• Journal of KIISE:Databases
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• v.27 no.2
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• pp.175-184
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• 2000

A data warehouse is an integrated and summarized collection of data that can efficiently support decision making process. The summarized data at the data warehouse is often stored in materialized views. These materialized views need to be updated when source data change. Since the propagation of updates to the views may impose a significant overhead, it is very important to update the warehouse views efficiently. Though various strategies have been proposed to maintain views in the past, they typically require too much accesses to the data sources when the changes of multiple data sources have to be reflected in the view. In this paper we propose an efficient view update strategy that uses relatively small number of accesses to the data sources. We also show the performance advantage of our method over other existing methods through experiments using TPC-D data and queries.

• 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 Information Technology Applications and Management
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• v.18 no.1
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• pp.125-141
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• 2011

As the sales volume of online shops increases, the job burden in the back-offices of the online shops also increases. Order picking is the most labor-intensive operation among the jobs in a back-office and mid-size pure click online shops are experiencing the time delay and complexity in order picking nowadays while fulfilling their customers' orders. Those warehouses of the mid-size shops are based on manual systems, and as order pickings are repeated, the warehouses get a mess and lots of products in those warehouses are getting missing, which results in severe delay in order picking. To overcome this kind of problem in online clothing shops, we research a methodology to locate warehousing products. When products arrive at a warehouse, they are packed into a box and located on a rack in the warehouse. At this point, the operator should determine the box to be put in and the location on the rack for the box to be put on. This problem could be formulated as an Integer Programming model, but the branch-and bound algorithm to solve the IP model requires enormous computation, and sometimes it is even impossible to get a solution in a proper time. So, we relaxed the problem, developed a set of heuristics as a methodology to get a semi-optimum in an acceptable time, and proved by an experiment that the solutions by our methodology are satisfactory and acceptable by field managers.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.20-27
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• 1999

This study presents an Automated Warehouse Management System (AWMS) using a bar-code system The AWMS has been designed to be associated with an Integrated Production Management System (IPMS), which basically includes the following four modules i.e the sales management, production management, material management, and data management. Now, whenever storage or retrieval of the material occurs in the warehouse, the events could be processed quickly and accurately only through reading the 13 digit bar-code including 5 digit position code and simply typing in the number of material. Consequently, the AWMS associated with IPMS could automatically coincide the item counts on document with those in the warehouse. Also, this makes it possible to identify the material quantities in real time.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.201-206
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• 2010

According to the ubiquitous technology development, managing of the warehouse system which stores and manages agriculture products is realized effectively. This paper proposes an ontology-based context aware system model for the purpose of storing and managing agricultural products using ubiquitous sensors to share and distribute information. In these days, owing to penetrating ubiquitous technologies into our way of life, the importance of information is increasing gradually. The importance of ontology in a domain is getting as well. Therefore, this paper focuses on describing to get data from ubiquitous devices such as sensors, and designs and build an ontology-based agricultural product warehouse model.

• Journal of the Korean Society of Tobacco Science
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• v.25 no.1
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• pp.53-58
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• 2003

This study deals with the leaf tobacco assignment problem of a leaf tobacco warehouse with multiple input points and single output point. Also, the number of storage frequences is not necessary the same as that of retrieval for each leaf tobacco. A mathematical model is developed with the objective of minimizing the total travel distance associated with storage and retrieval operations. We also develop several heuristics based on the retrieval order frequency, retrieval/storage frequency ratio of leaf tobacco, and ABC curve. It is observed that the ABC curve based heuristic gives the best solution which is near optimal. Based on the test results from real world data, the ABC curve based heuristic is found to give a best performance. Comparing to current assignment method, the ABC curve based heuristic reduced total travel distance about 17.2%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.139-144
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• 1997

The objective of this study is to establish an actual optimization strategy for the traditional multi-stage distribution system which consists of factory warehouse, central distribution warehouse, and regional distribution warehouse under the basic assumption of distribution system. A minimization algorithm of total operating cost in a multi-stage distribution system was developed by expanding the previously existing algorithm through consideration of additional transportation environment. Alternative non-linear transportation costs for the same travel distance can be applied for the multi-stage distribution system by estimating the corresponding characteristic values through the collection of the actual data representing the change of transportation circumstances.

• International Area Studies Review
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• v.20 no.3
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• pp.23-44
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• 2016

This study attempts to scrutinize the effectiveness of the international economic sanction on North Korea by analyzing the determinants of the North Korea-China trade with Chinese Customs House data classified under general, border, processing, bonded warehouse. The result shows that after international economic sanctions, North Korea-China trade was increased across types of trade. In particular, sanction-sensitive items were increased after sanctions and China also weakened the effectiveness of sanctions. To put it concretely, North Korea's food and fuel imports from china increased in processing and border trade. In the case of luxury good, it was increased in general trade. Strategy goods (weaponry) increased only in bonded warehouse trade. For China's import from North Korea, food and fuel were increased in total, border and bonded warehouse trade. Considering the features of each type of trade, these increase are presumed to have been playing a decisive role in weakening the effect of international sanction on North Korea.

• Advances in nano research
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• v.12 no.1
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• pp.65-72
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• 2022

The dynamic properties (frequencies, mode shapes, damping ratios) of the scaled WPC warehouse are compared using the operational modal analysis approach to the dynamic parameters (frequencies, mode shapes, damping ratios) of the full outer surface of titanium dioxide, 70 micron in thickness. Micro tremor ambient vibration data on ground level was used to provide ambient excitation. For the output-only modal identification, Enhanced Frequency Domain Decomposition (EFDD) was used. This study discovered a strong correlation between mode shapes. Titanium dioxide applied to the entire outer surface of the scaled WPC warehouse results in an average 14.05 percent difference in frequency values and 7.61 percent difference in damping ratios, demonstrating that nanomaterials can be used to increase rigidity in structures, or for reinforcement. Another significant finding in the study was the highest level of adherence of titanium dioxide and similar nanomaterials mentioned in the introduction to WPC structure surfaces.

• Asia pacific journal of information systems
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• v.14 no.3
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• pp.169-187
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• 2004

Self-maintainability of data warehouse(DW) views is an ability to maintain the DW views without requiring an access to (i) any underlying databases or (ii) any information beyond the DW views and the delta of the databases. With our proposed method, DW views can be updated by using only the old views and the differential files such as different files, referential integrity differential files, linked differential files, and backward-linked differential files that keep the truly relevant tuples in the delta. This method avoids accessing the underlying databases in that the method achieves self-maintainability even in preparing auxiliary information. We showed that out method can be applicable to the DW views that contain joins over relations in a star schema, a snowflake schema, or a galaxy schema.