• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.47-53
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• 2016

A type of workflow affiliation network is one of the specialized social network types, which represents the associative relation between actors and activities. There are many methods on a workflow affiliation network measuring centralities such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In particular, we are interested in the closeness centrality measurements on a workflow affiliation network discovered from enterprise workflow models, and we know that the time complexity problem is raised according to increasing the size of the workflow affiliation network. This paper proposes an estimated ranking algorithm and analyzes the accuracy and average computation time of the proposed algorithm. As a result, we show that the accuracy improves 47.5%, 29.44% in the sizes of network and the rates of samples, respectively. Also the estimated ranking algorithm's average computation time improves more than 82.40%, comparison with the original algorithm, when the network size is 2400, sampling rate is 30%.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.109-118
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• 2012

This paper theoretically derives an algorithm to discover a new type of workflow-based knowledge from workflow models, which is termed workflow-based affiliation network knowledge. In general, workflow intelligence (or business process intelligence) technology consists of four types of techniques that discover, analyze, monitor and control, and predict a series of workflow-based knowledge from workflow models and their execution histories. So, this paper proposes a knowledge discovery algorithm which is able to discover workflow-based affiliation networks that represent the association and participation relationships between activities and performers defined in ICN-based workflow models. In order particularly to prove the correctness and feasibility of the proposed algorithm, this paper tries to apply the algorithm to a specific workflow model and to show that it is able to derive its corresponding workflow-based affiliation network knowledge.

• 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.

• 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 Digital Contents Society
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• v.19 no.1
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• pp.149-155
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• 2018

In this paper we propose an algorithm for generating role-performer bipartite matrix for analyzing BPM-based human resource affiliations. Firstly, the proposed algorithm conducts the extraction of role-performer affiliation relationships from ICN(Infromation Contorl Net) based business process models. Then, the role-performer bipartite matrix is constructed in the final step of the algorithm. Conclusively, the bipartite matrix generated through the proposed algorithm ought to be used as the fundamental data structure for discovering the role-performer affiliation networking knowledge, and by using a variety of social network analysis techniques it enables us to acquire valuable analysis results about BPM-based human resource affiliations.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.217-223
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• 2024

The current study aimed to evaluate the effectiveness of using Support Vector Machine (SVM) for political affiliation classification. The system was designed to analyze the political tweets collected from Twitter and classify them as positive, negative, and neutral. The performance analysis of the SVM classifier was based on the calculation of metrics such as accuracy, precision, recall, and f1-score. The results showed that the classifier had high accuracy and f1-score, indicating its effectiveness in classifying the political tweets. The implementation of SVM in this study is based on the principle of Structural Risk Minimization (SRM), which endeavors to identify the maximum margin hyperplane between two classes of data. The results indicate that SVM can be a reliable classification approach for the analysis of political affiliations, possessing the capability to accurately categorize both linear and non-linear information using linear, polynomial or radial basis kernels. This paper provides a comprehensive overview of using SVM for political affiliation analysis and highlights the importance of using accurate classification methods in the field of political analysis.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.115-115
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• 2014

First, We will briefly introduce early science results of AGN observations with KVN and KaVA. KaVA is the combined array of the Korean VLBI network (KVN) and VLBI Exploration of Radio Astronomy (VERA). These include KaVA monitoring of M87, Sgr A* and a few bright blazars and KVN Search for circular polarized Blazars. Furthermore, we will present our future plan of monitoring observation of Sgr A* and M87 with KaVA and Low Radio Power AGN multi frequency polarization survey with KVN. Because of the largeness of their centralsuper-massive black holes, we select them as top-priority sources of our key science program (KSP). The main science goals of the KaVA KSP are (1) mapping the velocity field of the M87 jet and testing magnetically-driven-jet paradigm, and (2) obtaining tightest constraints on physical properties of radio emitting region in Sgr A. High sensitivity achieved through simultaneous multifrequency phase referencing technique of KVN will allow us to explore Low Radio Power AGN cores which build majority of AGNs and therefore are important for undestanding the evolution of AGNs and of their hosts.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.547-558
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• 2012

Social network analysis is a graphical technique to search the relationships and characteristics of nodes (people, companies, and organizations) and an important node for positioning a visualized social network figure; however, it is difficult to characterize nodes in a social network figure. Therefore, their relationships and characteristics could be presented through an application of correspondence analysis to an affiliation matrix that is a type of similarity matrix between nodes. In this study, we provide the relationships and characteristics around authors and keywords in the JKSS(Journal of the Korean Statistical Society) of the Korean Statistical Society through the use of social network analysis and correspondence analysis.

• Korea Journal of Hospital Management
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• v.11 no.4
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• pp.63-81
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• 2006

This study was conducted to evaluate needs and intention of hospitals and clinics to join network with private health insurance, and to discover obstacles of participation of the networks. We carried out the questionnaire survey of the network managers of 236 medical institutions between December 27th, 2005 and January 25th, 2006. The result showed that the participation intention of network were different to the type of hospitals. Primary care clinics answered that participation intention and possibility were low. Secondary care hospitals was relatively affirmative regarding a network participation. Tertiary hospitals responded that they need the network with private health insurance, but participation possibility was lower than needs. The reason is that they worried about the side effect of the network with private health insurance. Depending on the type of hospitals, expected benefits from networking with private health insurance were different. We found that hospitals which already had affiliation with other hospitals answered in the affirmative regarding the network with private health insurance. In conclusion, to increase the effectiveness of network systems between hospital and private health insurance, the network is expected to consider different needs of the each hospital.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3568-3584
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• 2016

In mobile opportunistic networks (MONs), human-carried mobile devices such as PDAs and smartphones, with the capability of short range wireless communications, could form various intermittent contacts due to the mobility of humans, and then could use the contact opportunity to communicate with each other. The dynamic changes of the network topology are closely related to the human mobility patterns. In this paper, we propose a social motivation-aware mobility model for MONs, which explains the basic laws of human mobility from the psychological point of view. We analyze and model social motivations of human mobility mainly in terms of expectancy value theory and affiliation motivation. Furthermore, we introduce a new concept of geographic functional cells, which not only incorporates the influence of geographical constraints on human mobility but also simplifies the complicated configuration of simulation areas. Lastly, we validate our model by simulating three real scenarios and comparing it with reality traces and other synthetic traces. The simulation results show that our model has a better match in the performance evaluation when applying social-based forwarding protocols like BUBBULE.