• KIPS Transactions on Computer and Communication Systems
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• v.2 no.11
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• pp.461-470
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• 2013

This paper proposes high speed kernel data collection method for analysis of memory workload, using technique of direct access to process's memory management structure. The conventional analysis tools have a slower data collection speed and they are lack of scalability due to collection only formalized memory information. The proposed method collects kernel data much faster than the conventional methods using technique of direct collect to process's memory information, page table, page structure in the memory management structure, and it can collect data which user wanted. We collect memory management data of the running process, and analyze its memory workload.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.21-31
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• 2020

Internet of Things is growing rapidly. As it evolves, the amount of data is increasing significantly. It requires a new personalized information retrieval method. Internet of Things is defined as uniquely identifiable interoperable connected object. The first definition of Internet of Things was from Things oriented perspective. However, previous studies about personalized information retrieval method do not consider Things. To meet user's individual needs, previous studies concentrate on only human, not Things. In this paper, we propose a personalized information retrieval method considering participating device in Internet of Things. It provides personalized information using data type preference for each device. Moreover, it provides personalized results by integrating data type preference for set of devices. This paper describes a new personalized retrieval method and algorithm. It consists of five steps. Then, it presents four scenarios using proposed method. The scenarios show our work is more effective and efficient than existing one.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.51-69
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• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.361-370
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• 2020

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.131-136
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• 2017

PLM(Product Lifecycle Management) is an information management system that can integrate data, processes, business systems and human resources throughout the enterprise. BOM(Bills Of Material) is key data for designing, purchasing materials, manufacturing planning and management, which is basic for product development throughout the product life cycle. In this paper, we propose the efficient system to increase the data loading speed and the processing speed when using such large BOM data. We present the performance and usability of IMDG (In Memory Data Grid) for data processing when loading large amounts of data. In the UI, using the pure web grid of JavaScript instead of the existing data loading method can be improve performance of data managing.

• The Journal of Bigdata
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• v.3 no.2
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• pp.1-10
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• 2018

In the field of smart water management, there is an increasing demand for strengthening competitiveness through big data analysis. As a result, systematic management (Governance) of big data is becoming an important issue. Big data governance is a systematic approach to evaluating, directing and monitoring data management, such as data quality assurance, privacy protection, data lifetime management, data ownership and clarification of management rights. Failure to establish big data governance can lead to serious problems by using low quality data for critical decisions. In addition, personal privacy data can make Big Brother worry come true, and IT costs can skyrocket due to the neglect of data age management. Even if these technical problems are fixed, the big data effects will not be sustained unless there are organizations and personnel who are dedicated and responsible for data-related issues. In this paper, we propose a method of building data governance for smart water data management based on big data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.20-21
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• 2014

Recently, activation of related construction projects due to the large traditional gas resource development of frozen soil region of Russia are expected. It is necessary to provide a plan that can be utilized and collectively managed the occurrence data in the engineering & construction stage for continued contracts of the pipe construction. Therefore, this research is aimed to provide a management plan of occurrence data for efficient management in engineering & construction stage of pipeline business in frozen soil region. The data of the engineering & construction(related pipe construction projects) can be accumulated each version and multiply managed. Furthermore, I will be expected to be the foundation of the systematic management of the classifying based on metadata and the optimizing operations using big data method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.446-450
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• 2006

Recently, the advances on sensor technology increases the study on data processing oriented middleware. Usually, most of middleware uses the naive and delta method for queue management to process data effectively. But such a queue management method it is difficult to support guaranteeing of the requested quality of services because it simply discards the data in a queue when the overflow is occurred. To handle this problem, some methods for minimizing data volumes in a wireless sensor network have been studied, but most of them cause another problem that needs the huge processing time additionally with higher complexity of the proposed algorithm. In this paper, we propose a new method of delta-average queue management by applying the mean value to the exiting delta queue management method using the data differences to handle the problem of queue overflow. The proposed method has lower complexity than the others and increases the QoS of a WSN application by using mean value instead of using data discarding policy. In addition, it is designed to manage a queue effectively by controlling the range of data differences adaptively to the target sensor network environment.

• Korea Journal of Hospital Management
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• v.2 no.1
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• pp.22-47
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• 1997

This paper evaluated the relative efficiency of 33 provincial medical centers using Data Envelopment Analysis(DEA) and compared the DEA efficiency results with those of the current method conducted by the management evaluation team. DEA Was selected as an alternative efficiency evaluation method since it could handle multiple inputs and multiple outputs simultaneously and identify the sources of inefficiency. To analyze the sensitivity of productivity values to the variable sets, four different sets of input and output variables were identified. Results showed that most of the medical centers are operating far away from the efficiency frontier supporting the previous results. Some centers showed 100% efficiency regardless of the selected variable sets. DEA results are compared with current management evaluation results. Some inconsistencies were found for some DMUs between the results of two methods showing the existence of methodology bias. DEA results and ratio analyses results mostly agree for 1992 data.