• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.56-57
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• 2014

As school facilities have been expanding quantity better than quality, efficient school facility management has been focused on from 2001 in domestic. Due to obsolescence of school facility, objective management and maintenance cost of school facility is very important. However LCC(Life cycle cost) analyst, owner, engineer, contractor and facility manager have a difficulty to obtain and facilitate the basic analyzed data required to analyze LCC of school facility and establish maintenance plan. Therefore this research presents muti-dimensional analysis method through data warehouse technique for supplying maintenance cost information of school facility that can trace and accumulate the scattered LCCing data in the perspective of life cycle of school facility.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1718-1721
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• 2020

This paper proposes a method to protect the copyright of creators' content shared through an online sharing platform in a legal battle. When creators upload their independent creation content to the online sharing platform, it automatically upload information necessary for copyright effect to this system. The data warehouse of this system is as a private blockchain for to ensure non-repudiation and transparency of the information. We present that the data warehouse is to build as Hyperledger Fabric in this paper. And We present the transaction data structure of the blockchain to prevent orphan works. We also dealt with how to build a website where users can conveniently check the data (copyright related information) of this blockchain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.528-550
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• 2024

Warehousing demand prediction is an essential part of the supply chain, providing a fundamental basis for product manufacturing, replenishment, warehouse planning, etc. Existing forecasting methods cannot produce accurate forecasts since warehouse demand is affected by external factors such as holidays and seasons. Some aspects, such as consumer psychology and producer reputation, are challenging to quantify. The data can fluctuate widely or do not show obvious trend cycles. We introduce a new model for warehouse demand prediction called MAGRU, which stands for Multi-layer Attention with GRU. In the model, firstly, we perform the embedding operation on the input sequence to quantify the external influences; after that, we implement an encoder using GRU and the attention mechanism. The hidden state of GRU captures essential time series. In the decoder, we use attention again to select the key hidden states among all-time slices as the data to be fed into the GRU network. Experimental results show that this model has higher accuracy than RNN, LSTM, GRU, Prophet, XGboost, and DARNN. Using mean absolute error (MAE) and symmetric mean absolute percentage error(SMAPE) to evaluate the experimental results, MAGRU's MAE, RMSE, and SMAPE decreased by 7.65%, 10.03%, and 8.87% over GRU-LSTM, the current best model for solving this type of problem.

• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.242-248
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• 2003

◎ Uncover the hidden pattern from massive data(data warehouse) -Builds a reasonable model to predict the future for business advantage -Decision Making based on the learned models(omitted)

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.698-701
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• 2004

In many literatures, data mining has been used as a utilization of data warehouse and data collection. The biggest utilizations of data mining are for marketing and researches. This is solely because of the data available for this field is usually in large amount. The usability of the data mining is expandable also to the production process. While the object of research of the data mining in marketing is the customers and products, data mining in the production field is object to the so called 4MlE, man, machine, materials, method (recipe) and environment. All of the elements are important to the production process which determines the quality of the product. Because the final aim of the data mining in production field is the quality of the production, this data mining is commonly recognized as quality data mining. As the variables researched in quality data mining can be hundreds or more, it could take a long time to reveal the information from the data warehouse. Feature selection methodology is proposed to help the research take the best performance in a relatively short time. The usage of available simple statistical tools in this method can help the speed of the mining.

• Korea Information Processing Society Review
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• v.19 no.2
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• pp.54-61
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• 2012

• 한국공간정보시스템학회:학술대회논문집
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• 2000.06a
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• pp.231-238
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• 2000

• 소프트웨어공모대전
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• s.5
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• pp.28-36
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• 2000

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.427-438
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• 2003

In a data warehouse, users typically search for trends, patterns, or unusual data behaviors by issuing queries interactively. The OLAP range-sum query is widely used in finding trends and in discovering relationships among attributes in the data warehouse. In a recent environment of enterprises, data elements in a data cube are frequently changed. The problem is that the cost of updating a prefix sum cube is very high. In this paper, we propose a novel algorithm which reduces the update cost significantly by an index structure called the Δ-tree. Also, we propose a hybrid method to provide either approximate or precise results to reduce the overall cost of queries. It is highly beneficial for various applications that need quick approximate answers rather than time consuming accurate ones, such as decision support systems. An extensive experiment shows that our method performs very efficiently on diverse dimensionalities, compared to other methods.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.585-595
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• 2004

A query processing is a critical issue in data warehouse environment since queries on data warehouses often involve hundreds of complex operations over large volumes of data. Data warehouses therefore build a large number of materialized views to increase the system performance. Which views to materialized is an important factor on the view maintenance cost as well as the query performance. The goal of materialized view selection problem is to select an optimal set of views that minimizes total query response time in addition to the view maintenance cost. In this paper, we present an efficient solution for the materialized view selection problem. Although the optimal selection of materialized views is NP-hard problem, we developed a feasible solution by utilizing the characteristics of relational operators such as join, selection, and grouping.