• Journal of Information Technology Applications and Management
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• v.15 no.2
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• pp.1-14
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• 2008

The distinguished prosperity of information technologies from great progress of e-business during the last decade has unavoidably made software development for active data mining to discovery hidden predictive information regarding business trends and behavior from vary large databases. Therefore this paper develops an active mining object(ADMO) component, which provides real-time predictive information from web databases. The ADMO component is to extended ADO(ActiveX Data Object) component to active data mining component based on COM(Component Object Model) for application program interface(API). ADMO component development made use of window script component(WSC) based on XML(eXtensible Markup Language). For the purpose of investigating the application environments and the practical schemes of the ADMO component, experiments for diverse practical applications were performed in this paper. As a result, ADMO component confirmed that it could effectively extract the analytic information of classification and aggregation from vary large databases for Web services.

• Asia pacific journal of information systems
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• v.15 no.4
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• pp.153-164
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• 2005

Nowadays, large amounts of XML documents are available in the Internet. Thus, we need to analyze them multidimensionally in the same way as relational data. In this paper, we propose a new framework for multidimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where all fact and dimension data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new OLAP language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate measure, axis and slicer. They incorporate text mining operations for aggregating text data. We apply XML-OLAP to the United States patent XML warehouse to demonstrate multidimensional analysis of XML documents.

• Proceedings of the Korea Association of Information Systems Conference
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• 2005.05a
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• pp.65-78
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• 2005

Nowadays, large amounts of XML documents are available on the Internet. Thus, we need to analyze them multi-dimensionally in the same way as relational data. In this paper, we propose a new frame-work for multidimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact data as well as dimension data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multidimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure data. They specify text mining operators for aggregating text constituting the measure data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-dimensionally analyzing the U.S. patents.

• The KIPS Transactions:PartA
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• v.17A no.4
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• pp.181-188
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• 2010

In this study, we suggest the system to analyze network users' action patterns by using Data-Mining Technique. We installed Network Tap to implement the analysis system of network action and copied the network packet. The copied packet is stored at the database through MainMemoryDB(MMDB) of the high-speed. The stored data analyze the users' action patterns by using Data-Mining Technique and then report the results to the network manager on real-time. Also, we applied the standard XML document exchange method to share the data between different systems. We propose this action pattern analysis system operated embedded type of SetToBox to install easily and support low price.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.257-264
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• 2014

This paper is to construction of the data mining preprocessing tool for efficient database marketing. We compare and evaluate the often used data mining tools based on the access method to local and remote databases, and on the exchange of information resources between different computers. The evaluated preprocessing of data mining tools are Answer Tree, Climentine, Enterprise Miner, Kensington, and Weka. We propose a design principle for an efficient system for data preprocessing for data mining on the distributed networks. This system is based on Java technology including EJB(Enterprise Java Beans) and XML(eXtensible Markup Language).

• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.59-65
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• 2017

XML has established itself as the format for data exchange on the internet and the volume of its instance is large scale. Therefore, to extract similar information from XML instance is one of research topics but is insufficient. In this paper, we extract similar information from various kind of XML instances according to the same goal. Also we use only the structure information of XML instance for information extraction because some of XML instance is described without its schema. In order to efficiently extract similar information, we propose a minimum unit of processing and two approaches for finding the unit. The one is a structure-based method which uses only the structure information of XML instance and another is a measure-based method which finds a unit by numerical formula. Our two approaches can be applied to any application that needs the extraction of similar information based on XML data. Also the approach can be used for HTML instance.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.61-70
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• 2022

General users who use smartphone apps often use the Vault app to protect personal information such as photos and videos owned by individuals. However, there are increasing cases of criminals using the Vault app function for anti-forensic purposes to hide illegal videos. These apps are one of the apps registered on Google Play. This paper proposes a methodology for extracting feature points through XML-based keyword frequency analysis to explore Vault apps used by criminals, and text mining techniques are applied to extract feature points. In this paper, XML syntax was compared and analyzed using strings.xml files included in the app for 15 hidden Vault anti-forensics apps and non-hidden Vault apps, respectively. In hidden Vault anti-forensics apps, more hidden-related words are found at a higher frequency in the first and second rounds of terminology processing. Unlike most conventional methods of static analysis of APK files from an engineering point of view, this paper is meaningful in that it approached from a humanities and sociological point of view to find a feature of classifying anti-forensics apps. In conclusion, applying text mining techniques through XML parsing can be used as basic data for exploring hidden Vault anti-forensics apps.

• Journal of Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.27-35
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• 2006

As the increasing interest about geographical information, such researches and applied fields become wide. OGC(Open GIS Consortium) developed GML(Geography Markup Language) which is adopted XML(extensible Markup Language) in GIS field. In various applied field, GML is used and studied continuously. This paper try to find out the meaningful rules using Apriori algorithm from GML documents, one of the data mining techniques which is studied based on existing XML documents There are two ways to find out the rules. One is the way that find out the related rules as extracting the content in GML documents, the other find out the related rules based on used tags and attributes. This paper describes searching the rules through two ways and shows the system adopted two ways.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.261-269
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• 2007

As a basic research to integrate and to retrieve XML documents efficiently, this paper proposes a clustering method by structures of XML documents. We apply an algorithm processing the many transaction data to the clustering of XML documents, which is a quite different method from the previous algorithms measuring structure similarity. Our method performs the clustering of XML documents not only using the cluster histograms that represent the distribution of items in clusters but also considering the global cluster cohesion. We compare the proposed method with the existing techniques by performing experiments. Experiments show that our method not only creates good quality clusters but also improves the processing time.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.15-23
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• 2006

Millions of documents are already on the Internet, and new documents are being formed all the time. This poses a very important problem in the management and querying of documents to classify them on the Internet by the most suitable means. However, most users have been using the document classification method based on a keyword. This method does not classify documents efficiently, and there is a weakness in the category of document that includes meaning. Document classification by a person can be very correct sometimes and often times is required. Therefore, in this paper, We wish to classify documents by using a neural network algorithm and C4.5 algorithms. We used resume data forming by XML for a document classification experiment. The result showed excellent possibilities in the document category. Therefore, We expect an applicable solution for various document classification problems.