• Knowledge Management Research
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• v.13 no.5
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• pp.43-64
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• 2012

Emerging technologies have significant implications in establishing competitive advantages and are characterized by continuous rapid development. Efficient benchmarking is more and more important in the development of emerging technologies. Similar input level and importance are two necessary criteria need to be considered for emerging technology's benchmarking. In this study, we proposed a sequential use of self-organizing map(SOM), data envelopment analysis(DEA) and analytical hierarchy process(AHP) method for the stepwise benchmarking of emerging technology. The proposed method uses two-level SOM to cluster the emerging technologies with similar required input levels together, then, in each cluster, uses DEA-BCC model to evaluate the efficiencies of the emerging technologies and do tier analysis to form tiers. On each tier, AHP rating method is used to calculate each emerging technology's importance priority. The optimal benchmarking path of each cluster is established by connecting the emerging technologies with the highest importance priority. In order to validate the proposed method, we apply it to a case of biotechnology. The result shows the proposed method can overcome difficulties in benchmarking, select suitable benchmarking targets and make the benchmarking process more efficient and reasonable.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.1
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• pp.32-41
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• 2010

DEA(Data Envelopment Analysis) is the relative efficiency measure among homogeneous DMU(Decision- Making Units) which can be used to useful tool to improve performance through efficiency evaluation and benchmarking. However, the general case of DEA was considered as unrealistic since it consists a benchmarking regardless of DMU characteristic by input and output elements and the high efficiency gap in benchmarking for inefficient DMU. To solve this problem, stratification method for benchmarking was suggested, but simply presented benchmarking path in repeatedly applying level. In this paper, we suggest a new method that inefficient DMU can choice the optimal path to benchmark the most efficient DMU base on the similarity among the input elements. For this, we propose a route choice method that combined a stratification benchmarking algorithm and SOM (Self-Organizing Map). An implementation on real environment is also presented.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.84-94
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• 2021

Graph databases have been developed to efficiently store and query graph data composed of vertices and edges to express relationships between objects. Since the query types of graph database show very different characteristics from traditional NoSQL databases, benchmarking tools suitable for graph databases to verify the performance of the graph database are needed. In this paper, we propose an efficient graph database benchmarking system that supports diversity in graph inputs and queries. The proposed system utilizes OrientDB to conduct benchmarking for graph databases. In order to support the diversity of input graphs and query graphs, we use LDBC that is an existing graph data generation tool. We demonstrate the feasibility and effectiveness of the proposed scheme through analysis of benchmarking results. As a result of performance evaluation, it has been shown that the proposed system can generate customizable synthetic graph data, and benchmarking can be performed based on the generated graph data.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• Educational Technology International
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• v.12 no.1
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• pp.21-46
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• 2011

International virtual teamwork offers new opportunities for the professional development of teachers. In this paper, we examine the initial experiences in an ongoing international virtual benchmarking project coordinated by the Finnish Online University of Applied Sciences. What challenges does an international context present for project construction and collaboration? Data from five countries, in the form of participant reflections and researchers' observations, were analysed according to four types of barriers: language, time, technical and mental barriers. Initial data indicates that trust is an essential starting point, as there is neither time nor possibilities to build mutual trust by traditional means. Organisational confidentiality issues, however, can complicate the situation. The project introduces 'collision' as a method of professional development, in which physical and organisational borders are crossed and the skills and competencies needed in global learning environments are acquired.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.937-942
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• 2008

In this paper, new methodology is proposed to estimate the cooling load using design parameters of building and predicted weather data. Only two parameters such as maximum and minimum temperature are necessary to obtain hourly distribution of cooling load for the next day. The maximum and minimum temperature that are used for input parameters can be obtained from forecasted weather data. Benchmarking building(research building) is selected to validate the performance of the proposed method, and the estimated cooling loads in hourly bases are calculated and compared with the measured data for benchmarking building. The estimated results show fairly good agreement with the measured data for benchmarking building.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.6
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• pp.628-641
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• 2014

Stepwise efficiency improvement in data envelopment analysis (DEA)-based benchmarking is a realistic and effective method by which inefficient decision making units (DMUs) can choose benchmarks in a stepwise manner and, thereby, effect gradual performance improvement. Most of the previous research relevant to stepwise efficiency improvement has focused primarily on how to stratify DMUs into multiple layers and how to select immediate benchmark targets in leading levels for lagging-level DMUs. It can be said that the sequence of benchmark targets was constructed in a myopic way, which can limit its effectiveness. To address this issue, this paper proposes an optimization approach to the construction of a sequence of benchmarks in DEA-based benchmarking, wherein two optimization criteria are employed : similarity of input-output use patterns, and proximity of input-output use levels between DMUs. To illustrate the proposed method, we applied it to the benchmarking of 23 national universities in South Korea.

• Journal of Korean Society for Quality Management
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• v.37 no.1
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• pp.1-9
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• 2009

Data envelopment analysis(DEA) has proved to be powerful for benchmarking and has been widely used in a variety of settings since the advent of it. DEA can be used in identifying the best performing units to be benchmarked against as well as in providing actionable measure for improvement of a organization's performance. However, the selection of performance benchmarks is a matter of both technical production possibilities and organizational policy considerations, managerial preferences and external restrictions. In that regards, DEA has a limited value in benchmarking because it focuses on only technical production Possibilities. This research proposes a new perspective in using DEA and a frame-work for benchmarking to select benchmarks that are both feasible and desirable in terms of organizational context. To do this, the concept of local and global efficiency is newly proposed. To show how useful the suggested concept and framework are, a case study is addressed.

• International Journal of High-Rise Buildings
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• v.2 no.4
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• pp.285-291
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• 2013

This paper proposes a framework for understanding the energy consumption differences between tall and low-rise buildings. Energy usage data from 706 office buildings in New York illustrates expected correlations from the framework. Notable correlations include: taller buildings tend to use more energy until a plateau at 30~39 floors; tall buildings in Manhattan use 20% more energy than low-rise buildings in Manhattan, while tall buildings outside Manhattan use 4% more energy than low-rise buildings outside Manhattan. Additional correlations are discussed, among which is the trend that the Energy Star program in New York City assigns higher ratings to tall buildings with higher EUIs than low-rise buildings with the same EUI. Since Energy Star is based on regressions of existing buildings, the Energy Star ratings suggest taller buildings have higher EUIs than shorter buildings, which is confirmed by the New York City energy benchmarking data.