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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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KIPS Transactions on Software and Data Engineering
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Korea Information Processing Society
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Volume & Issues
Volume 2, Issue 12 - Dec 2013
Volume 2, Issue 11 - Nov 2013
Volume 2, Issue 10 - Oct 2013
Volume 2, Issue 9 - Sep 2013
Volume 2, Issue 8 - Aug 2013
Volume 2, Issue 7 - Jul 2013
Volume 2, Issue 6 - Jun 2013
Volume 2, Issue 5 - May 2013
Volume 2, Issue 4 - Apr 2013
Volume 2, Issue 3 - Mar 2013
Volume 2, Issue 2 - Feb 2013
Volume 2, Issue 1 - Jan 2013
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Improvement of Classification Accuracy on Success and Failure Factors in Software Reuse using Feature Selection
Kim, Young-Ok ; Kwon, Ki-Tae ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 4, 2013, Pages 219~226
DOI : 10.3745/KTSDE.2013.2.4.219
Feature selection is the one of important issues in the field of machine learning and pattern recognition. It is the technique to find a subset from the source data and can give the best classification performance. Ie, it is the technique to extract the subset closely related to the purpose of the classification. In this paper, we experimented to select the best feature subset for improving classification accuracy when classify success and failure factors in software reuse. And we compared with existing studies. As a result, we found that a feature subset was selected in this study showed the better classification accuracy.
An Service oriented XL-BPMN Metamodel and Business Modeling Process
Song, Chee-Yang ; Cho, Eun-Sook ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 4, 2013, Pages 227~238
DOI : 10.3745/KTSDE.2013.2.4.227
The business based existing BPMN model is a lack of service oriented modeling techniques. Therefore, it requires a layered technique of service oriented business modeling so that can meet the design for a complex application system, developing a system based on SOA. In order to enhance reusability and modularity of BPMN business model, this paper proposes a metamodel and business modeling process based on this metamodel that can hierarchically build a BPMN model. Towards this end, the XL-BPMN metamodel hierarchically established based on MDA and MVS styles are first defined. Then a BPMN service modeling process is constructed based on modeling elements of this metamodel according to the modeling phases. Finally, the result of a case study in which the proposed method is applied to an online shopping mall system is discussed. With the use of well-defined metamodel and modeling process, it is hoped that it can be shown that a service dominated and layered BPMN business model can be established, and that the modularity and reusability of the constructed BPMN business model can be maximized.
A Formal Model and a Design of Inference Engine for Context-Aware Mobile Computing
Kim, Moon Kwon ; Kim, Soo Dong ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 4, 2013, Pages 239~250
DOI : 10.3745/KTSDE.2013.2.4.239
Context-aware mobile computing has become the primary approach to realize automatic, autonomous, and user-centric computing in the context of largely increasing the amount of mobile devices used that embed available sensors. However, designing an inference engine nonetheless requires the tasks of analyzing contexts, situations that can be inferred, etc. Moreover, a mobile device has limited resources and limited computation capability, which results in recognizing the common sense of its unsuitable environment for processing inference. Hence, we propose context-situation reasoning elements and their formal models in this paper, and we verify the formal models' applicability by applying them to an example. Finally, we design and implement an inference engine that realize the context-situation inference elements in computing environment, and we experiment an example by using the proposed inference engine to verify applicability and reusability of the inference engine.
A Study on the Influence Factors in Data Quality of Public Organizations
Jung, Seung Ho ; Jeong, Duke Hoon ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 4, 2013, Pages 251~266
DOI : 10.3745/KTSDE.2013.2.4.251
By the progress of informatization, the data which is involved in the administration and public organizations are increased the requestion of the utilization. Nevertheless most of the agencies could not actively participate in sharing and opening the data to the public because of data quality problems. The purpose of this study is to verify the relationship for data quality, managerial and organizational factors which is to derive at the level of the organization's data quality management success factors suggested in previous studies, and the acceptance of the organization's quality management. The result identify that organizational factors, organization's data quality management encouragement and support, give effect data quality through the acceptance of data quality management. However, managerial factors was no effect the data quality management acceptance. This study than managerial approach when considering the quality control for the public organizations, in the early days of the current situation of a company-wide consensus was required, as well as directly to the level of quality factors affecting the quality of acceptance is presented to derive but has significance.
Efficient Similarity Joins by Adaptive Prefix Filtering
Park, Jong Soo ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 4, 2013, Pages 267~272
DOI : 10.3745/KTSDE.2013.2.4.267
As an important operation with many applications such as data cleaning and duplicate detection, the similarity join is a challenging issue, which finds all pairs of records whose similarities are above a given threshold in a dataset. We propose a new algorithm that uses the prefix filtering principle as strong constraints on generation of candidate pairs for fast similarity joins. The candidate pair is generated only when the current prefix token of a probing record shares one prefix token of an indexing record within the constrained prefix tokens by the principle. This generation method needs not to compute an upper bound of the overlap between two records, which results in reduction of execution time. Experimental results show that our algorithm significantly outperforms the previous prefix filtering-based algorithms on real datasets.
Medical Image Classification and Retrieval Using BoF Feature Histogram with Random Forest Classifier
Son, Jung Eun ; Ko, Byoung Chul ; Nam, Jae Yeal ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 4, 2013, Pages 273~280
DOI : 10.3745/KTSDE.2013.2.4.273
This paper presents novel OCS-LBP (Oriented Center Symmetric Local Binary Patterns) based on orientation of pixel gradient and image retrieval system based on BoF (Bag-of-Feature) and random forest classifier. Feature vectors extracted from training data are clustered into code book and each feature is transformed new BoF feature using code book. BoF features are applied to random forest for training and random forest having N classes is constructed by combining several decision trees. For testing, the same OCS-LBP feature is extracted from a query image and BoF is applied to trained random forest classifier. In contrast to conventional retrieval system, query image selects similar K-nearest neighbor (K-NN) classes after random forest is performed. Then, Top K similar images are retrieved from database images that are only labeled K-NN classes. Compared with other retrieval algorithms, the proposed method shows both fast processing time and improved retrieval performance.
Machine Learning Process for the Prediction of the IT Asset Fault Recovery
Moon, Young-Joon ; Rhew, Sung-Yul ; Choi, Il-Woo ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 4, 2013, Pages 281~290
DOI : 10.3745/KTSDE.2013.2.4.281
The IT asset is a core part that supports the management objective of an organization, and the fast settlement of the IT asset fault is very important. In this study, a fault recovery prediction technique is proposed, which uses the existing fault data to address the IT asset fault. The proposed fault recovery prediction technique is as follows. First, the existing fault recovery data were pre-processed and classified by fault recovery type; second, a rule was established for the keyword mapping of the classified fault recovery types and reported data; and third, a machine learning process that allows the prediction of the fault recovery method based on the established rule was presented. To verify the effectiveness of the proposed machine learning process, company A's 33,000 computer fault data for the duration of six months were tested. The hit rate for fault recovery prediction was approximately 72%, and it increased to 81% via continuous machine learning.
Analyzing Users' Perception and Attitude Associated with Usage of Signage
Kim, Hang Sub ; Kim, Hyung Joon ; Lee, Bong Gyou ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 4, 2013, Pages 291~302
DOI : 10.3745/KTSDE.2013.2.4.291
Signage can be defined as the media device that provides specific information to many unspecified users in public places. Recently applied context-aware technology, signage provides personal on-demand information services in a way that can continue to evolve. The purpose of this study is to analyze characteristics of types which users identify signage on the perceptions and attitudes about consideration of the perspective of the experts in the fields. The research is carried out by applying Q methodology with in-depth interview. First, interviews are conducted to determine the perceptions and attitudes of experts and practitioners on signage. Thereafter users' perceptions and attitudes toward signage are classified by each types using Q methodology. The first type is named as 'signage as smart media', the second type is named as 'signage as passive media', and the third type is named as 'signage as interactive media' is named. The results of this study will be useful guidelines for conducting further academic researches and R&D.