute. The analysis results show that all five project team formation factors of KC and CC are statistically significant. A prototype was also developed for selecting project team members using the binary logistics regression model. The proposed ProVO model can assist quantitative decision making on the selection of project team members by a project-oriented R&D organization from the aspects of knowledge and collaboration.

Jungtae Mun

Papers

A team formation model based on knowledge and collaboration

Manufacturing enterprise collaboration based on a goal-oriented fuzzy trust evaluation model in a virtual enterprise

Self-evolution framework of manufacturing systems based on fractal organization

A goal-oriented trust model for virtual organization creation

Modeling and analysis of project team formation factors in a project-oriented virtual organization (ProVO)