• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.13-21
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• 2010

One of the most distinguished features of the DHP association rules mining algorithm is that it counts the support of hash key combinations composed of k items at phase k-1, and uses the counted support for pruning candidate large itemsets to improve performance. At this time, it is desirable for each hash key combination to have a separate count variable, where it is impossible to allocate the variables owing to memory shortage. So, the algorithm uses a direct hashing mechanism in which several hash key combinations conflict and are counted in a same hash bucket. But the direct hashing mechanism is not efficient because the distribution of hash key combinations is unvalanced by the characteristics sourced from the mining process. This paper proposes a mapped perfect hashing function which maps the region of hash key combinations into a continuous integer space for phase 3 and maximizes the efficiency of direct hashing mechanism. The results of a performance test experimented on 42 test data sets shows that the average performance improvement of the proposed hashing mechanism is 7.3% compared to the existing method, and the highest performance improvement is 16.9%. Also, it shows that the proposed method is more efficient in case the length of transactions or large itemsets are long or the number of total items is large.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.86-101
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• 2006

The bandwidth of channel and the power of the mobile devices are limited on a wireless environment. In this case, data broadcast has become an excellent technique for efficient data dissemination. A significant amount of researches have been done on generating an efficient broadcast program of a set of data items with different access frequencies over multiple wireless broadcast channels as well as single wireless broadcast channel. In this paper, an efficient data allocation method over multiple wireless broadcasting channels is explored. In the traditional approaches, a set of data items are partitioned into a number of channel based on their access probabilities. However, these approaches ignore a variation of access probabilities of data items allocated in each channel. In practice, it is difficult to have many broadcast channels and thus each channel need to broadcast many data items. Therefore, if a set of data items broadcast in each channel have different repetition frequencies based on their access frequencies, it will give much better performance than the traditional approaches. In this paper, we propose an adaptive data allocation technique based on data access probabilities over multiple broadcast channels. Our proposed technique allows the adaptation of repetition frequency of each data item within each channel by taking its access probabilities into at count.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.127-138
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• 2021

The aim of this study is to demonstrate interaction as a describable field and tries to understand interaction from the perspective of attributes, thus building a theoretical to help interactive designer understand this field by common rule, rather than waste huge time and labor cost on iteration. Since the concept of interaction language has been brought out in 2000, there are varies of related academical studies, but all with defect such as proposed theoretical models are built on a non-uniform scale, or the analyzing perspective are mainly based on researcher's personal experience and being too unobjective. The value of this study is the clustered resource of research which mainly based on academical review. It collected 21 papers researched on interaction paradigm or interaction attributes published since 2000, extracting 19 interaction attribute models which contains 174 interaction attributes. Furthermore, these 174 attributes were re-clustered based on a more unified standard scale, and the two theoretical models summarized from it are respectively focuses on interaction control and interaction experience, both of which covered 6 independent attributes. The propose of this theoretical models and the analyzation of the cluster static will contribute on further revealing of the importance of interaction attribute, or the attention interaction attribute has been paid on. Also, in this regard, the interactive designer could reasonably allocate their energy during design process, and the future potential on various direction of interaction design could be discussed.

• Journal of Platform Technology
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• v.11 no.5
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• pp.59-71
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• 2023

This paper proposes a method of estimating the illumination chromaticity of a scene in which an image is taken. Storring and Bianco proposed a method of estimating illuminant chromaticity using skin color. Storring et al. used skin color distribution characteristics and black body locus, but there is a problem that the link between the locus and CIE-xy data is reduced. Bianco et al. estimated the illuminant chromaticity by comparing the skin color distribution in standard lighting with the skin color distribution in the input image. This method is difficult to measure and secure as much skin color as possible in various illumination. The proposed method can estimate the illuminant chromaticity for any input image by analyzing the relationship between the skin color information and the illuminant chromaticity. The estimation method is divided into an analysis stage and a test stage, and the data set was classified into an analysis group and a test group and used. Skin chromaticity is calculated by obtaining skin color areas from all input images of the analysis group, respectively. A mapping is obtained by analyzing the correlation between the average set of skin chromaticity and the reference illuminant chromaticity set. The calculated mapping is applied to all input images of the analysis group to estimate the illuminant chromaticity, calculate the error with the reference illuminant chromaticity, and repeat the above process until there is no change in the error to obtain a stable mapping. The obtained mapping is applied to the test group images similar to the analysis stage to estimate the illuminant chromaticity. Since there is no independent data set containing skin area and illuminant reference information, the experimental data set was made using some of the images of the Intel TAU data set. Compared to Finlayson, a similar theory-based existing method, it showed performance improvement of more than 40%, Zhang 11%, and Kim 16%.

• Management & Information Systems Review
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• v.36 no.1
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• pp.1-19
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• 2017

One of the methods of securing the reliability of accounting information is maintaining high audit quality. The first step of improving audit quality is lowering audit engagement risks. Thus, this study analyzed the relationship between the characteristics of accounting firms and audit engagement risks based on the Bayesian Network. For this, Markov Blanket, the minimum explanatory variable set, which affects audit engagement risks, was presented, and based on the drawn causal relationship, sensitivity analysis was conducted to verify the characteristics of accounting firms, which affect audit engagement risks. The existing preceding research that used multiple regression analysis presumes the linearity between explanatory variables and dependent variables, so there was a limit in drawing the relationship between explanatory variables. Therefore, this study figured out the interdependence between variables using the General Bayesian Network and examined the impact that each variable has finally on audit engagement risks that affects the audit quality. The results of this study would greatly contribute to improving the efficiency of the supervisory task by allowing a supervisory institution to identify an accounting firms that does not manage audit engagement risks properly and to improve the supervision of the accounting firms in advance. In addition, this study will be used as a reference when a supervisory institution would improve the system related to audit quality by presenting the characteristics of accounting firms related to the audit quality.

• Journal of the Korean Geographical Society
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• v.30 no.4
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• pp.389-410
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• 1995

Large metropolitan areas in the US are distinguished by highly fragmented fiscally independent suburban municipalities and special districts. The suburban fragmentation implies the congregation of similar socio-economic groups escaped from central cities and the disparity of geographical resources among local government jurisdictions. This study examines St.Louis County suburbs as a case study for the implictions of suburban governmental fragmentation and the fiscal disparities across local governmental boundaries by analyzing their relationship with public service provision using police and public school services as examples. The distribution of fiscal resources across political boundaries, the processes which created them, and public service inequalities reproduced from the disparity of fiscal resources reveal the causes and consequences of suburban governmental fragmentation. The central part of suburban fragmentation is the segregation of public goods consumption reproduced from the disparity of local fiscal resources, and it acts as a basic geographical segregating force in the suburban spatial organization.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.362-371
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• 2005

Similarity indexing and searching are well known to be difficult in high-dimensional applications such as multimedia databases. Especially, they become more difficult when multiple features have to be indexed together. In this paper, we propose a novel indexing method called the GB-index that is designed to efficiently handle complex similarity queries as well as relevance feedback in high-dimensional image databases. In order to provide the flexibility in controlling multiple features and query objects, the GB-index treats each dimension independently The efficiency of the GB-index is realized by specialized bitmap indexing that represents all objects in a database as a set of bitmaps. Main contributions of the GB-index are three-fold: (1) It provides a novel way to index high-dimensional data; (2) It efficiently handles complex similarity queries; and (3) Disjunctive queries driven by relevance feedback are efficiently treated. Empirical results demonstrate that the GB-index achieves great speedups over the sequential scan and the VA-file.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.2
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• pp.191-204
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• 2003

In the home gateway environment, several characteristics for the user authentication mechanism should be reflected separately from the existing distributed service environment. First, the platform of a home gateway is a component based system that its services are installed dynamically. Second, the convenience that user can use several services by authentication of once should be offered. Finally, the system resources of a home gateway are restricted. However, a user authentication mechanism that reflected these characteristics is not shown at the user admin service specification of the OSGi service platform.(OSGi is the representative standardization organization of hone gateway.) Also, there is no existing authentication protocol that satisfies these qualities at the same time. In this paper, we propose a new user authentication mechanism considering those characteristics for the home gateway environment. We also design and implement an independent authentication service bundle based on the OSGi service platform so that it can perform user authentication operations for each bundle service. We supplement and extend the Kerberos Protocol that can apply.

• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.678-689
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• 2000

This paper describes an automatic summarization approach that constructs a summary by extracting sentences that are likely to represent the main theme of a document. As a way of selecting summary sentences, the system uses a model that takes into account lexical and statistical information obtained from a document corpus. As such, the system consists of two parts: the training part and the summarization part. The former processes sentences that have been manually tagged for summary sentences and extracts necessary statistical information of various kinds, and the latter uses the information to calculate the likelihood that a given sentence is to be included in the summary. There are at least three unique aspects of this research. First of all, the system uses a text component identification model to categorize sentences into one of the text components. This allows us to eliminate parts of text that are not likely to contain summary sentences. Second, although our statistically-based model stems from an existing one developed for English texts, it applies the framework to individual features separately and computes the final score for each sentence by combining the pieces of evidence using the Dempster-Shafer combination rule. Third, not only were new features introduced but also all the features were tested for their effectiveness in the summarization framework.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.253-262
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• 2000

This paper proposes a new RBF neural network that determines the number and the center of hidden neurons based on the adaptive feature extraction for the input data. The principal component analysis is applied for extracting adaptively the features by reducing the dimension of the given input data. It can simultaneously achieve a superior property of both the principal component analysis by mapping input data into set of statistically independent features and the RBF neural networks. The proposed neural networks has been applied to classify the 200 breast cancer databases by 2-class. The simulation results shows that the proposed neural networks has better performances of the learning time and the classification for test data, in comparison with those using the k-means clustering algorithm. And it is affected less than the k-means clustering algorithm by the initial weight setting and the scope of the smoothing factor.