• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• IMMUNE NETWORK
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• v.5 no.2
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• pp.78-88
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• 2005

Background: Collagen-induced arthritis (CIA) in mice is animal model of autoimmune disease known as rheumatic arthritis in human. We investigated CII-specific CD4+ T cell receptor usage in CIA mice. Methods: In CIA model, draining lymph node (dLN) CD4+ T cells and splenocytes at $3^{rd},\;5^{th},\;8^{th}$ week, we investigated CII-specific T cell proliferation, production of IL-17, IFN-${\gamma}$, TNF-${\alpha}$, IL-4 and IL-10. And we also performed anti-CII IgG Ab measurements in serum level, TCRV ${\beta}$ usage and T cell clonality with RT-PCR-SSCP analysis. Also, we performed proliferative response against CII when CII-specific T cell subset is deleted. Results: CIA mice showed more increase in the serum level of anti-CII IgG than normal mice after induction of arthritis. And the level of anti-CII IgG2a in CIA mice was increased after $3^{rd}$ week after primary immunization, while anti-CII IgG1 was decreased. Draining LN CD4+ T cells have proliferated against CII stimulation at $3^{rd}$ week after $1^{st}$immunization. CD4+T cells derived from dLN of CIA mice produced proinflammatory cytokine IFN-${\gamma}$, IL-17 etc. Draining LN CD4 T cells of CIA presented higher proportion of CD4+V ${\beta}3$+subset compared to those of normal mice at $3^{rd}$ week after $1^{st}$ immunization, and they were increased in proportion by CII stimulation. Draining LN CD4+ T cells without TCRV ${\beta}3+/V{\beta}8.1/8.2+/V{\beta}$10b+cells were not responsive against CII stimulation. But, CII-reactive response of TCRV ${\beta}3-/V{\beta}8.1/8.2-/V{\beta}$10b- T cells was recovered when $V{\beta}3+$ T cells were added in culture. Conclusion: Our results indicate that CD4+$V{\beta}3+$ T cells are selectively expanded in dLN of CIA mice, and their recovery upon CII re-stimulation in vitro, as well as the production Th1-type cytokines, may play pivotal role in CIA pathogenesis.

• Clinical and Experimental Pediatrics
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• v.51 no.9
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• pp.1012-1017
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• 2008

Purpose : This study aimed to assess the mechanical properties of the abdominal aorta in school-aged patients treated for Kawasaki disease and in normal, healthy children. Methods : This study examined 28 children with Kawasaki disease who had been followed up on and 30 healthy subjects of the same age and gender. We recorded systolic (Ps) and diastolic (Pd) blood pressure values and the aortic diameter at both minimum diastolic (Dd) and maximum systolic (Ds) expansion using two-dimensional echocardiography. These measurements were used to determine 1) aortic strain: S=(Ds-Dd)/Dd; 2) pressure strain elastic modulus: Ep=(Ps-Pd)/S; and 3) normalized Ep: $Ep^*=Ep/Pd$. Results : Ep (P=0.008) and $Ep^*$ (P=0.043) of the Kawasaki disease group were relatively high compared to those of the control group. Ep (P=0.002) and $Ep^*$ (P=0.015) of patients with coronary aneurysm were also relatively high compared with those of patients without coronary aneurysm, but lipid profiles did not differ, except for homocysteine (P=0.008). Therefore, in patients with coronary aneurysm, aortic stiffness was higher, compared to not only the control group but also patients without coronary aneurysm. However, in patients without coronary aneurysm, aortic stiffness was not significant, different compared to the control group. Conclusion : Measuring aortic distensibility may be helpful in assessing the risk of early atheroscletic change in the long-term management of Kawasaki disease.

• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.1 no.1
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• pp.89-115
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• 1968

1. The experiment was carried out for investigating the interaction between potassium nutrition and thermoperiod (as an environment regulating factor) in relation to behaviors of several nutrients including phosphorus-32 and Potassium-42 in IPOMOEA BATAS. 2. To obtain same condition to trace the behaviors of phosphorus and potassum-42 they were simultaneously incorporated to roots. The determination of each CPM by counting twice with adequate interval and calculating true CPM of each isotope according to different half-life, was carried out with satisfactory. 3. Some specific symptoms i.e, chlorosis and withering of growing point under the condition of lower potassium level were found and was accelerated by the low night temperature. 4. A manganese shortage in growing point of the lower potassium level was found by activiation analysis and very low distribution ratio of phosphorus-32 and potassium-42 in the growing point of the lower potassium level was manifested, though the contents of nitrogen, phosphorus, potassium, sodium and magnesium were not in great difference. 5. In addition to the low water content with appearence of "hard", shorterning internode and lower ratio of roots to shoot as well as the symptoms of potassium deficiency such as brown spot in leaf blade and necrosis of leaf margin were appeared at later stage of experiment at the lower potassium level. 6. Very stimulating vegetative growth, e.g, large plant length, leaf expansion, increasing node number and fresh weight as well as high ratio of roots to shoot, high water content was resulted in the condition of higher potassium level. 7. A specific interaction between higher potassium level and thermoperiod was found, that is, the largest tuber production and the largest ratio of roots to shoot were resulted in the combined condition of higher potassium level and constant temperature while the largest plant length, fresh weight etc. i.e. the most stimulative vegetative growth was resulted in the combined condition of higher potassium level and low night temperature. 8. Comparatively low water content in the former condition of stimulative tuber production was resulted(especially at the tuber thickening stage), while high water content in the latter condition of stimulative vegetation was resulted though the higher potassium level made generally high water contents. 9. The nitrogen contents of soluble and insoluble did not make distinct difference between the lower and higher potassium level. 10. Though the phosphorus contents were not distinctly different by the potassium level, the lower potassium level made the percentage of phosphorus increased at tuber forming stage accumulating more phosphorus in roots, while the higher potassium level decreased percentage of phosphorus at that stage. 11. The higher potassium level made distinctly high potassium contents than the lower potassium level and increased contents at the tuber forming stage through both conditions. 12. The sodium contents were low in the condition of higher potassium level than the lower potassium level and decreased at tuber forming stage in both conditions, on the contary of potassium. 13. Except the noticeable deficeney of manganese in the growing point of the lower potassium level, mangense and magnesium contents in other organs did not make distinct difference according to the potassium level. 14. Generally more uptake and large absorption rate of phosphorus-32 and potassium-42 were resulted at the higher potassium level, and the most uptake, and the largest absorption rate of phosphorus and potassium-42 (especially potassium-42 at tuber forming stage) were resulted in the condition of higher potassium level and constant temperature which made the highest tuber production. 15. The higher potassium level stimulated the translocation of phoshorus-32 and potassium-42 from roots to shoots while the lower potassium level suppressed or blocked the translocation. 16. Therefore, very large distribution rate of $p^{32}$, $K^{42}$ in shoot, especially, in growing point, compared with roots was resulted in the higher potassium level. 17. The lower potassium level suppressed the translocation of phosporus-32 from roots to shoot more than that of potassium-42. 18. The uptake of potassium-42 and translocation in IPOMOEA BATATAS were more vivid than phosphorus-32. 19. A specific interaction between potassium nutrition and thermoperiod which resulted the largest tuber production etc. was discussed in relation to behaviors of minerals and potasium-42 etc. 20. Also, the specific effect of the lower and higher potassium level on the growth pattern of IPOMOEA BATATAS were discussed in relation to behaviors of minerals and isotopes. 21. An emphasize on the significance of the higher potassium level as well as the interaction with the regulating factor and problem of potassium level (gradient) for crops product ion were discussed from the point of dynamical and variable function of potassium.