Youngjong, Kim;Sungil, Jang;Myung Ho, Kim;Jinho, Park
Journal of Information Processing Systems
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v.18
no.6
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pp.741-754
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2022
Openstack is widely used as a representative open-source infrastructure of the service (IaaS) platform. The Openstack Identity Service is a centralized approach component based on the token including the Memcached for cache, which is the in-memory key-value store. Token validation requests are concentrated on the centralized server as the number of differently encrypted tokens increases. This paper proposes the practical Byzantine fault tolerance (PBFT) blockchain-based Openstack Identity Service, which can improve the performance efficiency and reduce security vulnerabilities through a PBFT blockchain framework-based decentralized approach. The experiment conducted by using the Apache JMeter demonstrated that latency was improved by more than 33.99% and 72.57% in the PBFT blockchain-based Openstack Identity Service, compared to the Openstack Identity Service, for 500 and 1,000 differently encrypted tokens, respectively.
The purpose of this study is to reduce carbon dioxide emissions in the cement industry and to collect carbon dioxide generated in industrial facilities such as cement factories and thermal power plants, store and utilize it, and convert high-value-added resources. While conventional Ordinary Portland Cement is characterized by hardening through hydration reactions, basic research is underway to develop cement that reacts with carbon dioxide and converts it into carbonate mineralization.
Proceedings of the Korea Information Processing Society Conference
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2007.05a
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pp.443-446
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2007
A new association rule mining algorithm, which reflects the strategic importance of associative relationships between items, was developed and presented in this paper. This algorithm exploits the basic framework of Apriori procedures and TSAA(transitive support association Apriori) procedure developed by Hyun and Choi in evaluating non-frequent itemsets. The algorithm considers the strategic importance(weight) of feature variables in the association rule mining process. Sample feature variables of strategic importance include: profitability, marketing value, customer satisfaction, and frequency. A database with 730 transaction data set of a large scale discount store was used to compare and verify the performance of the presented algorithm against the existing Apriori and TSAA algorithms. The result clearly indicated that the new algorithm produced substantially different association itemsets according to the weights assigned to the strategic feature variables.
Proceedings of the Korea Information Processing Society Conference
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2007.05a
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pp.1195-1198
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2007
In this paper we study the problem of real time support in wireless sensor networks and propose a Real time Geographic Routing Protocol (ReGeo) which routes a packet towards the destination based on a compromise between distance and queue count to reduce traffic concentration wherever it has been determined to be too high and uses Gradient Table to store the route satisfying the delay constraints. We describe our prototype implementation of ReGeo Routing in TOSSIM - a TinyOS mote simulator. The simulation results show that the proposed routing protocol not only increases the packet delivery ratio but also keeps overall End to End Delay under a bounded value.
Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.
Early conflict research in channel and organization area have focused on the definition of conflict construct, its cause, consequence and identified conflict resolution management. Recent studies about conflict, however, have explored new assumption of complexity, a multidimensional conflict construct, contextual conflict management strategies, positive and negative conflict/consequence, and the conflict resolution strategy. Although many literatures exists on channel conflict resolution, little research has been done about relationship learning and performance from conflict resolution perspective. This study explores how channel members can achieve a relationship learning, as a conflict resolution mechanism, which enhance co-created value in marketing channel relationship. Therefore we propose that conflict resolution strategies(collaborating behavior and avoiding behavior) influence channel performance(effectiveness and efficiency) through relationship learning processes(learning via information exchange, joint interpretation and coordination, relationship-specific knowledge memory), in view of buyer-seller relationship. The research model is shown at
This study is conducted to verify existing differences in consumer attitude according to distributor type and PB product type. Pre-test was conducted for this study in order to select the distributor and to classify the product type, FGI was conducted with 10 graduate students of K university in Kyong-gi. This study survey housewives, office workers, and university students excluding the participants in the pre-test. In the final analysis, research hypothesis is verified through the data of 280 answers in Korea. This research is conducted with a factor design of 3 types of distributors -department store, discount store, convenience store-and 2 types of product -utilitarian product, hedonic product. To verify the hypotheses, ANOVA is carried out. Reliability test of each measurement variables, Cronbach α coefficient is used. For each analysis, SPSS Windows 15.0 statistical program is used. The findings suggest that First, according to the size and characteristics, distributors are classified into department stores, discount stores, and convenience stores and it is verified whether if there are differences in consumers' attitude (product attitude, brand attitude and purchase intention) by the effect of different distributors. Results showed that product attitude is statistically significant. Second, product type is classified by two categories according to whether the product seeks for practicality or emotional pleasure - Utilitarian product and Hedonic product. In this context, the result after verifying whether if there is difference in the attitudes -product attitude, brand attitude, and purchase intention - in accordance with the product types is shown that utilitarian products makes bigger difference compared to hedonic products. Third, it is confirmed that there is interaction effect between product attitude and purchase intention according to the distributer type and product type. However, we find that in terms of brand attitude, there is no interaction effect. The implications of this research is as the following. First, we propose the need of PB product development and marketing strategy, which considers the product types in accordance with the scale and features of each distributor. Second, PB products should break away from the simplicity of standardized products and consider the different features of distributors. Distributors will be in need of a strategy to build a compelling brand that can differentiate itself from other distributors. This will contribute to the improvement in reliability and formation of product value.
In a situation where the use and introduction of Large Language Models (LLMs) is expanding due to recent developments in generative AI technology, it is difficult to find actual application cases or implementation methods for the use of internal company data in existing studies. Accordingly, this study presents a method of implementing generative AI services using the LLM application architecture using the most widely used LangChain framework. To this end, we reviewed various ways to overcome the problem of lack of information, focusing on the use of LLM, and presented specific solutions. To this end, we analyze methods of fine-tuning or direct use of document information and look in detail at the main steps of information storage and retrieval methods using the retrieval augmented generation (RAG) model to solve these problems. In particular, similar context recommendation and Question-Answering (QA) systems were utilized as a method to store and search information in a vector store using the RAG model. In addition, the specific operation method, major implementation steps and cases, including implementation source and user interface were presented to enhance understanding of generative AI technology. This has meaning and value in enabling LLM to be actively utilized in implementing services within companies.
Purpose Product characteristics and price value in website have strongly effects on customer satisfaction. Especially, in the online shopping site, the scarcity limits the customer's opportunity to purchase the product. Thus scarcity has been proposed as a important factor that makes the customer highly aware of the merchantability of the product. The scarcity in the web store is used as an important variable to make purchasing decisions of users easier by psychological pressure. In the case of scarce products with price discounts in online commerce, advertising formats that highlight scarcity value in the web commerce market are very effective in enhancing purchase intentions of consumers. Unlike offline stores, the importance of scarcity becomes more important when reflecting the characteristics of online commerce. Therefore, this study intends to confirm the influence of the degree of price discounts and scarcity information presented by Web sites on consumer purchase behavior in Web purchase behavior. Design/methodology/approach This study conducted a web-based experimental study on price sensitivity and price discount. Therefore, we created experimental web-sites that offer two stimuli according to the discount rate. The 200 respondents were randomly assigned. The stimuli were fictitious based on tourism products. The first stimulus presented the price discount(15% discount) with basic explanation about the package of the tourist package. The stimuli assigned to the second group were used for groups with high price discount intensity(65% discount). In this way, the two stimuli clearly distinguished the level of price discount intensity. This paper conducted t-test analysis and structural equation to analyze the experiemental results after confirming the reliability and validity. Findings The results of this study are as follows. The difference in price discount intensity (15% vs 65%) with scarcity showed the mean difference among all the variables. Therefore, this study concluded that there is a significant difference between the price discount of 15% and 65% for the acquisition value and transaction value of users. In particular, consumers' purchase intention is greater and product recommendation intensity is stronger when the price discount is 65%. As a result, the high degree of the price discount intensity with scarcity exerts a greater influence on consumers' purchase intentions. Product scarcity also have a significant impact on perceived value of users. Therefore, purchase intention of customers increases when perceived value increases their profit and pleasure feeling.
The purpose of this study is to investigate the effect of salesperson's adaptive selling tactics on consumers who purchase Small and Medium Enterprises' product with the help of salespeople in the store. Specifically, we examine whether salesperson's adaptive selling tactics affect consumers hedonic shopping value and utilitarian shopping value. Furthermore, we try to figure out the effect of shopping value on the salesperson relationship commitment and consumer product satisfaction. The subjects of this study have surveyed the consumers with experience in purchasing SME's products through salespeople in the Gyeongnam area. As data process, SPSS 21.0 was used as the analysis tool and AMOS 21.0 was used to analyze the structural equation model. The implications of these findings are as follows. First, the salesperson's role has a positive effect on the consumer's product satisfaction. Second, it can be suggested that the salesperson's adaptive selling tactics are an important variable affecting the consumer's shopping value. Third, the results on the consumers who have hedonic shopping value have a strong positive influence rather than the consumers who have the utilitarian shopping value. The adaptive selling tactics of the salesperson should be encouraged to increase sales performance. In order to generate more performance in a competitive market environment, it is necessary to continuously strive to increase contact between the salesperson and the consumer in order to improve the quality of the relationship.
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