• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.492-500
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• 2014

Activity monitoring has been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior. In this research, we propose a data-driven activity monitoring method to measure relative sales performance which is not sensitive to special event which frequently occur in marketing area. Moreover, the proposed method can automatically updates the monitoring threshold that accommodates a drastically changing business environment. The results from simulation and practical case study from sales of electronic devices demonstrate the usefulness and applicability of the proposed activity monitoring method.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.55-70
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• 2011

In this paper, we propose a policy-driven RFID data management event definition language, which is possibly applicable as a partial standard for SSI (Software System Infrastructure) Part 4 (Application Interface, 24791-4) defined by ISO/IEC JTC 1/SC 31/WG 4 (RFID for Item Management). The SSI's RFID application interface part is originally defined for providing a unified interface of the RFID middleware functionality―data management, device management, device interface and security functions. However, the current specifications are too circumstantial to be understood by the application developers who used to lack the professional and technological backgrounds of the RFID middleware functionality. As an impeccable solution, we use the concept of event-constraint policy that is not only representing semantic contents of RFID domains but also providing transparencies with higher level abstractions to RFID applications, and that is able to provide a means of specifying event-constraints for filtering a huge number of raw data caught from the associated RF readers. Conclusively, we try to embody the proposed concept by newly defining an XML-based RFID event policy definition language, which is abbreviated to rXPDL. Additionally, we expect that the specification of rXPDL proposed in the paper becomes a technological basis for the domestic as well as the international standards that are able to be extensively applied to RFID and ubiquitous sensor networks.

• Journal of the Korea Safety Management & Science
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• v.25 no.3
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• pp.63-71
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• 2023

The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

• Asian Journal of Innovation and Policy
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• v.2 no.2
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• pp.240-264
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• 2013

The study compares network structures that emerged in three inter-organisational projects set up under the MSC Malaysia initiative by the Government of Malaysia. These consortia are seen as policy-driven inter-organisational networks and, with data collected through interviews; the links among the organisations are mapped to gain an understanding of the structures that emerged in these networks. The findings provide lessons for other emerging countries that are embarking on similar projects i.e. cluster-oriented developments with policy-driven inter-organisational networks. These findings are seen as particularly useful when emerging countries invest in technology-related projects and invite multinational companies to work together with local firms.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.479-487
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• 2016

In a Web 2.0 context, users are exposed to numerous smart devices and services that allow real-time interaction between users (or consumers) and developers (or producers). For the provisioning of new user-created services based on user's context, the data management of service creation experiences becomes a non-trivial task. This article introduces a data model for service creation and then proposes a service creation management architecture which enables new service creation using the data model, the management of the service creation data, and the semantic service discovery across internal/external service repositories. The article also explains the use of the proposed architecture with two different scenarios: home and mobile environments. The proposed architecture for service creation data management offers consistent and seamless handling of the service creation data throughout its usage lifecycle.

• Knowledge Management Research
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• v.25 no.2
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• pp.19-47
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• 2024

Organizational ability to analyze and utilize data plays an important role in knowledge management and decision-making. This study aims to investigate the potential application of large language models in corporate data analysis. Focusing on the field of human resources, the research examines the data analysis capabilities of these models. Using the widely studied IBM HR dataset, the study reproduces machine learning-based employee turnover prediction analyses from previous research through ChatGPT and compares its predictive performance. Unlike past research methods that required advanced programming skills, ChatGPT-based machine learning data analysis, conducted through the analyst's natural language requests, offers the advantages of being much easier and faster. Moreover, its prediction accuracy was found to be competitive compared to previous studies. This suggests that large language models could serve as effective and practical alternatives in the field of corporate data analysis, which has traditionally demanded advanced programming capabilities. Furthermore, this approach is expected to contribute to the popularization of data analysis and the spread of data-driven decision-making (DDDM). The prompts used during the data analysis process and the program code generated by ChatGPT are also included in the appendix for verification, providing a foundation for future data analysis research using large language models.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Journal of Information Technology Services
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• v.8 no.1
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• pp.1-9
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• 2009

It is essential for companies to manage massive data for dealing with large volume of transactions and customers' needs. To this end, the companies have operated data warehouse with many complex tools for data gathering and reporting to the end-users. However, the data from the heterogeneous tools at the various sources cannot be exchanged because of the different interfaces. Therefore, the data cannot be controlled with integrated manner, and furthermore the companies do not focus the quality of data resulting in the data quality problem. Thus, this study suggests how to manage massive data with a metadata. In particular, we investigate current status of metadata management, its appliance, and perspectives. The contribution of this research is to apply the metadata management system to the real world and to suggest its management procedure.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.157-164
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• 2003

The amount of information increases rapidly when working in a distributed environment where multiple collaborative partners work together on a complex product. Today's PDM (product data management) systems provide good capabilities regarding the management of product data within a single company. However, taking into account the variety of systems used at partner sites in an engineering environment one can easily imagine problems regarding the interoperability and the data consistency. This paper presents a concept to improve the workflow management using the parameters network. It shows a parameter driven engineering workflow that is able to manage engineering task across company boarders. We introduce a mechanism of workflow management based on the engineering parameters and an architecture of the distributed workspace to apply it within a PDM system. For a parameter mapping between CAD and PDM system we developed an XML-based CATIA data interface module using CAA.