• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.6
• /
• pp.540-550
• /
• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• The Journal of Society for e-Business Studies
• /
• v.20 no.4
• /
• pp.41-59
• /
• 2015

Information systems has been developed and used in various business area, therefore there are abundance of history data (log data) stored, and subsequently, it is required to analyze those log data. Previous studies have been focusing on the discovering of relationship between events and no identification of anomaly instances. Previously, anomaly instances are treated as noise and simply ignored. However, this kind of anomaly instances can occur repeatedly. Hence, a new methodology to detect the anomaly instances is needed. In this paper, we propose a methodology of LAPID (Local Anomaly Process Instance Detection) for discriminating an anomalous process instance from the log data. We specified a distance metric from the activity relation matrix of each instance, and use it to detect API (Anomaly Process Instance). For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. To demonstrate our proposed methodology, we performed our experiment on real data from a domestic port terminal.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.9
• /
• pp.3126-3145
• /
• 2014

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users' job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users' budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.

• Journal of Information Technology Services
• /
• v.7 no.3
• /
• pp.97-114
• /
• 2008

Many companies have implemented ERP systems to enhance their process competitiveness. Since most ERP systems down to date are implemented and managed on each separated business-unit or company level, such systems run short of the consideration about global business processes and global system managements. In order to integrate a successful global ERP, it is essential to apply the well-systematic implementation methodology which considers global standardization and global IT requirements. It is, however, the actual circumstance that such well-structured methodologies for global ERP implementation are hardly shown not only from domestic site but from foreign one. This paper indicates the global ERP implementation guideline with integrated approach including; the standard process design for efficient execution of global business; the ERP implementation method considering global IT requirements; and, the management method for global system operation. GSI ERP methodology is composed of 3 Phase：Global Strategy Planning, Global Template Construction and Global Roll-Out. Phase1; Global Strategy Planning contains Environment Analysis, GSI direction and Implementation Plan. Phase2; Global Template Construction contains Business blueprint, GSI operation design and Global template implementation. Phase3; Global Roll-out contains local business analysis, local ERP implementation and Global ERP Operation.

• The Journal of Society for e-Business Studies
• /
• v.3 no.2
• /
• pp.179-202
• /
• 1998

This study reviews the concept of Integrated Logistic Support(ILS), ILS standards, ILS elements, and the processes of developing ILS elements and Logistics Support Analysis(LSA). It also examines the potentials of applying ILS development process to building CALS systems in commercial sectors in order to accomplish business process innovation and achieve life-cycle cost savings in product and equipment management. In order to utilize the ILS approach for commercial industries, we need customizing the process of Logistics Support Analysis defined in MIL-STD-1388-lA. The success of ILS implementation depends on the determination of the range of ILS elements in relation with the application environment, and the appropriate development of those elements. During the development process, in order to analyze and design logistics flow processes and supporting activities, we suggest the object-oriented logistics process modeling approach with basic concepts and constructs embedded in objects. Several diagrams including Class Diagram, Class-Instance Diagram, and Integrated Instance Diagram, are provided. Simple Steps to follow are suggested for the analysis and design of inter-organizational logistics flow and support processes. The outcomes of the study are expected to contribute to stimulating the utilization of ILS concepts and development process during building commercial CALS systems.

• Journal of information and communication convergence engineering
• /
• v.3 no.4
• /
• pp.209-212
• /
• 2005

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

• CDE review
• /
• v.9 no.3
• /
• pp.31-35
• /
• 2003

Product Data Management(PDM) is a tool that helps engineers and others manage both data and the product development process. Now a day, automotive industry is interested in PDM for their product's quality and cost. In this study, we introduce about samlip PDM construction instance. This case explains the demanded function of PDM and the detail implementation methodology for automotive company.

• The Journal of Society for e-Business Studies
• /
• v.10 no.4
• /
• pp.1-18
• /
• 2005

In ebXML, a representative framework for electronic commerce, a BPS (Business Process Specification) should be finally defined as an instance of XML-version BPSS for the configuration of B2B (Business to Business) runtime systems . In order to define the instance more complete and consistent, it is required to validate all the semantic constraints on the instance . Due to the limitations of XML Schema constructs, however, current XML-version BPSS fails to specify formal semantic constraints completely. This paper presents how to find, express and reuse BPSS semantic constraints that could not be explicitly defined in the XML-version BPSS. The method facilitates the validation of XML-version BPSS instances easily with some useful guides for fixing violations of semantic constraints. Furthermore, B2B business processes can be standardized and applied more efficiently and effectively.

• Journal of Korea Multimedia Society
• /
• v.7 no.5
• /
• pp.601-616
• /
• 2004

Information retrieval systems for structured documents provide multiple levels of retrieval capability by supporting structure-based queries. In order to process structure-based queries for structured documents, information for structural nesting relationship between elements and for element sequence must be maintained. This paper presents four index structures that can process various query types about structures such as structural relationships between elements or element occurrence order. The proposed algorithms are based on the concept of Global Document Instance Tree.

• Journal of KIISE:Software and Applications
• /
• v.28 no.10
• /
• pp.757-763
• /
• 2001

This paper propose a new clustering algorithm called G-CLUS based on the relative gravitation. In this method every instance has the same mass at first. the gravitations among instances make each instance move to the attractive direction gradually and eventually natural clusters are formed without the initial seed and the number of clusters. Our proposed method can determine the number of clusters via a process of gravitational agglomeration and it can reduce the sensitivity to outliers by using the resultant of gravitation. We also improved the computational complexity by applying the concept of a cube to the proposed algorithm. In our experiments, we show the behavior of instance movement clustering process for each model, clustering process and the results for an example data set, and the results of comparison between the other clustering algorithm and our proposed. method.