• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1386-1396
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• 2009

Most decision support functions of spatial data warehouse rely on the OLAP operations upon a spatial cube. Meanwhile, higher performance is always guaranteed by indexing the cube, which stores huge amount of pre-aggregated information. Hierarchical Dwarf was proposed as a solution, which can be taken as an extension of the Dwarf, a compressed index for cube structures. However, it does not consider the spatial dimension and even aggregates incorrectly if there are redundant values at the lower levels. OLAP-favored Searching was proposed as a spatial hierarchy based OLAP operation, which employs the advantages of R-tree. Although it supports aggregating functions well against specified areas, it ignores the operations on the spatial dimensions. In this paper, an indexing approach, which aims at utilizing the concept hierarchy of the spatial cube for decision support, is proposed. The index consists of concept hierarchy trees of all dimensions, which are linked according to the tuples stored in the fact table. It saves storage cost by preventing identical trees from being created redundantly. Also, it reduces the OLAP operation cost by integrating the spatial and aspatial dimensions in the virtual concept hierarchy.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.2
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• pp.55-73
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• 2022

This study investigated the cases of the domestic classification system and compared and analyzed them with the academic standard classification system to derive future improvement directions. The direction of future improvement of the academic standard classification system presented based on this is as follows. First, it seems necessary to clearly guarantee the operation of the classification system as a law for the continuous development of the academic standard classification system. Second, it is necessary to improve it to a comprehensive classification principle that satisfies both current issues and global universality so that domestic and foreign data can be collected and compared smoothly by producing a wide-ranging classification system. Third, it is necessary to select a clear revision cycle of the academic standard classification system, and it seems appropriate to proceed with the revision every five years in order to reflect the academic field across a vast field. Currently, research on such a domestic classification system is insufficient, and such investigations are continuously conducted in the future, requiring continuous interest and research on the domestic classification system.

• Journal of Environmental Policy
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• v.5 no.1
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• pp.45-70
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• 2006

China has achieved great economic growth above 9% annual since it changed to more of a market economy system by its reform and open-door policy. At the same time, China has experienced severe ecological deterioration, such as air and water pollutions caused by its rapid urbanization and industrialization. China is now confronted with environmental pollution and ecological deterioration at a critical point, at which economic development in China is limited. Moreover, environmental problems in China have become a lit fuse for social fluctuation beyond pollution problems. The root and background of environmental problems in China, firstly, are its government's lack of understanding of these problems and incorrect economic policies affected by political and ideological prejudice. Secondly, the plundering of resources, 'the principle of development first' which didn't consider environmental sustainability is another source of environmental deterioration in China. In addition, a huge population and poverty in China have increased the difficulty in solving its environmental problems, and in fact have accelerated them. The Chinese government has established many environmental laws and institutions, increased environmental investments, and is enlarging the participation of NGOs and the general public in some limited scale to solve its environmental problems. However, it has not obtained effective results because of the lack of environmental investments owing to the government's limit of the development phase, a structural limit of law enforcement and local protectionism, and the limit of political independency in NGOs and the lack of public participation in China. It seems that China remains in the stage of 'economic development first, environmental protection second', contrary to its catch-phrase of 'the harmony between economic development and environmental protection'. China is now confronted with dual pressure both domestically and abroad because of deepening environmental problems. There are growing public's protests and demonstrations in China in response to the spread of damage owing to environmental pollution and ecological deterioration. On the other hand, international society, in particular neighboring countries, regard China as a principal cause of ecological disaster. In the face of this dual pressure, China is presently contemplating a 'recycling economy' that helps sustainable development through the structural reform of industries using too much energy and through more severe law enforcement than now. Therefore, it is desirable to promote regional cooperation more progressively and practically in the direction of building China's ability to solve environmental problems.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.