• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.632-639
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• 2011

Searching distributed resource efficiently is very important in distributed computing, cloud computing environment. Distributed resource searching may have system overheads and take much time in proportion to the searching number, because distributed resource searching has to circuit many peers for searching information. The open-source community project JXTA defines an open set of standard protocols for ad hoc, pervasive, peer-to-peer computing as a common platform for developing a wide variety of decentralized network applications. In this paper, we proposed peer search algorithm based on JXTA-Sim. original JXTA peer searching algorithm selected a loosely-consistent DHT. Our Lookup algorithm decreases message number of WALK_LOOKUP and reduce the network system overload, and we make a result of same performance both original algorithm and our proposed algorithm.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.25-33
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• 2018

This paper proposes a cloud workflow model theoretically supported by the information control net modeling methodology as a cloud workflow modeling methodology that is mandatory in implementing realtime enterprise workflow management systems running with cloud computing environments. The eventual goal of the cloud workflow model proposed in this paper is to support those cloud workflow architectures reflecting the types of cloud deployment models such as private, community, public, and hybrid cloud deployment models. Moreover, the proposed model is a mathematical graph model that is extended from the information control net modeling methodology used in conventional enterprise workflow modeling, and it aims to theoretically couple this methodology with the cloud deployment models. Finally, this paper tries to verify the feasibility of the proposed model by building a possible cloud workflow architecture and its cloud workflow services on a realtime enterpeise cloud workflow management system.

• Genomics & Informatics
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• v.19 no.4
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• pp.49.1-49.11
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• 2021

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1095-1100
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• 2009

The interests and studies on co-creation activity in metaverse space, or MMO communication applications, increase sharply. The study performed the experiments on the participatory virtual space design that has dedicated to the common goal of creativity, and provides the environments to enhance ideas, technologies, and artistic artifacts sharing and communication. The participants were recruited from a community of artists and musicians. They have been actively participated the design process as photographers, painters, media artists, sound artists, and collectors. During the period, we have preceded 5 consecutive experiments, which were led by 5 independent artists, tested the value of co-creation spaces.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.75-82
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• 2012

In this paper, an improved community detection algorithm based on the modularity is proposed. The existing algorithm does not consider the information that the nodes have in checking the possible modularity increase, hence the computation may be inefficient. The proposed algorithm computes the node degree (weight) and sorts them in non-increasing order. By checking the possible modularity value increase for the nodes in the nonincreasing order of node weights, the algorithm finds the final solution more quickly than the existing algorithm does. Through the computational experiments, it is shown that the proposed algorithm finds a modularity as good as the existing algorithm obtains.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.197-209
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• 2011

The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attacks as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. In addition, we also investigate the impact of the proposed methods on community detection, a very popular application in the graph mining field. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.489-498
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• 2012

This paper consists of two parts: In the first part, we describe our work to build hierarchical knowledge base of digital library patron's research interests and learning topics in various scholarly areas through analyzing well classified Electronic Theses and Dissertations (ETDs) of NDLTD Union catalog. Journal articles from ACM Transactions and conference web sites of computing areas also are added in the analysis to specialize computing fields. This hierarchical knowledge base would be a useful tool for many social computing and information service applications, such as personalization, recommender system, text mining, technology opportunity mining, information visualization, and so on. In the second part, we compare four grouping algorithms to select best one for our data mining researches by testing each one with the hierarchical knowledge base we described in the first part. From these two studies, we intent to show traditional verification methods for social community miming researches, based on interviewing and answering questionnaires, which are expensive, slow, and privacy threatening, can be replaced with systematic, consistent, fast, and privacy protecting methods by using our suggested hierarchical knowledge base.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.91-100
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• 2013

In recent years, online brand communities, whereby firms and customers interact freely, are emerging trend, because customers' opinions collected in these communities can help firms to achieve their innovation effectively. In this study, we examined whether customer opinions containing negative emotions have influence on their adoption for organizational innovation. To that end, we firstly classified negative emotions into five categories of detailed negative emotions such as Fear, Anger, Shame, Sadness, and Frustration. Then, we developed a lexicon for each category of negative emotions, using WordNet and SentiWordNet. From 81,543 customer opinions collected from MyStarbucksIdea.com which is Starbucks' brand community, we extracted terms that belong to each lexicon. We conducted an experiment to examine whether the existence, frequency and strength of terms with negative emotions in each category affect the adoption of customer opinions for organizational innovation. In the experiment, we statistically verified that there is a positive relationship between customer ideas containing negative emotions and their adoption for innovation. Especially, Frustration and Sadness out of the five emotions are significantly influential to organizational innovation.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.73-84
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• 2019

Machine learning as a service, the so-called MLaaS, has recently attracted much attention in almost all industries and research groups. The main reason for this is that you do not need network servers, storage, or even data scientists, except for the data itself, to build a productive service model. However, machine learning is often very difficult for most developers, especially in traditional science due to the lack of well-structured big data for scientific data. For experiment or application researchers, the results of an experiment are rarely shared with other researchers, so creating big data in specific research areas is also a big challenge. In this paper, we introduce the KISTI-ML platform, a community-based rapid AI model development for scientific data. It is a place where machine learning beginners use their own data to automatically generate code by providing a user-friendly online development environment. Users can share datasets and their Jupyter interactive notebooks among authorized community members, including know-how such as data preprocessing to extract features, hidden network design, and other engineering techniques.

• Proceedings of the CALSEC Conference
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• 2000.08a
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• pp.207-214
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• 2000

Over the last several decades, the topic of the source selection and evaluation has gained a great attention in the information systems community as an effective tool to acquire information systems in an organization. The source selection and evaluation process is a multiple-criteria decision-making problem associated with several evaluation issues. In this case study, evaluation issues include management, technologies, logistics, and cost. This case study was conducted to compare a new source selection and evaluation process by using the analytic hierarchy process with the traditional approach. This study provides useful insight about how to apply the analytic hierarchy process technique to the traditional approach.