• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.131-140
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• 2013

This paper deals with the design and implementation of an interface for interoperation between DiskSim, a well-known disk simulator, and a system-level simulator based on DEVSim++. Such inter-operational simulation aims at evaluation of an overall performance of storage systems which consist of multiple computer nodes with a variety of I/O level specifications. A well-known system-level simulation framework, DEVSim++ environment is based on the DEVS formalism, which provides a sound semantics of modular and hierarchical modeling methodology at the discrete event systems level such as multi-node computer systems. For maintainability we assume that there is no change of the source codes for two heterogeneous simulation engines. Thus, we adopt a notion of simulators interoperation in which there should be a means to synchronize simulation times as well as to exchange messages between simulators. As an interface for such interoperation DiskSimManager is designed and implemented. Various experiments, comparing the results of the standalone DiskSim simulation and the interoperation simulation using the proposed interface of DiskSimManager, proved that DiskSimManager works correctly as an interface for interoperation between DEVSim++ and DiskSim.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.147-163
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• 2016

Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.106-117
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• 2003

The advanced medical information systems usually consist of loosely-coupled interaction of independent systems, such as HIS/RIS and PACS. To support easier information exchange between these systems and between hospitals, and to support new types of medical service such as teleradiology, it becomes essential to integrate separated medical information and allow them to be exchanged and retrieved through internet. This thesis proposes an integrated medical information system using XML. We analyzed HL7 and DICOM standard formats, and designed an integrated XML DTD. We extracted information from HL7 messages and DICOM files and generated XML document instances and XSL stylesheets based on the proposed XML DTD. We implemented the web interface for the integrated medical information system, which supports data sharing, information exchange and retrieval between two different standard formats. The proposed XML-based integrated medical information system will contribute to solve the problems of current medical information systems, by enabling integration of separated medical informations and by allowing data exchange and sharing through internet. The proposed system with XML is more robust than web-based medical information systems developed by using HTML, because XML itself provides more flexibility and extensibility than HTML.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.259-270
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• 2010

Information systems nowadays are heterogeneous and distributed which integrate the enterprise information by processes. They are also very complex, because they are linked together by processes. It aims to integrate the systems so that these systems work as one system. A process is a framework which contains all of the business activities in an enterprise, and has a lot of information which is needed for measuring performance. A process consists of activities, and an activity contains events which can be considered information sources. In most cases, it is very valuable to determine if a process is meaningful, but it is difficult because of the complexity in measuring performance, and also because finding relationships between business factors and events is not a simple problem. So it would reduce operation cost and allow efficient process execution if I could evaluate the process before it ends. In this paper we propose an event based process measurement model. First, we propose the concept of process performance measurement, and a model for selecting process and activity indexes from the events which are collected from information systems. Second, we propose at methodologies and data schema that can store and manage the selected process indexes, the mapping methods between indexes and events. Finally, we propose a process Performance measurement model using the collected events which gives users a valuable managerial information.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.282-296
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• 2010

OWL is an ontology language for the Semantic Web, and suited to modelling the knowledge of a specific domain in the real-world. Ontology also can infer new implicit knowledge from the explicit knowledge. However, the modeled knowledge cannot be complete as the whole of the common-sense of the human cannot be represented totally. Ontology do not concern handling nonmonotonic reasoning to detect incomplete modeling such as the integrity constraints and exceptions. A default rule can handle the exception about a specific class in ontology. Integrity constraint can be clear that restrictions on class define which and how many relationships the instances of that class must hold. In this paper, we propose a practical reasoning system for open and closed-world reasoning that supports a novel hybrid integration of ontology based on open world assumption (OWA) and non-monotonic rule based on closed-world assumption (CWA). The system utilizes a method to solve the problem which occurs when dealing with the incomplete knowledge under the OWA. The method uses the answer set programming (ASP) to find a solution. ASP is a logic-program, which can be seen as the computational embodiment of non-monotonic reasoning, and enables a query based on CWA to knowledge base (KB) of description logic. Our system not only finds practical cases from examples by the Protege, which require non-monotonic reasoning, but also estimates novel reasoning results for the cases based on KB which realizes a transparent integration of rules and ontologies supported by some well-known projects.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.716-730
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• 2008

In Service-Oriented Architecture (SOA), service providers develop and deploy reusable services on the repositories, and service consumers utilize blackbox form of services through their interfaces. Services are also highly evolvable and often heterogeneous. Due to these characteristics of the service, it is hard to manage the faults if faults occur on the services. Autonomic Computing (AC) is a way of designing systems which can manage themselves without direct human intervention. Applying the key disciplines of AC to service management is appealing since key technical issues for service management can be effectively resolved by AC. In this paper, we present a theoretical model, Symptom-Cause-Actuator (SCA), to enable autonomous service fault management in SOA. We derive SCA model from our rigorous observation on how physicians treat patients. In this paper, we first define a five-phase computing model and meta-model of SCA. And, we define a schema of SCA profile, which contains instances of symptoms, causes, actuators and their dependency values in a machine readable form. Then, we present detailed algorithms for the five phases that are used to manage faults the services. To show the applicability of our approach, we demonstrate the result of our case study for the domain of 'Flight Ticket Management Services'.

• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.465-475
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• 2002

An object-oriented framework supports efficient component-based software development by providing a flexible architecture that can be decomposed into easily modifiable and composable classes. Object-oriented frameworks require thorough testing as they are intended to be reused repeatedly In developing numerous applications. Furthermore, additional testing is needed each time the framework is modified and extended for reuse. To test a framework, it must be instantiated into a complete, executable system. It is, however, practically impossible to test a framework exhaustively against all kinds of framework instantiations, as possible systems into which a framework can be configured are infinitely diverse. If we can classify possible configurations of a framework into a finite number of groups so that all configurations of a group have the same structural or behavioral characteristics, we can effectively cover all significant test cases for the framework testing by choosing a representative configuration from each group. This paper proposes a systematic method of classifying object structures of a framework hotspot and extracting structural test patterns from them. This paper also presents how we can select an instance of object structure from each extracted test pattern for use in the frameworks hotspot testing. This method is useful for selection of optimal test cases and systematic construction of executable test target.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.48-59
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• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.