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

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.691-700
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• 2009

An event means a flow which has a time attribute such as the a symptom of patients, an interval event has the time period between the start-time-point and the end-time-point. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from interval event such as patient histories and purchase histories. In this paper, we suggest a method of temporal data mining that finds association rules of event causal relationships and predicts an occurrence of effect event based on discovered rules. Our method can predict the occurrence of an event by summarizing an interval event using the time attribute of an event and finding the causal relationship of event. As a result of simulation, this method can discover better knowledge than others by considering a lot of supports of an event and finding the significant rare relation on interval events which means an essential cause of an event, regardless of an occurrence support of an event in comparison with conventional data mining techniques.

• Journal of KIISE
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• v.44 no.4
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• pp.344-351
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• 2017

An attribute based signature system is a cryptographic system where users produce signatures based on some predicate of attributes, using keys issued by one or more attribute authorities. If a private key is leaked during signature generation, the signature can be forged. Therefore, signing operation computations should be performed using secure hardware, which is called tamper resistant hardware in this paper. However, since tamper resistant hardware does not provide high performance, it cannot perform many operations requiring attribute based signatures in a short time frame. This paper proposes a new attribute based signature system using high performance general hardware and low performance tamper resistant hardware. The proposed signature scheme consists of two signature schemes within a existing attribute based signature scheme and a digital signature scheme. In the proposed scheme, although the attribute based signature is performed in insecure environments, the digital signature scheme using tamper resistant hardware guarantees the security of the signature scheme. The proposed scheme improves the performance by 11 times compared to the traditional attribute based signature scheme on a system using only tamper resistant hardware.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.235-241
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• 2015

Nowadays, research and industry on the internet of things is rapidly developing. Bug fixed field of the Software development related internet of things is a very important things. In this study, we analyze the properties that can affect what the bug fix-time by analyzing the time required to fix a bug associated with the Internet of Things. Using the k-NN classification method based on the attribute information to be classified as bug reports. Extracts a bug report based on the results of a similar property. Bug fixed by calculating the time of a similar bug report predicts the fix-time for new bugs. Depending on the prediction of the properties that affect the bug correction time, the properties of os, component, reporter, and assignee showed the best prediction accuracy.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.217-227
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• 2008

The purpose of this research is the estimation of a transfer ratio according to transfer time reflecting passenger attributes such as sex, age, income, job, the car ownership, and other variables with the assumption that a transfer ratio would be different depending on each passenger attribute. This research tested transfer time through a question-survey, estimated transfer time according to the passenger attributes using a data sample, and presented some applicable models about marginal transfer time for the case of the determination of transfer ratios according to transfer time. In this research the sample which had been surveyed for passengers walking to access a transfer station was used to test and estimate and the question was present walking time to the transfer time and the marginal transfer walking time.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.235-242
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• 2015

Social network technology has been increasing interest in the big data service and development. However, the data stored in the distributed server and not on the central server technology is easy enough to find and extract. In this paper, we propose a big data management techniques to minimize the processing time of information you want from the content server and the management server that provides big data services. The proposed method is to link the in-group data, classified data and groups according to the type, feature, characteristic of big data and the attribute information applied to a hash chain. Further, the data generated to extract the stored data in the distributed server to record time for improving the data index information processing speed of the data classification of the multi-attribute information imparted to the data. As experimental result, The average seek time of the data through the number of cluster groups was increased an average of 14.6% and the data processing time through the number of keywords was reduced an average of 13%.

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.483-503
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• 1997

One important question that arises when dealing with temporal databases in context of object-oriented systems is the method that associates time with attributes relationship semantics. Results of previous work about attribute versioning, particularity extending flat(First Normal Form: FNF) or nested(Non-First Normal Form: NFNF) relational models. are not applicable to temporal object-oriented databases. This is because object-oriented models provide more powerful constructs than traditional models for structuring complex objects. Therefore, this paper presents an formal approach for incorporating temporal extension to object-oriented databases. Our goal in this paper is to study temporal object-oriented database representation according to generalization, aggregation and association among objects. We define tile concepts of attribute versioning in temporal object-oriented model, and we concentrate on the representation of temporal relationship among objects. Another contribution of this paper is to specify time constraints on relationship semantics and analyze our model based on representation criteria. By means of formalizing tile temporal object oriented model, this paper can not only provide tile robust operating functions that design algebraic operators, but also entrance the reuse of modules.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.113-115
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• 2015

In this paper, we proposed an algorithm based on movement prediction using rate of change of the attribute information of nodes what is called WRC(Weighted Rate Control) in delay tolerant networks(DTNs). Existing DTN routing algorithms based on movement prediction communicate by selecting relay nodes increasing connectivity with destination node. Thus, because the mobile nodes are in flux, the prediction algorithms that do not reflect the newest attribute information of node decrease reliability. In this paper, proposed algorithm approximate speed and direction of attribute information of node and analysis rate of change of attribute information of node. Then, it predict movement path of node using proposed weight. As the result, proposed algorithm show that network overhead and transmission delay time decreased by predicting movement path of node.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.133-138
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• 2006

Learning with Attribute Value Taxonomies (AVT) has shown that it is possible to construct accurate, compact and robust classifiers from a partially missing dataset (dataset that contains attribute values specified with different level of precision). Yet, in many cases AVTs are generated from experts or people with specialized knowledge in their domain. Unfortunately these user-provided AVTs can be time-consuming to construct and misguided during the AVT building process. Moreover experts are occasionally unavailable to provide an AVT for a particular domain. Against these backgrounds, this paper introduces an AVT generating method called GA-AVT-Learner, which finds a near optimal AVT with a given training dataset using a genetic algorithm. This paper conducted experiments generating AVTs through GA-AVT-Learner with a variety of real world datasets. We compared these AVTs with other types of AVTs such as HAC-AVTs and user-provided AVTs. Through the experiments we have proved that GA-AVT-Learner provides AVTs that yield more accurate and compact classifiers and improve performance in learning missing data.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1684-1690
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• 2004

This paper presents a method for classifier selection that uses distribution information of the training samples in a small region surrounding a sample. The conventional DCS-LA(Dynamic Classifier Selection - Local Accuracy) selects a classifier dynamically by comparing the local accuracy of each classifier at the test time, which inevitably requires long classification time. On the other hand, in the proposed approach, the best classifier in a local region is stored in the FSA(Feature Space Attribute) table during the training time, and the test is done by just referring to the table. Therefore, this approach enables fast classification because classification is not needed during test. Two feature space attributes are used entropy and density of k training samples around each sample. Each sample in the feature space is mapped into a point in the attribute space made by two attributes. The attribute space is divided into regular rectangular cells in which the local accuracy of each classifier is appended. The cells with associated local accuracy comprise the FSA table. During test, when a test sample is applied, the cell to which the test sample belongs is determined first by calculating the two attributes, and then, the most accurate classifier is chosen from the FSA table. To show the effectiveness of the proposed algorithm, it is compared with the conventional DCS -LA using the Elena database. The experiments show that the accuracy of the proposed algorithm is almost same as DCS-LA, but the classification time is about four times faster than that.