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Journal of Intelligence and Information Systems,
Vol. 16, No. 1, March 2010
Ontology Modularization Evaluation Framework
Sun-Ju Oh
Vol. 16, No. 1, Page: 1 ~ 16
Keywords : Ontology, Module, Modularization, Coupling, Cohesion
Abstract
Several techniques and methods for ontology modularization have been proposed recently. However, there are few ontology evaluation frameworks to evaluate these techniques and methods. Most researches on ontology modularization have not been focused on ontology modularization evaluation but ontology modularization process itself. In this paper, we devise a novel ontology modularization evaluation framework to measure the quality of ontology modules, logical integrity during modularization process and modularization tools. Experiments were conducted to validate the proposed framework. Three representative modularization approaches SWOOP, Prompt, and PATO were chosen and used to partition or extract modules from an ontology. Then the proposed evaluation framework is applied to these modules. The experiment results indicate that the modularization framework works well. The proposed framework would help ontology engineers improve ontology module quality, anticipate and reduce future maintenance as well as help ontology users to choose ontology modules that best meet their requirements.
Knowledge as Marketing Message : Design and Analysis of Human-Reader Based Personal Experience Management Business Model
Jung-Ho Jun, and Kyoung-Jun Lee
Vol. 16, No. 1, Page: 17 ~ 43
Keywords : Human-Reader System, Mobile RFID, Life-log, Ubiquitous Computing, Business Model, Tag Evaluation Model
Abstract
This research considers the role of knowledge as marketing message, designs and analyses the personal experience management (PEM) business model using Human-Reader system. It is difficult to save and manage person's daily experience and relevant contents due to the lack of proper infrastructure and system. On the contrary, using Human-Reader infrastructure, person's experience and various relevant contents can be easily saved and managed because seamlessness between offline and online and the various devices that person can always carry along in ubiquitous environment. Since person can store and manage information, contents and advertisements through Human-Reader system and u-PEMS, marketing messages and advertisements do not have to be repetitive and stimulating. Instead, marketing messages and advertisements in Human-Reader environment should be granting values that can be saved and managed. We propose various scenarios, processes and its issues. And we analyze the expected value of RFID tag used on the proposed business model by so-called 'Tag Evaluation Model' and assess the assumptions that are basis of the proposed business model for evaluate the feasibility of the u-PEM business model.
Secure Routing Mechanism to Defend Multiple Attacks in Sensor Networks
Soo-Young Moon, and Tae-Ho Cho
Vol. 16, No. 1, Page: 45 ~ 56
Keywords : Wireless Sensor Networks, Wormhole Attacks, False Report Attacks
Abstract
Sensor Networks are composed of many sensor nodes, which are capable of sensing, computing, and communicating with each other, and one or more sink node(s). Sensor networks collect information of various objects' identification and surrounding environment. Due to the limited resources of sensor nodes, use of wireless channel, and the lack of infrastructure, sensor networks are vulnerable to security threats. Most research of sensor networks have focused on how to detect and counter one type of attack. However, in real sensor networks, it is impractical to predict the attack to occur. Additionally, it is possible for multiple attacks to occur in sensor networks. In this paper, we propose the Secure Routing Mechanism to Defend Multiple Attacks in Sensor Networks. The proposed mechanism improves and combines existing security mechanisms, and achieves higher detection rates for single and multiple attacks.
Processing the Data from the uTSN of Uninterrupted Traffic Flow
Eun-Mi Park, and Euy-Hyun Suh
Vol. 16, No. 1, Page: 57 ~ 69
Keywords : Ubiquitous Sensor Network, Realtime Traffic Data Processing, Speed Profile, Shockwave Profile
Abstract
The ubiquitous transportation system environments make it possible to collect each vehicle's position and velocity data and to perform more sophisticated traffic flow management at individual vehicle or platoon level through V2V and V2I communication. It is necessary to develop a new data processing methodology to take advantage of the ubiquitous transportation system environments. This paper proposed to build 3-dimension data profiles to maintain the detailed traffic flow information contained in the individual vehicles' data and at the same time to keep the profiles from the meaningless fluctuations. Also methods to build the platoon profile and the shock wave speed profile are proposed, which have not been possible under ITS(Intelligent Transportation System) environments.
Development of an Intelligent Trading System Using Support Vector Machines and Genetic Algorithms
Sun-Woong Kim, and Hyunchul Ahn
Vol. 16, No. 1, Page: 71 ~ 92
Keywords : Intelligent Trading Systems, Support Vector Machines, Genetic Algorithms, KOSPI200
Abstract
As the use of trading systems increases recently, many researchers are interested in developing intelligent trading systems using artificial intelligence techniques. However, most prior studies on trading systems have common limitations. First, they just adopted several technical indicators based on stock indices as independent variables although there are a variety of variables that can be used as independent variables for predicting the market. In addition, most of them focus on developing a model that predicts the direction of the stock market indices rather than one that can generate trading signals for maximizing returns. Thus, in this study, we propose a novel intelligent trading system that mitigates these limitations. It is designed to use both the technical indicators and the other non-price variables on the market. Also, it adopts 'two-threshold mechanism' so that it can transform the outcome of the stock market prediction model based on support vector machines to the trading decision signals like buy, sell or hold. To validate the usefulness of the proposed system, we applied it to the real world data-the KOSPI200 index from May 2004 to December 2009. As a result, we found that the proposed system outperformed other comparative models from the perspective of 'rate of return'.
Illegal Cash Accommodation Detection Modeling Using Ensemble Size Reduction
Hwa-Kyung Lee, Sang-Bum Han, and Won-Chul Jhee
Vol. 16, No. 1, Page: 93 ~ 116
Keywords : Illegal Cash Accommodation, Fraud Detection System, Diversity Measure, Data Mining
Abstract
Ensemble approach is applied to the detection modeling of illegal cash accommodation (ICA) that is the well-known type of fraudulent usages of credit cards in far east nations and has not been addressed in the academic literatures. The performance of fraud detection model (FDM) suffers from the imbalanced data problem, which can be remedied to some extent using an ensemble of many classifiers. It is generally accepted that ensembles of classifiers produce better accuracy than a single classifier provided there is diversity in the ensemble. Furthermore, recent researches reveal that it may be better to ensemble some selected classifiers instead of all of the classifiers at hand. For the effective detection of ICA, we adopt ensemble size reduction technique that prunes the ensemble of all classifiers using accuracy and diversity measures. The diversity in ensemble manifests itself as disagreement or ambiguity among members. Data imbalance intrinsic to FDM affects our approach for ICA detection in two ways. First, we suggest the training procedure with over-sampling methods to obtain diverse training data sets. Second, we use some variants of accuracy and diversity measures that focus on fraud class. We also dynamically calculate the diversity measure-Forward Addition and Backward Elimination. In our experiments, Neural Networks, Decision Trees and Logit Regressions are the base models as the ensemble members and the performance of homogeneous ensembles are compared with that of heterogeneous ensembles. The experimental results show that the reduced size ensemble is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.
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