• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Food Science of Animal Resources
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• v.29 no.4
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• pp.506-512
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• 2009

Salmonellosis is an important worldwide foodborne infectious disease that is transmitted by many food vehicles including raw and processed animal products and fresh produce. In this study, the effectiveness of automated ELISA ($VIDAS^{(R)}$) and realtime PCR in the detection of Salmonella spp. in steamed pork and raw broccoli sprouts was evaluated by comparing their results with those of a conventional culture method. Bulk samples (500 g) of steamed pork and raw broccoli sprouts were inoculated with various levels of Salmonella and divided into 20 samples (25 g each). All the samples, including the controls, were analyzed using a conventional culture method, $VIDAS^{(R)}$, and real-time PCR to detect the presence of Salmonella. In addition, the levels of background flora in the steamed pork and the raw broccoli sprouts were determined. In the steamed pork that contained less than 100 CFU/g of aerobic bacteria, all three methods detected low levels of Salmonella without a statistical difference in their performance. In the broccoli sprouts with high quantities of background flora (ca. $6.7{\times}10^7$ CFU/g), however, all three methods were unable to detect low levels of Salmonella, and real-time PCR and $VIDAS^{(R)}$ more sensitively detected Salmonella than the culture method, with significant statistical differences. In conclusion, $VIDAS^{(R)}$ and real-time PCR could be superior to conventional culture methods in detecting Salmonella in food with high levels of background flora.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.157-167
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• 2009

Recently, the leakage of confidential information and personal information is taking place on the Internet more frequently than ever before. Most of such online security incidents are caused by attacks on vulnerabilities in web applications developed carelessly. It is impossible to detect an attack on a web application with existing firewalls and intrusion detection systems. Besides, the signature-based detection has a limited capability in detecting new threats. Therefore, many researches concerning the method to detect attacks on web applications are employing anomaly-based detection methods that use the web traffic analysis. Much research about anomaly-based detection through the normal web traffic analysis focus on three problems - the method to accurately analyze given web traffic, system performance needed for inspecting application payload of the packet required to detect attack on application layer and the maintenance and costs of lots of network security devices newly installed. The UTM(Unified Threat Management) system, a suggested solution for the problem, had a goal of resolving all of security problems at a time, but is not being widely used due to its low efficiency and high costs. Besides, the web filter that performs one of the functions of the UTM system, can not adequately detect a variety of recent sophisticated attacks on web applications. In order to resolve such problems, studies are being carried out on the web application firewall to introduce a new network security system. As such studies focus on speeding up packet processing by depending on high-priced hardware, the costs to deploy a web application firewall are rising. In addition, the current anomaly-based detection technologies that do not take into account the characteristics of the web application is causing lots of false positives and false negatives. In order to reduce false positives and false negatives, this study suggested a realtime anomaly detection method based on the analysis of the length of parameter value contained in the web client's request. In addition, it designed and suggested a WAF(Web Application Firewall) that can be applied to a low-priced system or legacy system to process application data without the help of an exclusive hardware. Furthermore, it suggested a method to resolve sluggish performance attributed to copying packets into application area for application data processing, Consequently, this study provide to deploy an effective web application firewall at a low cost at the moment when the deployment of an additional security system was considered burdened due to lots of network security systems currently used.

• Korean Journal of Environmental Biology
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• v.37 no.3
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• pp.433-445
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• 2019

This study was designed to develop simple tools to easily and efficiently predict the occurrence of algal bloom in agricultural lakes. Physicochemical water quality parameters were examined to reflect the phytoplankton productivity in 182 samples collected from 15 agricultural lakes from April to October 2018. Total phytoplankton abundance was significantly correlated with chlorophyll-a (Chl-a) (r=0.666) and Secchi depth (SD) (r= -0.351). The abundances of cyanobacteria and harmful cyanobacteria were also correlated with Chl-a (r=0.664, r=0.353) and SD (r= -0.340, r= -0.338), respectively, but not with total nitrogen (TN) and total phosphorus (TP). The Chl-a concentration was correlated with SD (r= -0.434), showing a higher similarity than phytoplankton abundance. Therefore, Chl-a and SD were selected as diagnostic factors for algal bloom prediction, instead of analyzing the standing crop of harmful cyanobacteria used in algae alarm systems. Specifically, accurate diagnoses were made using realtime SD measurements. The algal bloom diagnostic tool is an inverse cone-shaped container with an algal bloom diagnosis chart that modified SD and turbidity measurement methods. Lake water was collected to observe the number of rings visible in the container or the number indicated in each ring, depending on the degree of algal bloom,and to determine the final stage of algal blooming by comparison to the colorimetric level on the diagnosis chart. For an accurate diagnosis, we presented 4-step diagnostic criteria based on the concentration of Chl-a and the number of rings and a fan-shaped algal bloom diagnosis chart with Hexa code names. This tool eliminated the variables and errors of previous methods and the results were easily interpreted. This study is expected to facilitate the diagnosis of algal bloom in agricultural lakes and the establishment of an efficient algal bloom management plan.