• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• 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.

• Journal of Korean Society of Forest Science
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• v.20 no.1
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• pp.1-44
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• 1973

Needless to say, the management of national forest in all countries is very important in view of the national mission and management purposes. Korean national forest is also in particular significant in promoting national economy for the continuous increasing of the demand for wood, conservation of the land and social welfare. But there's no denying the fact that the leading aim of the Korean forest policy has been based upon the conservation of forest resources and recovery of land conservation function instead of improvement of the forest productive capacity. Therefore, the management of national forest should be aimed as an industry in the chain of the Korean national economy. And the increment of the forest productive capacity based on rationalized forest management is also urgently needed. Not only the increment of the timber production but also the establishment of the good forest in quality and quantity are to bring naturally many functions of conservation and other public benefits. In 1908 Korean national forest was historically established for the first time as a result of the notification for ownership, and was divided into two kinds in 1911-1924, such as indisposable national forest for land conservation, forest management, scientific research and public welfare, and the other national forest to be disposed. Indisposable forest is mostly under the jurisdiction of national forest stations (Chungbu, Tongbu, Nambu), and the tother national forests are under custody of respective cities and provinces, and under custody of the other government authorities. As of the end of 1971, national forest land is 19.5% (1,297,708 ha) of the total forest land area, but growing stock is 50.1% ($35,406,079m^3$) of the total forest growing stock, and timber production of national forest is 23.6% ($205,959m^3$) of the year production of total timber in Korea. Accordingly, it is the important fact that national forest occupies the major part of Korean forestry. The author positively affirms that success or failure of the management of national forest controls rise or fall of forestry in Korea. All functions of forest are very important, but among others the function of timber production is most important especially in Korea, that unavoidably imports a large quantity of foreign wood every year (in 1971 import of foreign wood-$3,756,000m^3$, 160,995,000 dollars). So, Korea urgently needs the improvement of forest productive capacity in national forest. But it is difficult that wood production meets the rapid increase of demand for wood to the development of economy, because production term of forestry is long, so national forest management should be rationalized by the effective investment and development of forestry techniques in the long view. Although Korean national forest business has many difficulties in the budget, techniques and the lack of labour due to outflow of rural village labour by development of national economy, and the increase of labour wages and administrative expenses etc. the development of national forest depends on adoption of the suitable forest techniques and management adapted for social and economical development. In this view point the writer has investigated and analyzed the status of the management of national forest in Korea to examine the irrational problems and suggest an improvement plan. The national forestry statistics cited in this study is based on the basic statistics and the statistics of the forest business as of the end of 1971 published by Office of Forestry, Republic of Korea, and the other depended on the data presented by the national forest stations. The writer wants to propose as follows (seemed to be helpful in improvement of Korean national forest management). 1) In the organization of national forest management, more national forest stations should be established to manage intensively, and the staff of working plan officials should be strengthened because of the importance of working plan. 2) By increasing the staff of protection officials, forest area assigned for each protection official should be decreased to 1,000-2,000 ha. 3) The frequent personnel changes of supervisor of national forest station(the responsible person on-the-spot) obstructs to accomplish the consistent management plan. 4) In the working plan drafting for national forest, basic investigations should be carefully practiced with sufficient expenditure and staff not to draft unreal working plan. 5) The area of working-unit should be decreased to less than 2,000 ha on the average for intensive management and the principle of a working-unit in a forest station should be realized as soon as possible. 6) Reforestation on open land should be completed in a short time with a debt of the special fund(a long term loan), and the land on which growing hardwood stands should be changed with conifers to increase productivity per unit area, and at the same time techical utilization method of hardwood should be developed. 7) Expenses of reforestation should be saved by mechanization and use of chemicals for reforestation and tree nursery operation providing against the lack of labour in future. 8) In forest protection, forest fire damage is enormous in comparison with foreign countries, accordingly prevention system and equipment should be improved, and also the minimum necessary budget should be counted up for establishment and manintenance of fire-lines. 9) Manufacture production should be enlarged to systematize protection, processing and circulation of forest business, and, by doing this, mich benefit is naturally given for rural people. 10) Establishment and arrangement of forest road networks and erosion control work are indispensable for the future development of national forest itself and local development. Therefore, these works should be promoted by the responsibility of general accounting instead of special accounting. 11) Mechanization of forest works should be realized for exploiting hinterlands to meet the demand for timber increased and for solving lack of labour, consequently it should promote import of forest machines, home production, training for operaters and careful adminitration. 12) Situation of labour in future will grow worse. Therefore, the countermeasure to maintain forest labourers and pay attention to public welfare facilities and works should be considered. 13) Although the condition of income and expenditure grows worse because of economical change, the regular expenditure should be fixed. So part of the surplus fund, as of the end of 1971, should be established for the fund, and used for enlarging reforestation and forest road networks(preceding investment in national forest).