• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.173-180
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• 2008

Certified e-Document Authority keep it with protection legal as a system a guarantee and identifies originality of an e-Record, It presume to be authenticity e-Records and contents of an e-Record prove what was not changed. But, e-Records has high medium degree of dependence and loss danger of information has very high problems. In addition, Because correction(attachment and deletion) and a revision of information are easy, a problem for integrity and the originality of an e-Record is caused. Existing system show the following inefficient. For the originality guarantee, an existing e-Documents encryption method accomplishes a encrypted process of a whole document with a symmetric key, if the information revised midway, the whole documents content must accomplish re-scanning and re-encryption process again. To get over such inefficient, this paper maximize efficiency which occurred at the time of partial information revision request by encryption and managing using the link information based on the linkage characteristics of the each page on the registered requested e-Documents, It was able to increase security configuration by minimizing problems on an information exposure through increasing complicated of the key management.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

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.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.

• Food Science and Preservation
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• v.23 no.2
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• pp.166-173
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• 2016

This study aimed to evaluate the commercialized packaging status of tomato and paprika, and to investigate the effect of different packaging materials on the quality of tomato and paprika during storage. Packaging statuses were surveyed at a department store, wholesale market, and supermarket in Seoul, Korea. Materials used for packaging tomato and paprika were cartons, polypropylene (PP), low-density polyethylene (LDPE), polystyrene (PS), and polyvinyl chloride (PVC). Tomato and paprika were packaged by using corrugated boxes, Styrofoam trays, PP film, and PVC film. The weight loss and hardness of non-packaged tomato and paprika were significantly different after 48 hr to the initial values (p<0.05). Box-packaged tomatoes had the lowest pH values and showed significantly higher soluble solid contents (p<0.05). However, there were no significant differences in among other packaging materials. For paprika, the ${\Delta}E$ values of PVC wrapping were higher than those of other packagings. Hence, the results demonstrated that a corrugated box with PP film and PP film bags with four holes plus wire-tying were most able to maintain the overall qualities of tomato and paprika, respectively, during storage.