• Journal of Distribution Science
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• v.9 no.2
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• pp.111-122
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• 2011

The purpose of this study is to survey the successful cases of small and medium Business Systematization Cognition by examining their entrepreneurial characteristics and analysing the factors affecting their success. To that end, previous studies on the association types of small businesses were studied. A research model was developed, and research hypotheses for an empirical analysis were established upon it. Suh et al. (2010) insist on the importance of Small Business Systematization in Korea but also show that small business performance is suffering: they are too small to stand alone. That is why association is so crucial for them: they must stand together. Unfortunately, association is difficult, as they have few specific links and little motivation. Even in franchising networks, association tends to be initiated by big franchisers, not small ones. In that sense, association among small businesses is crucial for their long-term survival. With this in mind, this study examines how they think and feel about the issue of 'Industrial Classification', how important Industrial Classification is to their business success, and what kinds of problems it raises in the markets. This study seeks the different cognitions among the association types of small businesses from the perspectives of participation motivation, systematization expectation, policy demand level, and management performance. We assume that different industrial classification types of small businesses will have different cognitions concerning these factors. There are four basic industrial classification types of small businesses: retail sales, restaurant, service, and manufacturing. To date, most of the studies in this area have focused on collecting data on the external environments of small businesses or performing statistical analyses on their status. In this study, we surveyed 4 market areas in Busan, Masan, and Changwon in Korea, where business associations consist of merchants, shop owners, and traders. We surveyed 330 shops and merchants by sending a questionnaire or visiting. Finally, 268 questionnaires were collected and used for the analysis. An ANOVA, T-test, and regression analyses were conducted to test the research hypotheses. The results demonstrate that there are differences in cognition depending upon the industrial classification type. Restaurants generally have a higher cognition concerning job offer problems and a lower cognition concerning their competitiveness. Restaurants also depend more on systematization expectation than do the other industrial classification types. On the policy demand level, restaurants have a higher cognition. This study identifies several factors that are contributing to management performance through differences in cognition that depend upon association type: systematization expectation and policy demand level have positive effects on management performance; participation motivation has a negative effect on management performance. We confirm also that the image factors of different cognitions are linked to an awareness of the value of systematization and that these factors show sequential and continual patterns in the course of generating performances. In conclusion, this study carries significant implications in its classifying of small businesses into the four different associational types (retail sales, restaurant, services, and manufacturing). We believe our study to be the first one to conduct an empirical survey in this subject area. More studies in this area will likely use our research frameworks. The data show that regionally based industrial classification associations such as those in rural cities or less developed areas tend to suffer more problems than those in urban areas. Moreover, restaurants suffer more problems than the norm. Most of the problems raised in this study concern the act of 'associating itself'. Most associations have serious difficulties in associating. On the other hand, the area where they have the least policy demand is that of service types. This study contributes to the argument that associating, rather than financial assistance or management consulting, promotes the start-up and managerial performance of small businesses. This study also has some limitations. The main limitation is the number of questionnaires. We could not survey all the industrial classification types across the country because of budget and time limitations. If we had, we could have produced many more useful results and enhanced the precision of our analysis. The history of systemization is very short and the number of industrial classification associations is relatively low in Korea. We should keep in mind, though, that this is very crucial to systemization entrepreneurs starting their businesses, as it can heavily affect their chances of success. Being strongly associated with each other might be critical to the business success of industrial classification members. Thus, the government needs to put more effort and resources into supporting the drive of industrial classification members to become more strongly associated.

• Korean Journal of Heritage: History & Science
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• v.51 no.2
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• pp.170-203
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• 2018

From the period of Japanese colonialism up to the present, the researchers of archaeology and ancient history in Korea and Japan have paid much attention to the Honam area. Recently the ruins and relics of Wae lineage are often discovered at archaeological excavation sites in this region. In particular, at least 17 keyhole-shaped mounds were confirmed. The fact that three mounds were built on one site was newly revealed. Haniwa, a representative earthenware object of the Kofun period in Japan, was discovered as well. Therefore, the study of a historical meaning of archaeological materials about Wae lineage in the Honam area thus far must be reexamined. The ruins and relics of Wae lineage in the Honam area have been studied by selecting only specific cases. I identified all the ruins and relics of Wae lineage in the Honam area and analyzed the process of their change in this paper. I reviewed the relationship between Wae and Mahan, and the purpose of their negotiation based on archaeological characteristics, changing processes, and historical records on a quarterly basis. The ruins and relics of Wae lineage have increased and widely spread since the early period of the 5th century in the Honam area. This tendency continued until the late period of the 6th century. Weapons of Wae lineage were introduced and tombs in the style of Wae were built from the late 4th century to the early period of the 5th century (TG232~TK216 period). Sueki was introduced from the middle to late period of the 5th century (TK208~TK23 period). Keyhole-shaped mounds and tombs in the style of Wae were built from the late period of the 5th century to the early period of the 6th century (TK47~MT15 period). Japanese weapons were introduced from the middle to late period of the 6th century (TK10~TK209 period). In other words, the archaeological appearance is different in each quarterly period. There was an intensive diplomatic relationship between Baekje and Wae in the TG232~TK216 period. The military might be included in a mission of Wae. The materials of Wae lineage of this period are likely to be related to this. Sueki spread to the inland part of the Honam area in the TK208~TK23 period. This Sueki tends to be excavated on advantageous sites for the accumulation and distribution of supplies. The main characters of the keyhole-shaped mounds are presumed to be a group of traders which were under the control of a certain influence in the north of Kyushu. The keyhole-shaped mounds were abruptly built at some distance from mounds of the leaders in the Honam area. Additionally, there was no special influence base to which the characters of the keyhole-shaped mounds belonged in the surroundings. However, it might have been impossible for the group of traders to build the keyhole-shaped mounds without the support from the residents at all because there was a big difference in the building technology of the keyhole-shaped mounds between Japan and the Honam area. The time of building some keyhole-shaped mounds is the same or almost the same with that of the mounds built for the regional leaders. This proves a close relationship with the residents as well. What do the archaeological materials of Wae lineage which have been used and buried mean over 200 years in the Honam area? Geumgwan Gaya, which had exported iron resources to Japan, perished in the early period of the 5th century. Instead of Gaya, the Honam area might have played an important role to supply the necessary resources to Japan. We assume that the Japanese (Wae) actively worked to acquire various resources focusing on the underground resources in the Honam area.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.