• The KIPS Transactions:PartD
• /
• v.15D no.4
• /
• pp.451-462
• /
• 2008

Recently, with the advent of applications using locations of moving objects, it becomes crucial to develop efficient index schemes for spatio-temporal databases. The $TPR^*$-tree is most popularly accepted as an index structure for processing future-time queries. In the $TPR^*$-tree, the future locations of moving objects are predicted based on the CBR(Conservative Bounding Rectangle). Since the areas predicted from CBRs tend to grow rapidly over time, CBRs thus enlarged lead to serious performance degradation in query processing. Against the problem, we propose a new method to adjust CBRs to be tight, thereby improving the performance of query processing. Our method examines whether the adjustment of a CBR is necessary when accessing a leaf node for processing a user query. Thus, it does not incur extra disk I/Os in this examination. Also, in order to make a correct decision, we devise a cost model that considers both the I/O overhead for the CBR adjustment and the performance gain in the future-time owing to the CBR adjustment. With the cost model, we can prevent unusual expansions of BRs even when updates on nodes are infrequent and also avoid unnecessary execution of the CBR adjustment. For performance evaluation, we conducted a variety of experiments. The results show that our method improves the performance of the original $TPR^*$-tree significantly.

• Knowledge Management Research
• /
• v.3 no.1
• /
• pp.93-106
• /
• 2002

Knowledge management is a recent area in business administration that deals with how to leverage knowledge as a key asset and resource in modern organizations. Also, Knowledge systems are the single most important industrial and commercial offspring of the discipline called artificial intelligence. A Case Based Reasoning(CBR) system solves new problems by recalling adapting previous solutions. This paper presents the results of a recent empirical study. Furthermore this study proposes a CBR Methodology designed to manage knowledge of Hana company under e-business.

• Journal of Korean Physical Therapy Science
• /
• v.4 no.2
• /
• pp.461-466
• /
• 1997

The physical therapist's participation in community based rehabilitation(CBR) is necessary, in a variety of ways, to ensure the disabled quality service. Although CBR needs the Physical Therapist's help, participation is limited due to unstable CBR policy, and because there is a lack of financial support, skilled Physical Therapist's are usually not hired. Physical Therapist's themselves do not seem to completely understand this. The experts active participation is needed for effective rehabilitation service. Therefore, the trained Physical Therspist's participation is absoutely necessary is CBR policy if it is even to provide completely effective service.

• International Journal of Highway Engineering
• /
• v.8 no.4 s.30
• /
• pp.75-85
• /
• 2006

Plate Bearing Test(PBT) and California Bearing Ratio Test(CBR) usually have been used to evaluate the bearing capacity of sub-layer in pavement system. However, these tests have shortcomings for which man powers and time are spent greatly. Many researchers proposed a simple Dynamic Cone Penetrometer Test(DCP) to evaluate the bearing capacity of sub-layers in pavement system. This study performed several field bearing capacity tests(DCP, PBT, CBR, FWD) to evaluate field performance of DCP on sub-base and subgrade at four test sections simultaneously. The results showed that DCPI, $M_{FWD}$, and $PBT_K_{30}$ are highly correlated, but CBR and other test are not. This study proposed the following regression models between FWD, DCP, and PBT: $$M_{FWD}=993.10\Big(\frac{1}{DCPI}\Big)+33.95\;R^2=0.77$$ $$M_{FWD}=3.7533K_{30}+23.085\;R^2=0.69$$

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.12 no.2
• /
• pp.149-155
• /
• 2008

The road construction, as part of effort to ease the worsening traffic, has been underway throughout the nation, while the existing road has been increasingly losing its load carrying capacity due to such factors as heavy traffic and weathering. In the case of site, the soil type, plasticity index, and specific gravity were SC, 12.2%, and 2.66, respectively. The maximum dry density, optimum moisture content and modified CBR were $1.895g/cm^3$ (Modified Compaction D), 13.6%, and 16.2%, respectively. A correlation of coefficient expressed good interrelationship by 0.90 between the CBR estimated from a dynamic penetration index of dynamic cone penetrometer test and a deformation modulus converted from a dynamic deflection modulus obtained from a portable FWD test.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.29-45
• /
• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Management & Information Systems Review
• /
• v.2
• /
• pp.169-193
• /
• 1998

Case- based reasoning(CBR) offers a new approach for developing knowledge based systems. Unlike the rule-based paradigm, in which domain knowledge is encoded in the form of production rules, in the case-based approach the problem solving experience of the domain expert is encoded in the form of cases stored in a casebase(CB). CBR allows a reasoner (1) to propose solutions in domains that are not completely understood by the reasoner, (2) to evaluate solutions when no algorithmic method is available for evaluation, and (3) to interprete open-ended and ill-defined concepts. CBR also helps reasoner (4) take actions to avoid repeating past mistakes, and (5) focus its reasoning on important parts of a problem. Owing to the above advantages, CBR has successfully been applied to many kinds of problems such as design, planning, diagnosis and instruction. In this paper, I propose case-based DSS(CBDSS). CBDSS is an intelligent DSS using CBR technique. CBDSS consists of interface, case-based reasoner, maintainer, casebase management system, domain dependent CB, domain independent CB, and so on.

• The Journal of Information Systems
• /
• v.18 no.3
• /
• pp.375-399
• /
• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.05a
• /
• pp.774-779
• /
• 2001

If rotating machinery have a fault, we can detect it using vibration or noise signals. However some maintenance engineers who doesn't have expert knowledge, needs the help of vibration experts for diagnosing rotating machinery. But qualified experts are rare, therefore we have been developed the case based reasoning (CBR) system which is able to manipulate the past experiences of vibration diagnosis experts. In the CBR system, the maintenance engineers can retrieve too information from previous cases which are most similar to new problem and they can solve new problem using solutions from the previous cases. In this paper, we propose a new method which is the case retrieval of CBR system using Petri net and we also applied it to diagnosis for electric motors as a practical problem.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2003.05a
• /
• pp.474-483
• /
• 2003

A number of studies for corporate bond rating classification problems have demonstrated that artificial intelligence approaches such as Case-based reasoning (CBR) can be alternative methodologies to statistical techniques. CBR is a problem solving technique in that the case specific knowledge of past experience is utilized to find a most similar solution to the new problems. To build a successful CBR system to deal with human information processing, the representation of knowledge of each attribute is an important key factor We propose a hybrid approach of using fuzzy sets that describe the approximate phenomena of the real world because it handles inexact knowledge represented by common linguistic terms in a similar way as human reasoning compared to the other existing techniques. Integration of fuzzy sets with CBR is important to develop effective methods for dealing with vague and incomplete knowledge to statistical represent using membership value of fuzzy sets in CBR.