• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.1022-1031
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• 2009

In this paper, we propose a researcher modeling technique based on ontology and construct social network for researchers using diverse relational properties. User ontology schema is created by extending the existing HR-XML model for a researcher model. User ontology schema and instance are created by OWL. We compose social network model for efficient cooperation between researchers using static relational properties such as educational background and dynamic relational properties such as co-authors and co-workers, etc. Closeness has direction because researcher network is differently configured by the researchers. We define inferencing rules using SWRL and inference ontology rules using racer inference machine to compose direct relationships between researchers. The proposed model for researchers can be applied to the cooperation model for researchers by retrieving common expert group dynamically.

• Journal of KIISE:Software and Applications
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• v.28 no.1
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• pp.14-25
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• 2001

For having various applications, XML is widely used in various fields. But, there have not been proposed a system to integrate and manage XML and its applications together. In this paper, we propose methods to integrate web-based XML applications(SMIL, RDF, WIDL) and for object-oriented modeling of each DTD and document instance. We prppose and system to merge object modeling of XML applications. With the proposed integrating algorithm, we can not only easily analysis various web-based XML applications which is represented by complex tags but also generate object-oriented schema for each document and store it to OODBMS.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1084-1098
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• 2005

In this paper, we propose a migration technique for autonomous mobile agents suitable to dynamic environments. The proposed migration technique dynamically creates the itinerary of agents that considers states of networks and systems. In the migration of the agent, it first sends prefetching message to the next system. The system receives necessary data for migration in advance. Through this, we reduce the amount of the sending data and save the time for creating the instance of the agent. And it improves the execution efficiency by using the checkpoint-based recovery method that does not execute the agent again and recovers the process states even though the errors take place. To show superiority of the proposed technique, we compare the proposed method with the existing methods through various simulations.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.95-102
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• 2007

This paper presents the core technology of Construction Business Process Automation to model and automate construction business processes. Business Process Reengineering (BPR) and Automation (BPA) have been recognized as one of the important aspects in construction business management. However, BPR requires a lot of efforts to identify, document, implement, execute, maintain, and keep track thousands of business processes to deliver a project. Moreover, existing BPA technologies used in existing Enterprise Resource Planning (ERP) systems do not lend themselves to effective scalability for construction business process management. Application of Workflow and Object Technologies would be quite effective in implementing a scalable enterprise application for construction business processes by addressing how: 1) Automated construction management tasks are developed as software components, 2) The process modeling is facilitated by dragging-and dropping task components in a network, 3) Raising business requests and instantiating corresponding process instances are delivered, and 4) Business process instances are executed by using workflow technology based on real-time simulation engine. This paper presents how the construction business process automation is achieved by using equipment reservation and cancellation processes simplified intentionally.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.970-976
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• 2001

In this paper, OUIG IDL CFE, provided by Sunsoft, is used to take a IDL definitions as inputs and parse those. OmniORB3 is introduced to support functionality of the ORB. Suns CFE produce AST after parsing inputs. Actually, the node of AST Is instances of classes which are derived from CFE classes. As the compiler back end visit the node of the AST using iterator class, UTL_ScopeActiveIterator, it dumps codes of output. During processing, two files are generated. Routines of generating code are invoked by BE_produce.cc and codes are produced while visiting root of AST, idl_global->root(). The dump* functions which dump codes is called according to the type of node. In this paper, Mapping C++ of IDL definition is experimented and results In the same as that of omniidl which is provided by omniORB3. The code of results behavior correctly on omniORB3. In the future, we are interested in optimizing the performance of marshalling code via IDL compiler.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.406-409
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• 2008

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data sets show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2357-2364
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• 2008

In this paper, we consider the problem of exploiting a taxonomy of propositionalized attributes in order to generate compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data set, show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.35-47
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• 2011

This paper proposes a set of rules to automatically generate OWL ontologies from relational databases. The purpose of the rules is to allow semantic access to existing RDB data without any database schema transformation and data migration process. In other words, the rules help a RDBMS play as a web ontology repository as well. However, the use of the mapping rules between RDB and OWL proposed by other studies for the objective causes troubles as follows. First, databases including the tables with a specific structure can't be translated into OWL. Second, the process for extracting an OWL individual unnecessarily lead to database join operations, or several SQL queries. On the other hand, our rules is designed to prevent these problems, can generate OWL classes and properties from database schemas and can generate OWL individuals from the database instances. In addition, an ontology generated by our rules is an OWL 2 DL ontology.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.43-55
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• 2003

Workflow mining is a newly emerging research issue for rediscovering and reengineering workflow models from workflow logs containing information about workflows being executed on the workflow engine. In this paper, we proposed workcase mining which was used dependency among activities. Main purpose of this paper is to minimize discrepancies between the modeled workflow process and the enacted workflow process as it is actually being executed. That is, we can get a complete set of activity firing sequences on buildtime. Besides, we can discover from workflow logs that which path out of all reachable paths a workcase has actually followed through on runtime. For this purpose we proposed two algorithm, the one is 'Activily-Dependent Net Algorithm' and the other is 'E-Walk Series Analysis Algorithm'.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.273-282
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• 2009

Missing data is one of the common problems in building analysis or prediction models using software project data. Missing imputation methods are known to be more effective missing data handling method than deleting methods in small software project data. While K nearest neighbor imputation is a proper missing imputation method in the software project data, it cannot use non-missing information of incomplete project instances. In this paper, we propose an approach to missing data imputation for numerical software project data by combining K nearest neighbor and maximum likelihood estimation; we also extend the average absolute error measure by normalization for accurate evaluation. Our approach overcomes the limitation of K nearest neighbor imputation and outperforms on our real data sets.