• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1065-1072
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• 2017

As smart devices and social network services (SNSs) become increasingly pervasive, individuals produce large amounts of data in real time. Accordingly, studies on unstructured data analysis are actively being conducted to solve the resultant problem of information overload and to facilitate effective data processing. Many such studies are conducted for filtering inappropriate information. In this paper, a feature-weighting method considering SNS-message features is proposed for the classification of short text messages generated on SNSs, using Markov logic networks for category inference. The performance of the proposed method is verified through a comparison with an existing frequency-based classification methods.

• Journal of applied mathematics & informatics
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• v.12 no.1_2
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• pp.395-408
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• 2003

Common Channel Signaling(CCS) networks need special controls to avoid overload of signaling networks and degradation of call process-ing rate, since they play an important role of controlling communication transfer networks. Congestion control and flow control mechanisms are well described in ITU-T recommendation on Signaling System No.7(SS7). For the practical provisions, however, we need an analysis on the relation among service objects, system requirements and implementation of congestion control algorithms. SS7 provides several options for controlling link congestion in CCS networks. In this paper we give a general queueing model of congestion control algorithm which covers both the international and national options. From the queuing model, we obtain the performance parameters such as throughput, message loss rate and mean delay for the international option. To show the performance of the algorithm, some numerical results are also given.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.371-373
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• 2001

In this paper, we present 2-step load cycle for daily load curve of up to and including 100kVA distribution transformer in domestic. Daily load patterns are classified by two methods dependent upon possession information. In case we possess daily load profiles make use of K-mean algorithm and in case we have not daily load profiles, make use of customer information of KEPCO. As the parameters of the load pattern classification, we use are daily load profiles and customer information of each distribution transformers. Data management system is used for NT oracle. We can present peak load magnitude, initial load magnitude and peak load duration for daily load patterns by 2-step load cycle for daily load curve of up to and including 100kVA distribution transformer in domestic. We think that this paper contributes to enhancing the distribution transformer overload criterion.

• Asia pacific journal of information systems
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• v.13 no.3
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• pp.243-259
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• 2003

Many R&D organizations have interests on knowledge management system(KMS) to organize and utilize their knowledge resources. Currently, for research collaboration and knowledge management R&D organizations use either a specialized knowledge management system or a set of general application systems such as basic messaging system and document management system. The objectives of this paper are to identify important functionalities of knowledge management systems based on team characteristics and knowledge process of research teams in research organizations and to provide implications to design and implement knowledge management system for R&D teams. Survey results show that research teams perceive communication, collaboration and connection functionalities are important when their team sizes are large or they are distributed. During knowledge capture process, they need personalization of knowledge to reduce information overload.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.342-351
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• 2002

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.315-325
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• 1999

Indexing techniques for distributed resources have much effect on an information service system based on distributed environment like digital library. There is a centralized indexing technique, a distributed technique, and a mixed technique for distributed indexing techniques. In this paper, we propose new distributed indexing technique using EIF(extended Inverted File) structure that mix the centralized technique and t도 distributed technique. And we propose management techniques using EIF structure and retrieval technique using EIF structure. This distributed indexing technique proposed is able to fast process retrieval request and reduce network overload and select servers relevant to query terms. This paper investigated performance of a proposed distributed indexing technique.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.209-219
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• 2008

Recently the number of Internet firms providing useful information like weather forecast data is growing. However most of such information is not prepared in accordance with customers' demand, resulting in relatively low customer satisfaction. To upgrade the service quality, it is recommended to devise a system for customers to get involved in the process of service production, which normally requires a huge investment on supporting computer systems like clusters. In this paper, as a way to cut down the budget for computer systems but to improve the performance, we extend the HPC cluster system to include other Internet servers working independently on the same LAN, to make use of their idle times. We also deal with some issues resulting from the extension, like the security problem and a possible deadlock caused by overload on some non-dedicated nodes. At the end, we apply the technique in the solution of some 2D grid problem.

• Journal of Smart Tourism
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• v.3 no.3
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• pp.5-15
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• 2023

Given the increasing information overload among users of online review websites, understanding the manner in which cognitive costs are reduced and efficient information is made reliable has become increasingly important. This study targets a unique consumer review design and explores how reviews from an external peer-to-peer site embedded in an online travel agency (OTA) website influence subsequent evaluation behaviors. The empirical results indicate that (1) embedded external reviews with a high average valence tend to strengthen the influence of the positive evaluation ratio while diminishing the effect of the review volume, and (2) embedded external reviews with a large variance strengthen the positive effect of the review volume while weakening the effect of the positive evaluation ratio on subsequent positive evaluations. The findings provide practical insights for consumers and online platforms.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.372-399
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• 2024

Online reviews play a significant role in consumer purchase decisions on e-commerce platforms. To address information overload in the context of online reviews, factors that drive review helpfulness have received considerable attention from scholars and practitioners. The purpose of this study is to explore the differential effects of discrete emotions (anger, disgust, fear, joy, sadness, and surprise) on perceived review helpfulness, drawing on cognitive appraisal theory of emotion and expectation-confirmation theory. Emotions embedded in 56,157 hotel reviews collected from TripAdvisor.com were extracted based on a transfer learning model to measure emotion variables as an alternative to dictionary-based methods adopted in previous research. We found that anger and fear have positive impacts on review helpfulness, while disgust and joy exert negative impacts. Moreover, hotel star-classification significantly moderates the relationships between several emotions (disgust, fear, and joy) and perceived review helpfulness. Our results extend the understanding of review assessment and have managerial implications for hotel managers and e-commerce vendors.