• Asia pacific journal of information systems
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• v.23 no.1
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• pp.87-106
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• 2013

In the service-oriented economy, knowledge and skills are considered core resources to secure competitive advantages and service innovation. Knowledge management capability, which facilitates to produce, share, accumulate and reuse knowledge, becomes as important as knowledge itself to create service value. Along with knowledge management capability, dynamic capability and operational capability are the key capabilities related to managing service delivery processes. Previous studies indicated that these three capabilities are related to service innovation. Although separately investigate the relationship between the three capabilities. The purpose of this study is 1) to define variables that have effects on service innovation including knowledge management capability, dynamic capability and operational capability, and 2) to empirically test to identify relationship among variables. In this study, knowledge management capability is defined as the capability to manage knowledge process. Dynamic capability is regarded as the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Operational capability refers to a high-level routine that, together with its implementing input flows, confers upon an organization's management a set of decision options for producing significant outputs of a particular type. The proposed research model was tested against the data collected through the survey method. The survey questionnaire was distributed to the managers who participated in an educational program for management consulting. Each individual who answered the questionnaire represented a knowledge based service firm. About 212 surveys questionnaires were sent via e-mail or directly delivered to respondents. The number of useable responses was 93. Measurement items were adapted from previous studies to reflect the characteristics of the industry each informant worked in. All measurement items were in, 5 point Likert scale with anchors ranging from strongly disagree (1) to strongly agree (5). Out of 93 respondents, about 81% were male, 82% of respondents were in their 30s. In terms of jobs, managers were 39.78%, professions/technicians were 24.73%, researchers were 12.90%, and sales people were 10.75%. Most of respondents worked for medium size enterprises (47,31%) in their, less than 30 employees (46.24%) in their number of employees, and less than 10 million USD (65.59%) in terms of sales volume. To test the proposed research model, structural equation modeling (SEM) technique (SPSS 16.0 and AMOS version 5) was used. We found that the three organizational capabilities have influence on service innovation directly or indirectly. Knowledge management capability directly affects dynamic capability and service innovation but indirectly affect operational capability through dynamic capability. Dynamic capability has no direct impact on service innovation, but influence service innovation indirectly through operational capability. Operational capability was found to positively affect service innovation. In sum, three organizational capabilities (knowledge management capability, dynamic capability and operational capability) need to be strategically managed at firm level, because organizational capabilities are significantly related to service innovation. An interesting result is that dynamic capability has a positive effect on service innovation only indirectly through operational capability. This result indicates that service innovation might have a characteristics similar to process innovation rather than product orientation. The results also show that organizational capabilities are inter-correlated to influence each other. Dynamic capability enables effective resource management, arrangement, and integration. Through these dynamic capability affected activities, strategic agility and responsibility get strength. Knowledge management capability intensify dynamic capability and service innovation. Knowledge management capability is the basis of dynamic capability as well. The theoretical and practical implications are discussed further in the conclusion section.

• Journal of The Korean Dental Society of Anesthesiology
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• v.14 no.1
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• pp.17-27
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• 2014

Background: Various medical emergency situations can occur during dental practices. Cardiac arrest is known to comprise approximately 1% of emergency situation. Thus, it is necessary for dentists to be able to perform cardiopulmonary resuscitation (CPR) to increase the chance of saving patient's life in emergency situation. In this paper, we conducted a survey study to evaluate to what extent dentists actually understood CPR practice and if they had experience in handling emergency situations in practice. Method: The survey was done for members of the Korean Dental Society of Anesthesiology (KDSA), who had great interest in CPR and for whom survey-by-mail was convenient. We had selected 472 members of the KDSA with a dental license and whose office address and contact information were appropriate, and sent them a survey questionnaire by mail asking about the degree of their CPR understanding and if they had experience of handling emergency questions before. Statistical analyses -frequency analysis, chi-square test, ANOVA, and so on- were performed by use of IBM SPSS Statistics 19 for each question. Result: Among 472 people, 181 responded (38.4% response rate). Among the respondents were 134 male and 47 female dentists. Their average age was $40.4{\pm}8.4$. In terms of practice type, there were 123 private practitioners (68.0%), 20 professors (11.0%), 16 dentists-in-service (8.8%), 13 residents (specialist training) (7.2%) and 9 military doctors (5%). There were 125 dentists (69.1%) who were specialists or receiving training to be specialist, most of whom were oral surgeon (57, 31.5%) and pediatric dentists (56, 30.9%). There were 153 people (85.0%) who received CPR training before, and 65 of them (35.9%) were receiving regular training. When asked about the ratio of chest pressure vs mouth-to-mouth respiration when conducting CPR, 107 people (59.1%) answered 30:2. However, only 27.1% of them answered correctly for a question regarding CPR stages, C(Circulation)- A(Airway)- B(Breathing)- D(Defibrillation), which was defined in revised 2010 CPR practice guideline. Dentists who had experience of handling emergency situations in their practice were 119 (65.6%). The kinds of emergency situations they experienced were syncope (68, 37.6%), allergic reactions to local anesthetic (44, 24.3%), hyperventilation (43, 23.8%), seizure (25, 13.8%), hypoglycemia (15, 8.3%), breathing difficulty (14, 7.8%), cardiac arrest (11, 6.1%), airway obstruction (6, 3.3%), intake of foreign material and angina pectoris (4, 2.2%), in order of frequency. Most respondents answered that they handled the situation appropriately under the given emergency situation. In terms of emergency equipment they had blood pressure device (70.2%), pulse oximetry (69.6%), Bag-Valve-Mask (56.9%), emergency medicine (41.4%), intubation kit (29.8%), automated external defibrillator (23.2%), suction kit (19.3%) and 12 people (6.6%) did not have any equipment. In terms of confidence in handling emergency situation, with 1-10 point scale, their response was $4.86{\pm}2.41$ points. The average point of those who received regular training was $5.92{\pm}2.20$, while those who did not was $4.29{\pm}2.29$ points (P<0.001) Conclusion: The result showed they had good knowledge of CPR but the information they had was not up-to-date. Also, they were frequently exposed to the risk of emergency situation during their dental practice but the level of confidence in handling the emergency situation was intermediate. Therefore, regular training of CPR to prepare them for handling emergency situation is deemed necessary.

• Asia pacific journal of information systems
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• v.20 no.2
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• pp.125-155
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• 2010

Ontologies have been adopted in various business and scientific communities as a key component of the Semantic Web. Despite the increasing importance of ontologies, ontology developers still perceive construction tasks as a challenge. A clearly defined and well-structured methodology can reduce the time required to develop an ontology and increase the probability of success of a project. However, no reliable knowledge-engineering methodology for ontology development currently exists; every methodology has been tailored toward the development of a particular ontology. In this study, we developed a Graduation Screen Ontology (GSO). The graduation screen domain was chosen for the several reasons. First, the graduation screen process is a complicated task requiring a complex reasoning process. Second, GSO may be reused for other universities because the graduation screen process is similar for most universities. Finally, GSO can be built within a given period because the size of the selected domain is reasonable. No standard ontology development methodology exists; thus, one of the existing ontology development methodologies had to be chosen. The most important considerations for selecting the ontology development methodology of GSO included whether it can be applied to a new domain; whether it covers a broader set of development tasks; and whether it gives sufficient explanation of each development task. We evaluated various ontology development methodologies based on the evaluation framework proposed by G$\acute{o}$mez-P$\acute{e}$rez et al. We concluded that METHONTOLOGY was the most applicable to the building of GSO for this study. METHONTOLOGY was derived from the experience of developing Chemical Ontology at the Polytechnic University of Madrid by Fern$\acute{a}$ndez-L$\acute{o}$pez et al. and is regarded as the most mature ontology development methodology. METHONTOLOGY describes a very detailed approach for building an ontology under a centralized development environment at the conceptual level. This methodology consists of three broad processes, with each process containing specific sub-processes: management (scheduling, control, and quality assurance); development (specification, conceptualization, formalization, implementation, and maintenance); and support process (knowledge acquisition, evaluation, documentation, configuration management, and integration). An ontology development language and ontology development tool for GSO construction also had to be selected. We adopted OWL-DL as the ontology development language. OWL was selected because of its computational quality of consistency in checking and classification, which is crucial in developing coherent and useful ontological models for very complex domains. In addition, Protege-OWL was chosen for an ontology development tool because it is supported by METHONTOLOGY and is widely used because of its platform-independent characteristics. Based on the GSO development experience of the researchers, some issues relating to the METHONTOLOGY, OWL-DL, and Prot$\acute{e}$g$\acute{e}$-OWL were identified. We focused on presenting drawbacks of METHONTOLOGY and discussing how each weakness could be addressed. First, METHONTOLOGY insists that domain experts who do not have ontology construction experience can easily build ontologies. However, it is still difficult for these domain experts to develop a sophisticated ontology, especially if they have insufficient background knowledge related to the ontology. Second, METHONTOLOGY does not include a development stage called the "feasibility study." This pre-development stage helps developers ensure not only that a planned ontology is necessary and sufficiently valuable to begin an ontology building project, but also to determine whether the project will be successful. Third, METHONTOLOGY excludes an explanation on the use and integration of existing ontologies. If an additional stage for considering reuse is introduced, developers might share benefits of reuse. Fourth, METHONTOLOGY fails to address the importance of collaboration. This methodology needs to explain the allocation of specific tasks to different developer groups, and how to combine these tasks once specific given jobs are completed. Fifth, METHONTOLOGY fails to suggest the methods and techniques applied in the conceptualization stage sufficiently. Introducing methods of concept extraction from multiple informal sources or methods of identifying relations may enhance the quality of ontologies. Sixth, METHONTOLOGY does not provide an evaluation process to confirm whether WebODE perfectly transforms a conceptual ontology into a formal ontology. It also does not guarantee whether the outcomes of the conceptualization stage are completely reflected in the implementation stage. Seventh, METHONTOLOGY needs to add criteria for user evaluation of the actual use of the constructed ontology under user environments. Eighth, although METHONTOLOGY allows continual knowledge acquisition while working on the ontology development process, consistent updates can be difficult for developers. Ninth, METHONTOLOGY demands that developers complete various documents during the conceptualization stage; thus, it can be considered a heavy methodology. Adopting an agile methodology will result in reinforcing active communication among developers and reducing the burden of documentation completion. Finally, this study concludes with contributions and practical implications. No previous research has addressed issues related to METHONTOLOGY from empirical experiences; this study is an initial attempt. In addition, several lessons learned from the development experience are discussed. This study also affords some insights for ontology methodology researchers who want to design a more advanced ontology development methodology.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.25-41
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• 2014

Recently, the number of social media users has increased rapidly because of the prevalence of smart devices. As a result, the amount of real-time data has been increasing exponentially, which, in turn, is generating more interest in using such data to create added value. For instance, several attempts are being made to analyze the relevant search keywords that are frequently used on new portal sites and the words that are regularly mentioned on various social media in order to identify social issues. The technique of "topic analysis" is employed in order to identify topics and themes from a large amount of text documents. As one of the most prevalent applications of topic analysis, the technique of issue tracking investigates changes in the social issues that are identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence. However, this traditional issue tracking approach has two limitations. First, when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. This creates practical limitations in the form of significant time and cost burdens. Therefore, this traditional approach is difficult to apply in most applications that need to perform an analysis on the additional period. Second, the issue is not only generated and terminated constantly, but also one issue can sometimes be distributed into several issues or multiple issues can be integrated into one single issue. In other words, each issue is characterized by a life cycle that consists of the stages of creation, transition (merging and segmentation), and termination. The existing issue tracking methods do not address the connection and effect relationship between these issues. The purpose of this study is to overcome the two limitations of the existing issue tracking method, one being the limitation regarding the analysis method and the other being the limitation involving the lack of consideration of the changeability of the issues. Let us assume that we perform multiple topic analysis for each multiple period. Then it is essential to map issues of different periods in order to trace trend of issues. However, it is not easy to discover connection between issues of different periods because the issues derived for each period mutually contain heterogeneity. In this study, to overcome these limitations without having to analyze the entire period's documents simultaneously, the analysis can be performed independently for each period. In addition, we performed issue mapping to link the identified issues of each period. An integrated approach on each details period was presented, and the issue flow of the entire integrated period was depicted in this study. Thus, as the entire process of the issue life cycle, including the stages of creation, transition (merging and segmentation), and extinction, is identified and examined systematically, the changeability of the issues was analyzed in this study. The proposed methodology is highly efficient in terms of time and cost, as it sufficiently considered the changeability of the issues. Further, the results of this study can be used to adapt the methodology to a practical situation. By applying the proposed methodology to actual Internet news, the potential practical applications of the proposed methodology are analyzed. Consequently, the proposed methodology was able to extend the period of the analysis and it could follow the course of progress of each issue's life cycle. Further, this methodology can facilitate a clearer understanding of complex social phenomena using topic analysis.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.3
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• pp.157-165
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• 2006

The change of external/internal factors of the cell rquires specific biological functions to maintain life. Such functions encourage particular genes to jnteract/regulate each other in multiple ways. Accordingly, we applied a linear decomposition model IFSA, which derives hidden variables, called the 'expression mode' that corresponds to the functions. To interpret gene interaction/regulation, we used a cross-correlation method given an expression mode. Linear decomposition models such as principal component analysis (PCA) and independent component analysis (ICA) were shown to be useful in analyzing high dimensional DNA microarray data, compared to clustering methods. These methods assume that gene expression is controlled by a linear combination of uncorrelated/indepdendent latent variables. However these methods have some difficulty in grouping similar patterns which are slightly time-delayed or asymmetric since only exactly matched Patterns are considered. In order to overcome this, we employ the (IFSA) method of [1] to locate phase- and shut-invariant features. Membership scoring functions play an important role to classify genes since linear decomposition models basically aim at data reduction not but at grouping data. We address a new function essential to the IFSA method. In this paper we stress that IFSA is useful in grouping functionally-related genes in the presence of time-shift and expression phase variance. Ultimately, we propose a new approach to investigate the multiple interaction information of genes.

• Journal of Korean Academy of Nursing Administration
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• v.4 no.1
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• pp.247-275
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• 1998

The purpose of this study was to offer the results of content analysis and qualitative study that explored the perceptions about quality nursing care of patients and families as consumers and to identify the implications of this study for quality nursing care management and research. The data was collected from 12 adult patients and 9 families who were admmitted at medical and surgical nursing unit of one university hospital in Seoul from October, 1996 to January, 1997. Research participants were asked to response "what do you think quality nursing care?" and similar questions during the interviews was performed. Data were analyzed using open coding and content analysis with frequencies and percents of attributes of quality nursing care. Attributes of quality nursing care and meaning of quality nursing care that patients and families perceived were explored. 1. The attributes of quality nursing care that patient and families perceived were categorized into 56 attributes. The highest response rate among the attributes was 'one's heart at ease' (76.2%), and the next high response rates were ranked in order 'consideration' , 'care about' (each 61.9% 'expert skill' (57.1%), 'deal with problem promptly' , 'information offer' (42.9%), 'intimate feeling' (38.1%), 'smile' 'service spirit' , 'do one's best' (each 33.3%), 'frequent visit' (23.8%), 'observe the time' (23.8%), 'direct nursing care' , 'speaking warmly' , give a hope' , 'address kindly' , 'a sense of duty' , 'good facilities' (each 19.0%), 'inquire after a patient health' , 'patient-centered nursing care' , 'showing an example' , 'professional knowledge' , 'careless moraly patient' , 'give encourage to patients' , 'good answer a question' (each 14.3%), 'do not imprudently' , 'do not disregard' , 'broad knowledge' , 'emergency treatment skill' , 'dependability' ,'consolation' giving a sense of security' , 'a self sacrificing spirit' , 'a sense of responsibility' 'hard - working', 'enough disposition of nursing staff (each 9.5%), 'improve patient's pride' and the rest attributes exhibited 4.7%, respectively. 2. The attributes that were identified in patients' data only were 8 categories, 'service sprit' (58.3 %) 'expert knowledge' , 'good answer a question' (each 25.0%), 'hard working' (16.7%), 'a warm character', 'professional attainments', 'do without reserve', 'satisfaction' (each 8.3%), 3. The attributes were identified to families' data only were 31 categories, 'speaking warmly' , 'direct nursing care', 'adress kindly', 'patientcentered nursing care', 'showing an example' (each 33.3%). 'do not imprudently' , 'do not disregard' , 'consolation', 'giving a sense of security', 'broad knowledge' , 'emergency treatment skill', 'dependability' ,'a self - sacrificing spirit', 'a sense of responsibility' (each 22.2%), 'improve patient's pride' , 'without discrimination' , 'show kindness' , 'individual nursing care', 'being with patient' , 'helping' , 'accuracy' , 'without any mistake' , 'love' , 'self - confidence', 'self possession', 'a self - denying spirit' , 'a sense of duty' , 'tighten discipline' , 'disposed room with similar patient to diagnosis', 'compensatory relationship between me dical team' , 'role of connection' (each 11.1 %). 4. The attributes of quality nursing care were integrated into 11 categories that they were 'patientcentered nursing care' (25.1%), 'expertise' (22.1%), 'caring'(18.1%), 'kindness'(11.1%L 'nurse attainments(10.1%), 'sincerity' (7.5%), 'good environment' (2.0%), 'effective organizational management', 'coordination', 'enough nursing staff' ( each 1.0%), 'satisfaction' (0.5%) were showed in the order of the highest rate. 5. The concept of quality nursing care were defined as 'give a satisfaction to patients by patientcentered care based on professional skill and caring with kindness and sincerity'. The description of the meaning of quality nursing care provided by this research participants, patients and families can provide important information for quality nursing care management, medical marketing, education and researches of this field. On the basis of the above findings the following recommendations are made: to suggest to utilize this results for patient care in practice setting, development of quality assessment tool in nursing care, repeat study by the same subjects and method, and to a comparative study by the same method to nurse.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.97-121
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• 2013

With the growing importance of knowledge for sustainable competitive advantages and innovation in a volatile environment, many researches on knowledge sharing have been conducted. However, previous researches have mostly relied on the questionnaire survey which has inherent perceptive errors of respondents. The current research has drawn the relationship among primary participant behaviors towards the participation level in knowledge sharing, basically from online user behaviors on Wikipedia, a representative community for online knowledge collaboration. Without users' participation in knowledge sharing, knowledge collaboration for creating knowledge cannot be successful. By the way, the editing patterns of Wikipedia users are diverse, resulting in different revisiting periods for the same number of edits, and thus varying results of shared knowledge. Therefore, we illuminated the participation level of knowledge sharing from two different angles of number of edits and revisiting period. The behavioral dimensions affecting the level of participation in knowledge sharing includes the article talk for public discussion and user talk for private messaging, and community registration, which are observable on Wiki platform. Public discussion is being progressed on article talk pages arranged for exchanging ideas about each article topic. An article talk page is often divided into several sections which mainly address specific type of issues raised during the article development procedure. From the diverse opinions about the relatively trivial things such as what text, link, or images should be added or removed and how they should be restructured to the profound professional insights are shared, negotiated, and improved over the course of discussion. Wikipedia also provides personal user talk pages as a private messaging tool. On these pages, diverse personal messages such as casual greetings, stories about activities on Wikipedia, and ordinary affairs of life are exchanged. If anyone wants to communicate with another person, he or she visits the person's user talk page and leaves a message. Wikipedia articles are assessed according to seven quality grades, of which the featured article level is the highest. The dataset includes participants' behavioral data related with 2,978 articles, which have reached the featured article level, with editing histories of articles, their article talk histories, and user talk histories extracted from user talk pages for each article. The time period for analysis is from the initiation of articles until their promotion to the featured article level. The number of edits represents the total number of participation in the editing of an article, and the revisiting period is the time difference between the first and last edits. At first, the participation levels of each user category classified according to behavioral dimensions have been analyzed and compared. And then, robust regressions have been conducted on the relationships among independent variables reflecting the degree of behavioral characteristics and the dependent variable representing the participation level. Especially, through adopting a motivational theory adequate for online environment in setting up research hypotheses, this work suggests a theoretical framework for the participation level of online knowledge sharing. Consequently, this work reached the following practical behavioral results besides some theoretical implications. First, both public discussion and private messaging positively affect the participation level in knowledge sharing. Second, public discussion exerts greater influence than private messaging on the participation level. Third, a synergy effect of public discussion and private messaging on the number of edits was found, whereas a pretty weak negative interaction effect of them on the revisiting period was observed. Fourth, community registration has a significant impact on the revisiting period, whereas being insignificant on the number of edits. Fifth, when it comes to the relation generated from private messaging, the frequency or depth of relation is shown to be more critical than the scope of relation for the participation level.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.