• Asia pacific journal of information systems
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• v.25 no.4
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• pp.686-711
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• 2015

The performance of a novel medical question-answering engine called CliniCluster and existing search engines, such as CQA-1.0, Google, and Google Scholar, was evaluated using known-item searching. Known-item searching is a document that has been critically appraised to be highly relevant to a therapy question. Results show that, using CliniCluster, known-items were retrieved on average at rank 2 ($MRR@10{\approx}0.50$), and most of the known-items could be identified from the top-10 document lists. In response to ill-defined questions, the known-items were ranked lower by CliniCluster and CQA-1.0, whereas for Google and Google Scholar, significant difference in ranking was not found between well- and ill-defined questions. Less than 40% of the known-items could be identified from the top-10 documents retrieved by CQA-1.0, Google, and Google Scholar. An analysis of the top-ranked documents by strength of evidence revealed that CliniCluster outperformed other search engines by providing a higher number of recent publications with the highest study design. In conclusion, the overall results support the use of CliniCluster in answering therapy questions by ranking highly relevant documents in the top positions of the search results.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.635-642
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• 2003

The radical development of recent network technology provides the basic foundation which can establish a high speed and cheap cluster system. It is a general trend that conventional cluster systems are built as the system over a fixed level based on stabilized and high speed local networks. A multi-distributed web cluster group is a web cluster model which can obtain high performance, high efficiency and high availability through mutual cooperative works between effective job division and system nodes through parallel performance of a given work and shared memory of SC-Server with low price and low speed system nodes on networks. For this, multi-distributed web cluster group builds a sub-cluster group bound with single imaginary networks of multiple system nodes and uses the web distributed shared memory of system nodes for the effective data transmission within sub-cluster groups. Since the presented model uses a load balancing and parallel computing method of large-scale work required from users, it can maximize the processing efficiency.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.161-174
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• 2007

In this paper, we propose a high performance database cluster system called HyperDB to process OLAP queries efficiently. HyperDB is a virtual database system running on top of internet-connected PCs; the PCs are used for their own purpose at ordinary times, but they are able to participate in the database cluster system at non-office hours. We propose fully logical replication technique and optimal parallel intra-query routing technique for extensibility and performance. Experiment for TPC-R benchmark shows significant performance upgrade compared with conventional approaches.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1040-1046
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• 2009

Thanks to the recent development of computing technology, the various forms of high-performance computers are available. Among them, the parallel-clustering CPU machines are realized for the high performance computing. These supercomputers (cluster computers) can be applied to various industries due to the advantages of lower price. Especially in the field of numerical flow simulation, use of supercomputers can produce results quickly, and various engineering problems can be reviewed effectively case by case. In this paper, an application of supercomputers (cluster computers) were examined for railroad industry of fire flow simulation by using parallel computational method. It make sure that the supercomputers are very useful tools for railroad engineering.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.293-301
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• 2023

With the advancement of artificial intelligence and its various use cases, accessing it through edge computing environments is gaining traction. However, due to the nature of edge computing environments, efficient management and optimization of clusters distributed in different geographical locations is considered a major challenge. To address these issues, this paper proposes a centralization and automation tool called KubEVC-Agent based on Kubernetes. KubEVC-Agent centralizes the deployment, operation, and management of edge clusters and presents a use case of the data transformation for optimizing intra-cluster communication. This paper describes the components of KubEVC-Agent, its working principle, and experimental results to verify its effectiveness.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.44-53
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• 2011

This study designed and implemented a web-based computing environment for geovisual analytics using HTML5 canvas. The computing environment supports visualization tools and user's interaction. The visualization tools are cluster map, animated map, temporal parallel coordinate plot, and temporal heat map chart. Users can explore the temporal changes of cluster using multiple view and brushing technique. The computing environment that works well across browsers is used in the computing environment with multiple devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.103-110
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• 2016

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.1-10
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• 2009

Energy efficient operations are essential to increase the life time of wireless sensor network. A cluster-based protocol is the most common approach to preserve energy during a data aggregation. This paper deals with an energy awareness and autonomous clustering method based on time delay. This method consists of three stages. In the first phase, Candidate Cluster Headers(CCHs) are selected based on a time delay which reflects the remaining energy of a node, with considering coverage efficiency of a cluster. Then, time delay is again applied to declare Cluster Headers(CHs) out of the CCHs. In the last phase, the issue on an orphan node which is not included into a cluster is resolved. The simulation results show that the proposed method increases the life time of the network around triple times longer than LEACH(Low Energy Adaptive Cluster Hierarchy). Moreover, the cluster header frequency is less diverse, and the energy on cluster heads is less spent.

• Journal of KIISE:Databases
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• v.32 no.2
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• pp.142-150
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• 2005

A shared disks (SD) cluster couples multiple computing nodes, and every node shares a common database at the disk level. A great deal of research indicates that the SD cluster is suitable to high performance transaction processing, but the aggregation of SD cluster with real-time processing has not been investigated at all. A real-time transaction has not only ACID properties of traditional transactions but also time constraints. By adopting cluster technology, the real-time services will be highly available and can exploit inter-node parallelism. In this paper, we first develop an experiment model of an SD-based real-time database system (SD-RTDBS). Then we investigate the feasibility of real-time transaction processing in the SD cluster using the experiment model. We also evaluate the cross effect of real-time transaction processing algorithms and SD cluster algorithms under a wide variety of database workloads.