• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.167-178
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• 2021

The purpose of this study is to analyze the direct investment decision factors in the Kaesong Industrial Complex and Vietnam, and to contribute to the creation of domestic jobs and the revitalization of the inter-Korean economy. According to the analysis, most of the Kaesong Industrial Complex and Vietnamese investment companies are entering the complex for the purpose of utilizing cheap labor, cheap factory locations, sales/development of local markets, and bypass export production bases in third countries. This can be divided into production-efficient investors using differences in production price such as labor costs and market-oriented investors to sell and expand the local market, which seems to be consistent with global direct investment patterns such as Nike, Apple, and Amazon. However, even if the North Korea-U.S. denuclearization talks ease or lift sanctions, Vietnamese investors' willingness to invest in the North Korea has been most burdened by the possibility of closing special economic zones due to political risks. Last but not least, it is important to note that those willing to invest in North Korea are mostly smaller enterprises in textiles, sewing, footwear and leather industries-those that benefit from low-cost labor. Since their size is small, they need policy support in financing, especially in the early stages of their business. Even after they grow past the early stages, those without collateral would still need state guarantee letters to get financing. Thus, it is worth considering to use the Inter-Korean Cooperation Fund to compensate commercial banks for bad loan loss or for low-interest loans for smaller SMEs. The interviews with SMEs found that red-tape is one of the biggest difficulties they face. Thus, it is recommended that a one-stop service agency should be established to cover all processes and issues related to inter-Korean economic cooperation to eliminate redundancy and expediate government support for SMEs.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.