• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.77-85
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• 2013

This paper proposes a closeness centrality analysis algorithm for workflow-supported social networks that represent the collaborative relationships among the performers who are involved in a specific workflow model. The proposed algorithm uses the social network analysis techniques, particularly closeness centrality equations, to analyze the closeness centrality of the workflow-supported social network. Additionally, through an example we try to verify the accuracy and appropriateness of the proposed algorithm.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.65-71
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• 2016

In this paper, we propose a novel analysis method and its algorithms to perform the betweenness centrality measurements on a valued and directed workflow-supported social network. The conventional workflow-supported social network model is to represent the existences of task transferring relationships among their performers by using a binary social network. However, it is necessary to consider not only the existences of task transferring relationships but also their quantities and directions in order to obtain much more effective and sophisticated analysis results. In conclusion, this paper newly defines a concept of valued and directed workflow-supported social network, and its betweenness centrality analysis method and algorithms. Especially, to verify the proposed method and algorithms, we try to apply the conventional method and the proposed method to an example workflow model respectively, and compare their betweenness centrality analysis results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1454-1466
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• 2016

This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.71-77
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• 2015

This paper implements an estimated closeness centrality ranking algorithm in large-scale workflow-supported social networks and performance analyzes of the algorithm. Existing algorithm has a time complexity problem which is increasing performance time by network size. This problem also causes ranking process in large-scale workflow-supported social networks. To solve such problems, this paper conducts comparison analysis on the existing algorithm and estimated results by applying estimated-driven RankCCWSSN(Rank Closeness Centrality Workflow-supported Social Network). The RankCCWSSN algorithm proved its time-efficiency in a procedure about 50% decrease.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3216-3230
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• 2015

A hot-issued research topic in the workflow intelligence arena is the emerging topic of "workflow-supported organizational social networks." These specialized social networks have been proposed to primarily represent the process-driven work-sharing and work-collaborating relationships among the workflow-performers fulfilling a series of workflow-related operations in a workflow-supported organization. We can discover those organizational social networks, and visualize its analysis results as organizational knowledge. In this paper, we are particularly interested in how to visualize the degrees of closeness centralities among workflow-performers by proposing a graphical representation schema based on the Graph Markup Language, which is named to ccWSSN-GraphML. Additionally, we expatiate on the functional expansion of the closeness centralization formulas so as for the visualization framework to handle a group of workflow procedures (or a workflow package) with organizational workflow-performers.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.67-73
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• 2015

In this paper, we propose a disconnectedness determination algorithm for workflow-supported enterprise social networks. this networks can be discovered from the work-allocation relationships between activities and performers in workflow procedures and It networks are useful to analyze, evaluate and numerically explain the work-performance of performers. But there could be the disconnectedness due to the characteristics of workflow, and therefore analysis result of performers can be inappropriate logically as well as mathematically. So, we have to decide whether or not this network is disconnectedness before we analyze workflow-supported enterprise social networks. Conclusively, we try to carry out a thorough investigation into the formation reasons of the disconnectedness, and propose a determination algorithm of the disconnectedness for workflow-supported enterprise social networks.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.25-34
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• 2013

In this paper, we propose an activity-performer bipartite matrix generation algorithm for analyzing workflow-supported human-resource affiliations in a workflow model. The workflow-supported human-resource means that all performers of the organization managed by a workflow management system have to be affiliated with a certain set of activities in enacting the corresponding workflow model. We define an activity-performer affiliation network model that is a special type of social networks representing affiliation relationships between a group of performers and a group of activities in workflow models. The algorithm proposed in this paper generates a bipartite matrix from the activity-performer affiliation network model(APANM). Eventually, the generated activity-performer bipartite matrix can be used to analyze social network properties such as, centrality, density, and correlation, and to enable the organization to obtain the workflow-supported human-resource affiliations knowledge.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.59-66
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• 2015

A stochastic modeling approach as a mathematical method for workflow intelligence is widely used for analyzing and simulating workflow models in the literature. In particular, as a resource-centric modeling approach, this paper proposes a stochastic model to represent work-handover relationships between performers in a workflow-supported social network. Calculating probabilities for the work-handover relationships are determined by two types of probabilities. One is the work-transition probability between activities, and the other is the task assignment probability between activities and performers. In this paper, we describe formal definitions of stochastic workflow models and stochastic work-handover relationship models, as well. Then, we propose an algorithm for extracting a stochastic work-handover relationship model from a stochastic workflow model. As a consequence, the proposed model ought to be useful in performing resource-centric workflow simulations and model-log comparison analyses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3611-3634
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• 2015

In this paper, we build a theoretical framework for quantitatively measuring and graphically representing the degrees of closeness centralization among performers assigned to enact a workflow procedure. The degree of closeness centralization of a workflow-performer reflects how near the performer is to the other performers in enacting a corresponding workflow model designed for workflow-supported organizational operations. The proposed framework comprises three procedural phases and four functional transformations, such as discovery, analysis, and quantitation phases, which carry out ICN-to-WsoN, WsoN-to-SocioMatrix, SocioMatrix-to-DistanceMatrix, and DistanceMatrix-to-CCV transformations. We develop a series of algorithmic formalisms for the procedural phases and their transformative functionalities, and verify the proposed framework through an operational example. Finally, we expatiate on the functional expansion of the closeness centralization formulas so as for the theoretical framework to handle a group of workflow procedures (or a workflow package) with organization-wide workflow-performers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.691-708
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• 2014

This paper formalizes a special type of social networking knowledge, which is called "workflow performer-role affiliation networking knowledge." A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. In Particular, these affiliated relationships exhibit a stream of organizational work-sharing knowledge and utilize business process intelligence to explore resources allotting and planning knowledge concealed in the corresponding workflow model. In this paper, we particularly focus on the performer-role affiliation relationships and their implications as organizational and business process intelligence in workflow-driven organizations. We elaborate a series of theoretical formalisms and practical implementation for modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge, and practical details as workflow performer-role affiliation knowledge representation, discovery, and visualization techniques. These theoretical concepts and practical algorithms are based upon information control net methodology for formally describing workflow models, and the affiliated knowledge eventually represents the various degrees of involvements and participations between a group of performers and a group of roles in a corresponding workflow model. Finally, we summarily describe the implications of the proposed affiliation networking knowledge as business process intelligence, and how worthwhile it is in discovering and visualizing the knowledge in workflow-driven organizations and enterprises that produce massively parallel interactions and large-scaled operational data collections through deploying and enacting massively parallel and large-scale workflow models.