• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4108-4122
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• 2019

Workflow process mining is becoming a more and more valuable activity in workflow-supported enterprises, and through which it is possible to achieve the high levels of qualitative business goals in terms of improving the effectiveness and efficiency of the workflow-supported information systems, increasing their operational performances, reducing their completion times with minimizing redundancy times, and saving their managerial costs. One of the critical challenges in the workflow process mining activity is to devise a reasonable approach to discover and recognize the bottleneck points of workflow process models from their enactment event histories. We have intuitively realized the fact that the iterative process pattern of redo-activities ought to have the high possibility of becoming a bottleneck point of a workflow process model. Hence, we, in this paper, propose an algorithmic approach and its implementation to discover the redo-activities and their performers' involvements patterns from workflow process enactment event logs. Additionally, we carry out a series of experimental analyses by applying the implemented algorithm to four datasets of workflow process enactment event logs released from the BPI Challenges. Finally, those discovered redo-activities and their performers' involvements patterns are visualized in a graphical form of information control nets as well as a tabular form of the involvement percentages, respectively.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.101-108
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• 2019

Workflow management systems (WfMSs) automate and manage workflows, which are implementations of organizational processes operated in process-centric organizations. In this paper, wepropose an algorithm to discover temporal work transference networks from workflow execution logs. The temporal work transference network is a special type of enterprise social networks that consists of workflow performers, and relationships among them that are formed by work transferences between performers who are responsible in performing precedent and succeeding activities in a workflow process. In terms of analysis, the temporal work transference network is an analytical property that has significant value to be analyzed to discover organizational knowledge for human resource management and related decision-making steps for process-centric organizations. Also, the beginning point of implementinga human-centered workflow intelligence framework dealing with work transference networks is to develop an algorithm for discovering temporal work transference cases on workflow execution logs. To this end, we first formalize a concept of temporal work transference network, and next, we present a discovery algorithm which is for the construction of temporal work transference network from workflow execution logs. Then, as a verification of the proposed algorithm, we apply the algorithm to an XES-formatted log dataset that was released by the process mining research group and finally summarize the discovery result.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.149-156
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• 2021

Text mining is one of the useful tools to discover public opinion and perception regarding political issues from big data. It is very common that users of social media express their opinion with newly-coined words such as slang and emoji. However, those new words are not effectively captured by traditional text mining methods that process text data using a language dictionary. In this study, we propose effective methods to extract newly-coined words that connote the political stance and opinion of users. With various text mining techniques, I attempt to discover the context and the political meaning of the new words.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3996-4004
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• 2022

The data collected by the In-Situ Decommissioning (ISD) sensors are time-specific, age-specific, and developmental stage-specific. Research has been done on the stream data collected by ISD testbed in the recent few years to seek both frequent episodes and abnormal frequent episodes. Frequent episodes in the data stream have confirmed the daily cycle of the sensor responses and established sequences of different types of sensors, which was verified by the experimental setup of the ISD Sensor Network Test Bed. However, the discovery of abnormal frequent episodes remained a challenge because these abnormal frequent episodes are very small signals and may be buried in the background noise of voltage and current changes. In this work, we proposed Advanced Data Analytics (ADA) methods that are applied to the baseline data to identify frequent episodes and extended our approach by adding more features extracted from the baseline data to discover abnormal frequent episodes, which may lead to the early indicators of ISD system failures. In the study, we have evaluated our approach using the baseline data, and the performance evaluation results show that our approach is able to discover frequent episodes as well as abnormal frequent episodes conveniently.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.627-628
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• 2015

Construction defect which can causes economic damage such as schedule delay, cost overrun is a considerably important factor in construction industry. In general, a construction defect features a difficulty to find out causes precisely because it occurs when several interrelated causes combine. Yet, studies have tried to understand the interrelationships between factors are limited. In addition, despite of a tremendous amount of construction data, it's not still enough to analyze them, but tends to depend on experience or know-how of practitioners. Thus, it is necessary to identify underlying causes in influential factors by utilizing related data. This paper analyses Interrelationships between causal factors using Association Rule Mining to discover root causes of construction defects. Confidence and Lift that can be used for presenting the interrelationships of the causes were extracted from 1241 cases in 30 projects in Korea. It is expected that this paper allows the construction managers to discover key factors and make right decisions to reduce occurrence of construction defects. Furthermore, analysis of interrelationships can improve understanding of structural patterns of construction defects.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.513-522
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• 2008

Service-Oriented Architecture(SOA) is an method to develop applications by developing and publishing reusable services which potentially be used in various applications, and discovering and composing right services dynamically. SOA adopts a paradigm of publish-discover-invoke, which is considerably different from object-oriented and component-based development(CBD) approaches. The service in SOA is different from function in procedural programming, object in object-oriented programming, and component in CBD, and its fundamental requirement is a high level of reusability and applicability. In SOA, service providers and service consumers are loosely coupled since the providers try to develop reusable services and the consumers try to locate right services without knowing much about the providers and their published services. Moreover, the process of searching, choosing and invoking right services is not presented in conventional programming paradigms. Therefore, conventional approaches to developing user interfaces and invoking the functionality on servers through JSP, and RMI in object-oriented programming cannot well be applicable to designing clients’ programs in SOA. Therefore, there is a high demand for a practical and systematic process for developing clients’ applications, and the such a process should be devised by considering key characteristics of services and SOA. However, little work on this area is known to date, and there has not a process for client side just except few guide lines for developing service client. In this paper, we propose a practical and systematic development process for developing clients' applications in SOA. Then, we define instructions for carrying out each activity in the process. To show the applicability of the proposed work, we show the result of applying our process in developing a services application for searching and booking hotels.

• Journal of Astronomy and Space Sciences
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• v.13 no.1
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• pp.9-18
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• 1996

CCD differential photometry was carried out for seven stars in the "New Catalogue of Suspected Variable Stars" in order to discover new short-period pulsating variables such as low and high amplitude $\delta$Scuti stars and RR Lyrate stars. It was found that NSV1132, NSV3031, and NSV5119 are RRs, Irregular, and low amplitude $\delta$Scuti type variables respectively.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.55-57
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• 2004

본 논문은 온톨로지를 활용한 스키마 매칭 알고리즘을 제안한다. 기존의 대부분의 스키마 매칭 방법은 단순매칭을 대상으로 하는 반면, 제안된 방법은 계층적 구조의 온톨로지에 기반하여 복합매칭을 계산할 수 있다. 특히 제안된 온톨로지는 이전의 매칭결과에 대한 사용자의 피드백을 이용하여 자동으로 갱신됨에 따라 적절한 도메인 정보를 유지할 수 있다. 성능평가를 위한 실험결과, 온톨로지의 적용이 매칭 성능을 향상시킴을 확인할 수 있었다.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.05a
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• pp.27-30
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• 2002

PMFBG sensor was fabricated using phase mask and Excimer laser. The reflected wavelength of PMFBG sensor had dual peaks due to intrinsic birefringence. To discover the polarization axes, peak sensitivity was measured under compression test. The signal characteristics of PMFBG sensor were also examined in embedding condition. The embedded PMFBG sensor in epoxy block was loaded for the transverse strain measurements. Experiments showed that the PMFBG sensor could successfully measure the transverse strain. This PMFBG sensor is useful for the structures that require measuring transverse stram.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.361-363
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• 2001

Although data mining promises a new paradigm to discover medical knowledge form a database, there are many problems to be solved before real application is feasible. We had the chance to provide a data set to be analyzed as a discovery challenge by using various data mining techniques at the PKDD conference. As data providers, we evaluated and discussed results and clarified problems.