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Design and Implementation of MongoDB-based Unstructured Log Processing System over Cloud Computing Environment (클라우드 환경에서 MongoDB 기반의 비정형 로그 처리 시스템 설계 및 구현)

  • Kim, Myoungjin;Han, Seungho;Cui, Yun;Lee, Hanku
    • 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.

Results of Definitive Chemoradiotherapy for Unresectable Esophageal Cancer (절제 불가능한 식도암의 근치적 항암화학방사선치료의 성적)

  • Noh, O-Kyu;Je, Hyoung-Uk;Kim, Sung-Bae;Lee, Gin-Hyug;Park, Seung-Il;Lee, Sang-Wook;Song, Si-Yeol;Ahn, Seung-Do;Choi, Eun-Kyung;Kim, Jong-Hoon
    • Radiation Oncology Journal
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    • v.26 no.4
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    • pp.195-203
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    • 2008
  • Purpose: To investigate the treatment outcome and failure patterns after definitive chemoradiation therapy in locally advanced, unresectable esophageal cancer. Materials and Methods: From February 1994 to December 2002, 168 patients with locally advanced unresectable or medically inoperable esophageal cancer were treated by definitive chemoradiation therapy. External beam radiation therapy (EBRT) ($42{\sim}46\;Gy$) was delivered to the region encompassing the primary tumor and involved lymph nodes, while the supraclavicular fossa and celiac area were included in the treatment area as a function of disease location. The administered cone-down radiation dose to the gross tumor went up to $54{\sim}66\;Gy$, while the fraction size of the EBRT was 1.8-2.0 Gy/fraction qd or 1.2 Gy/fraction bid. An optional high dose rate (HDR) intraluminal brachytherapy (BT) boost was also administered (Ir-192, $9{\sim}12\;Gy/3{\sim}4\;fx$). Two cycles of concurrent FP chemotherapy (5-FU $1,000\;mg/m^2$/day, days $2{\sim}6$, $30{\sim}34$, cisplatin $60\;mg/m^2$/day, days 1, 29) were delivered during radiotherapy with the addition of two more cycles. Results: One hundred sixty patients were analyzable for this review [median follow-up time: 10 months (range $1{\sim}149$ months)). The number of patients within AJCC stages I, II, III, and IV was 5 (3.1%), 38 (23.8%), 68 (42.5%), and 49 (30.6%), respectively. A HDR intraluminal BT was performed in 26 patients. The 160 patients had a median EBRT radiation dose of 59.4 Gy (range $44.4{\sim}66$) and a total radiation dose, including BT, of 60 Gy (range $44.4{\sim}72$), while 144 patients received a dose higher than 40 Gy. Despite the treatment, the disease recurrence rate was 101/160 (63.1%). Of these, the patterns of recurrence were local in 20 patients (12.5%), persistent disease and local progression in 61 (38.1%), distant metastasis in 15 (9.4%), and concomitant local and distant failure in 5 (3.1%). The overall survival rate was 31.8% at 2 years and 14.2% at 5 years (median 11.1 months). Disease-free survival was 29.0% at 2 years and 22.7% at 5 years (median 10.4 months). The response to treatment and N-stage were significant factors affecting overall survival. In addition, total radiation dose (${\geq}50\;Gy$ vs. < 50 Gy), BT and fractionation scheme (qd. vs. bid.) were not significant factors for overall survival and disease-free survival. Conclusion: Survival outcome after definitive chemoradiation therapy in unresectable esophageal cancer was comparable to those of other series. The main failure pattern was local recurrence. Survival rate did not improve with increased radiation dose over 50 Gy or the use of brachytherapy or hyperfractionation.

A Study on Searching for Export Candidate Countries of the Korean Food and Beverage Industry Using Node2vec Graph Embedding and Light GBM Link Prediction (Node2vec 그래프 임베딩과 Light GBM 링크 예측을 활용한 식음료 산업의 수출 후보국가 탐색 연구)

  • Lee, Jae-Seong;Jun, Seung-Pyo;Seo, Jinny
    • Journal of Intelligence and Information Systems
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    • v.27 no.4
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    • pp.73-95
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    • 2021
  • This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

A Study on Recent Research Trend in Management of Technology Using Keywords Network Analysis (키워드 네트워크 분석을 통해 살펴본 기술경영의 최근 연구동향)

  • Kho, Jaechang;Cho, Kuentae;Cho, Yoonho
    • Journal of Intelligence and Information Systems
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    • v.19 no.2
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    • pp.101-123
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    • 2013
  • Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

Effects of Concurrent Chemotherapy and Postoperative Prophylactic Paraaortic Irradiation for Cervical Cancer with Common Iliac Node Involvement (자궁경부암의 근치적 절제술 후 총장골동맥림프절 침범 시 동시항암화학치료와 예방적 대동맥주위림프절 방사선조사의 효과)

  • Han, Tae-Jin;Wu, Hong-Gyun;Kim, Hak-Jae;Ha, Sung-Whan;Kang, Soon-Beom;Song, Yong-Sang;Park, Noh-Hyun
    • Radiation Oncology Journal
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    • v.28 no.3
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    • pp.125-132
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    • 2010
  • Purpose: To retrospectively assess the advantages and side effects of prophylactic Paraaortic irradiation in cervical cancer patients with common iliac nodal involvement, the results for survival, patterns of failure, and treatment-related toxicity. Materials and Methods: From May 1985 to October 2004, 909 patients with cervical carcinoma received postoperative radiotherapy at the Seoul National University Hospital. Among them, 54 patients with positive common iliac nodes on pathology and negative Paraaortic node were included in the study. In addition, 44 patients received standard pelvic irradiation delivered 50.4 Gy per 28 fractions (standard irradiation group), and chemotherapy was combined in 16 of them. The other 10 patients received pelvic irradiation at a dose of 50.4 Gy per 28 fractions in addition to Paraaortic irradiation at 45 Gy per 25 fractions (extended irradiation group). In addition, all of them received chemotherapy in combination with radiation. Follow-up times for pelvic and Paraaortic irradiation ranged from 6 to 201 months (median follow-up time, 58 months) and 21 to 58 months (median follow-up time, 47 months), respectively. Results: The 4-year overall survival, disease free survival, and distant metastasis free survival in the standard irradiation group and extended irradiation group were 67.2% vs. 90.0% (p=0.291), 59.0% vs. 70.0% (p=0.568) and 67.5% vs. 90.0% (p=0.196), respectively. The most common site of first failure for the standard irradiation group was the paraaortic lymph node, while no paraaortic failure was observed in the extended irradiation group. Relatively, hematologic toxicity grade 3 or greater was common in the extended irradiation group (2/10 extended vs. 2/44 standard), while gastrointestinal toxicity of grade 3 or greater was lower (2/10 extended vs. 6/44 standard), and urologic toxicity of grade 3 or greater was observed in the standard irradition group only (0/10 vs. 3/44). Conclusion: Concurrent chemotherapy and prophylactic Paraaortic irradiation in patients with common iliac nodal involvement showed slightly improved clinical outcomes aside from increased hematologic toxicity, which was statistically insignificant. Considering the relatively small number of patients and short follow-up times, additional studies are needed to obtain more conclusive outcomes.

The Early Experience with a Laparoscopy-assisted Pylorus-preserving Gastrectomy: A Comparison with a Laparoscopy-assisted Distal Gastrectomy with Billroth-I Reconstruction (복강경 보조 유문부보존 위절제술의 초기 경험: 복강경 보조 원위부 위절제술 후 Billroth-I 재건술과의 비교)

  • Park, Jong-Ik;Jin, Sung-Ho;Bang, Ho-Yoon;Chae, Gi-Bong;Paik, Nam-Sun;Moon, Nan-Mo;Lee, Jong-Inn
    • Journal of Gastric Cancer
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    • v.8 no.1
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    • pp.20-26
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    • 2008
  • Purpose: Pylorus-preserving gastrectomy (PPG), which retains pyloric ring and gastric function, has been accepted as a function-preserving procedure for early gastric cancer for the prevention of postgastrectomy syndrome. This study was compared laparoscopy-assisted pylorus-preerving gastrectomy (LAPPG) with laparoscopy-assisted distal gastrectomy with Billroth-I reconstruction (LADGB I). Materials and Methods: Between November 2006 and September 2007, 39 patients with early gastric cancer underwent laparoscopy-assisted gastrectomy in the Department of Surgery at Korea Cancer Center Hospital. 9 of these patients underwent LAPPG and 18 underwent LADGBI. When LAPPG was underwent, we preserved the pyloric branch, hepatic branch, and celiac branch of the vagus nerve, the infrapyloric artery, and the right gastric artery and performed D1+$\beta$ lymphadenectomy to the exclusion of suprapyloric lymph node dissection. The distal stomach was resected while retaining a $2.5{\sim}3.0\;cm$ pyloric cuff and maintaining a $3.0{\sim}4.0\;cm$ distal margin for the resection. Results: The mean age for patients who underwent LAPPG and LADGBI were $59.9{\pm}9.4$ year-old and $64.1{\pm}10.0$ year-old, respectively. The sex ratio was 1.3 : 1.0 (male 5, female 4) in the LAPPG group and 2.6 : 1.0 (male 13, female 5) in the LADGBI group. Mean total number of dissected lymph nodes ($28.3{\pm}11.9$ versus $28.1{\pm}8.9$), operation time ($269.0{\pm}34.4$ versus $236.3{\pm}39.6$ minutes), estimated blood loss ($191.1{\pm}85.7$ versus $218.3{\pm}150.6\;ml$), time to first flatus ($3.6{\pm}0.9$ versus $3.5{\pm}0.8$ days), time to start of diet ($5.1{\pm}0.9$ versus $5.1{\pm}1.7$ days), and postoperative hospital stay ($10.1{\pm}4.0$ versus $9.2{\pm}3.0$ days) were not found significant differences (P>0.05). The postoperative complications were 1 patient with gastric stasis and 1 patient with wound seroma in LAPPG group and 1 patient with left lateral segment infarct of liver in the LADGB I group. Conclusion: Patients treated by LAPPG showed a comparable quality of surgical operation compared with those treated by LADGBI. LAPPG has an important role in the surgical management of early gastric cancer in terms of quality of postoperative life. Randomized controlled studies should be undertaken to analyze the optimal survival and long-term outcomes of this operative procedure.

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