• The KIPS Transactions:PartA
• /
• v.8A no.3
• /
• pp.209-216
• /
• 2001

The effective way to apply incremental attribute grammar to a complex language process is the use of optimized algorithm. In optimized algorithm for incremental attribute grammar, the new input attribute tree should be exactly compared with the previous input attribute tree, in order to determine which subtrees from the old should be used in constructing the new one. In this paper the new optimized algorithm was reconstructed by analyzing the algorithm suggested by Carle and Pollock, and a generation process of new attribute tree d’copy was added. Through the performance evaluation for the suggested matching algorithm, the run time is approximately improved by 19.5%, compared to the result of existing algorithm.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.29 no.4
• /
• pp.34-43
• /
• 2001

Unlike ordinary recreational facilities, an amusement park consists of various entertainment facilities, attractions, food services, souvenir shops and other attribute. The purpose of this study is to survey users´ responses to such attributes and analyze the importance and performance of each attribute, and thereby, ultimately help improve the efficiency of management and operation of the amusement parks. For this purpose, a survey was conducted of Everland users in August, 1999. 420 users were chosen by means of he systematic sampling. All the suers were asked to rate the importance of 14 attributes of Everland at the entrance and all of them were asked to do the same at the exit. As a result, it was found that the attribute rated highest by the users was ´attraction´, followed by ´service´, ´accessibility´ and ´cost´ in that order. On the other hand, it was found that the total average of importance rated for 14 attributes was 3.31, while that of performance was 3.10. As a consequence of analyzing the action grids, it was found that ´appropriateness of the circulation system´ should be improved most urgently. 7 attributes were categorized as ´keeping up good work´, and 6 ones were rated ´low priority´ in terms of improvement. There was no attribute considered to be ´possible overkill´. Meanwhile, as a result of analyzing the difference among groups in order to determine users´ response depending on their demographic and socio-economic variables, it was found that only the ´age´ variable was significant. It is expected that the results that the results of this study would be useful in determining priorities when improving amusement park facilities or their programs.

• Journal of the Korea Society for Simulation
• /
• v.9 no.3
• /
• pp.77-89
• /
• 2000

This paper suggests a multi-attribute dispatching rule for an automated guided vehicle(AGV). The attributes to be considered are the number of queues in outgoing buffers of workstations, distance between an idle AGV and a workstation with a job waiting for the service of vehicle, and the number of queues in input buffers of the destination workstation of a job. The suggested rule is based on the simple additive weighting method using a normalized score for each attribute. A neural network approach is applied to obtain an appropriate weight vector of attributes based on the current status of the manufacturing system. Backpropagation algorithm is used to train the neural network model. The proposed dispatching rules and some single attribute rules are compared and analyzed by simulation technique. A number of simulation runs are executed under different experimental conditions to compare the several performance measures of the suggested rules and some existing single attribute dispatching rules each other.

• Proceedings of the IEEK Conference
• /
• 2003.11b
• /
• pp.207-210
• /
• 2003

In this paper, we explore a new data mining capability that is based on Quantitative Attribute and Time Series. Our solution procedure consists of two steps. First, We derive an algorithm to contain the Quantitative Attribute into a set of candidate item. Second, We redefine the concepts of confidence and support for composite association rules. It is shown that proposed methode is very advantageous and can lead to prominent performance improvement.

• The KIPS Transactions:PartD
• /
• v.14D no.5
• /
• pp.459-470
• /
• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In most DSMS(Data Stream Management System), the selection predicates of continuous queries are grouped or indexed to guarantee these constraints. This paper proposes a new scheme tailed an ASC(Attribute Selection Construct) that collectively evaluates selection predicates containing the same attribute in multiple continuous queries. An ASC contains valuable information, such as attribute usage status, partially pre calculated matching results and selectivity statistics for its multiple selection predicates. The processing order of those ASC's that are corresponding to the attributes of a base data stream can significantly influence the overall performance of multiple query evaluation. Consequently, a method of establishing an efficient evaluation order of multiple ASC's is also proposed. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.12
• /
• pp.259-266
• /
• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of KIISE:Databases
• /
• v.37 no.6
• /
• pp.285-291
• /
• 2010

In this paper, we propose a new paradigm of weighting methods for naive Bayesian learning. We propose more fine-grained weighting methods, called value weighting method, in the context of naive Bayesian learning. While the current weighting methods assign a weight to an attribute, we assign a weight to an attribute value. We develop new methods, using Kullback-Leibler function, for both value weighting and feature weighting in the context of naive Bayesian. The performance of the proposed methods has been compared with the attribute weighting method and general naive bayesian. The proposed method shows better performance in most of the cases.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1999.04a
• /
• pp.184-188
• /
• 1999

The performance of an AGV varies with the applied AGV dispatching rule in the operation of AGVS. This study proposes a multi-attribute AGV dispatching rule. The suggested dispatching rule considers the output queue of a workstation, distance between an idle AGV and a workstation to be served, the input queue of the destination and the remaining job process of a part. This study suggests two types of and the remaining job process of a part. This study suggests two types of multi-attribute dispatching rules. One is an one-stage rule which selects the part to be served considering four attributes simultaneously. The other is a two-stage rule by which a workstation is selected and a part is chosen from the selected workstation. The simulation runs were executed under different experimental conditions to obtain preliminary statistics on the several performance measures.

• Knowledge Management Research
• /
• v.10 no.4
• /
• pp.163-183
• /
• 2009

Numerous studies in the field of buyer-supplier relationship research have focused on the buyer's performance. In contrast, supplier's performance has been paid relatively little attention by researchers, especially the research about the supplier's innovation in the relationship is still in its early stage. In this paper, we examine the relation between the attribute of buyer-supplier relationship and the attribute of supplier's innovation through case research. We define the attribute of buyer-supplier relationship as 'tie strength' (Granovetter, 1973), and the attribute of supplier's innovation as 'exploitation or exploration' (March, 1991). We selected the semiconductor equipment industry of U.S.A, Japan and Korea and firm (JUSUNG Engineering) as cases that examine the relation. We found that a strong tie relationship is positively associated with supplier's exploitation based innovation, and a weak tie relationship is positively related to it's exploration based innovation in this research also. In addition, we could verify reduction of strong tie relationship cause supplier's organizational change.

• Journal of Information Technology Applications and Management
• /
• v.16 no.4
• /
• pp.93-107
• /
• 2009

This study investigated consumer preferences for Internet medical information service in China using the multi-attribute utility theory. The multi-attribute utility theory is a compositional approach for modeling consumer preferences wherein researchers calculate the overall service utility by summing up the evaluation results for each attribute. We found that Chinese Internet medical information users consider the availability of information and quick response to be the most important attributes. Further, they think that the comment feature is less important as compared to other attributes such as costs and updates. In addition, we found that the Internet users having more Internet experience consider these attributes to be more important as compared to the people who are just beginning to surf the Internet. For any successful Internet business, Internet marketers should assess individual-level preference and accordingly organize a fresh campaign. As of now, Internet marketers need estimation methods to predict the market performance of new services in many different business environments. We believe that the multi-attribute utility theory is a useful approach in this regard.