• Journal of Power Electronics
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• v.10 no.6
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• pp.611-620
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• 2010

This paper presents a new architecture, called "Power Electronics Remote Wiring and Measurement Laboratory (PermLAB)", that translates a common gateway interface (CGI) string from a remote web user to a web server connected to a 3-dimension switching matrix board, can be used to switch on and off, and to control a cluster of instruments and components. PermLAB addresses real-time connection, switching, and data acquisition over the Internet instead of using simulated data. A software procedure uses a signature system to identify each instrument and component in a complex system. The Web-server application is developed in HTML, JavaScript and Java, and in C language for the CGI interface, which resides in a controller portion of LabVIEW. The LabVIEW software fully integrates the Web sever, LabVIEW data acquisition boards and controllers, and the 3-dimensional switching matrix board. The paper will analyze a half-wave rectifier (AC - DC converter) circuit connected over the Internet using the PermLAB. PermLAB allows students to obtain real data by real-time wiring of real circuits in the laboratory using a "virtual breadboard" on the Web. The software for the Web-based 3-dimensional system is flexible, portable, can be integrated into many laboratory applications or expanded, and easily accessible worldwide.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.717-730
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• 2004

Recently. many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain WRL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users' interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit proposes or inclinations are classified into the same group. In this paper. we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.651-657
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• 2015

The amount of web traffic has increased as a result of the rapid growth of the use of web-based applications. In order to obtain valuable information from web logs, we need to develop systems that can support interactive, flexible, and efficient ways to analyze and handle large amounts of data. In this paper, we present CERES, a log-based, interactive web analytics system for backbone networks. Since CERES focuses on analyzing web log records generated from backbone networks, it is possible to perform a web analysis from the perspective of a network. CERES is designed for deployment in a server cluster using the Hadoop Distributed File System (HDFS) as the underlying storage. We transform and store web log records from backbone networks into relations and then allow users to use a SQL-like language to analyze web log records in a flexible and interactive manner. In particular, we use the data cube technique to enable the efficient statistical analysis of web log. The system provides users a web-based, multi-modal user interface.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.1-8
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• 2007

As the web document of XML, an exchange language of data in the advanced Internet, is increasing, a target of information retrieval becomes the web documents. Therefore, there we researches on structure, integration and retrieval of XML documents. This paper proposes a clustering method of XML documents based on frequent structures, as a basic research to efficiently process query and retrieval. To do so, first, trees representing XML documents are decomposed and we extract frequent structures from them. Second, we perform clustering considering the weight of large items to adjust cluster creation and cluster cohesion, considering frequent structures as items of transactions. Third, we show the excellence of our method through some experiments which compare which the previous methods.

• International Commerce and Information Review
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• v.9 no.3
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• pp.33-57
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• 2007

This study tests e-business performance difference of the inter group by strategy type through the cluster analysis, after confirming the linear relationship between the e-business strategy type and its performance. According to the result of the study the effect on the e-business performance reveals to differ by the e-business strategy type. That is to say, while the firm's information system performance, e-business realization and the inter group competition dominance are affected positively by the e-process strategy, web application strategy and customer attracting strategy. This confirms that the information system performance, the realization performance and the competition dominance are affected by e - business strategy type. The key factor leading to the success of e-business is the commitment of e-process strategy and web application strategy. For the case of the customer attracting strategy the result shows the importance of the strategy. In the cluster group analysis, the additional analysis, the effect is seen to be more powerful in the firm's information system performance, e - business realization and securing the competition advantage for the case of the multiple strategy than that of single one. Accordingly, the firm needs to adopt the multiple strategy suited for the characteristic of the firm rather than the single strategy.

• Genomics & Informatics
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• v.6 no.3
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• pp.157-159
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• 2008

Multiple sequence alignment (MSA) is the most important step for many of biological sequence analyses, homology search, and protein structural assignments. However, large amount of data make biologists difficult to perform MSA analyses and it requires much computational time to align many sequences. Here, we have developed a simple and fast web alignment tool for aligning, editing, and visualizing large amount of sequence data. We used a cluster server installed ClustalW-MPI using web services and message passing interface (MPI). It also enables users to edit multiple sequence alignments for manual editing and to download the input data and results such as alignments and phylogenetic tree.

• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.225-230
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• 2003

In this paper, we develope algorithms that distribute the load on the heterogeneously clustered web system, The response time based on the concurrent user is examined for the suggested algorithms. Simulation experience shows that the response time using the dynamically weighted methods seems to have a good results compare to that with the fixed weighted methods. And, also the effectiveness of clustered system becomes better as long as the number of concurrent user increases.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.199-208
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• 2008

Tagging represents one of the Web 2.0 technology, and has an appropriate mechanism for the classification of dynamically changing Web informations. This technique is capable of searching the Web informations using the user specified tags, but still it has a limitation of providing only the limited informations to the tags. Therefore, in order to search the related informations easily, we need to extend this technique further to search not only the desired informations through the designated tags and also the related informations. In this paper, we first have designed and developed an algorithm that can get a desired tag cluster, which is capable of collecting the searched tags along with the related tags. We first performed a test to compare the difference between the user collected tag data through RSS and the reduced data. The second test focused on the accuracy of extracted related tags that depends on the similarity functions, such as the Pearson Correlation and Euclidean. Finally, we showed the final results visually using the graph algorithm.

• Journal of KIISE
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• v.42 no.10
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• pp.1195-1206
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• 2015

For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.

• Journal of the Korea Computer Industry Society
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• v.3 no.1
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• pp.77-84
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• 2002

An cluster system used consist of large number of running servers, one has the problem that does the low availability occured by the high chance of the server failures and it is difficult to provide occuring software aging. In this paper, running cluster web servers consists of n primary servers and k backup servers, based on the operational parameters such as number of running primary servers, number of backup severs, rejuvenation period, rejuvenation time, failure rate of sewers, repair rate of servers, unstable rate of servers. We calculate to evaluate the rejuvenation policy such steady-state probabilities, downtime, availability, and downtime cost. We validate the solutions of mathematical model by experiments based on various operation parameters and find that the software rejuvenation method can be adopted as prventive fault tolerant technique for stability of system. The failure rate and unstable rate of the servers are essential factors for decision making of the rejuvenation policies.