• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.85-102
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• 2004

Currently, developed technologies for semantic web services discovery are based on ontologies. These ontologies are DAML-S(DARPA Agent Markup Language for Services) and Process Handbook Project of MIT. These technologies have some problems for intelligent web services discovery. So, in this paper we analyzed those ontologies and proposed TM-S, Topic Maps for Services. TM-S is the presentation model for semantic web services. And TM-S includes benefits and complements weaknesses of those ontologies. And we proposed TMS-QL, TM-S Query Language. TMS-QL is query language for intelligent web services discovery. At last, we designed and implemented intelligent web service discovery system that deals TM-S ontology and TMS-QL

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.23-32
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• 2017

We can obtain useful knowledge from data by using a subgroup discovery algorithm. Subgroup discovery is a rule model learning method that finds data subgroups containing specific information from data and expresses them in a rule form. Subgroups are meaningful as they account for a high percentage of total data and tend to differ significantly from the overall data. Subgroup is expressed with conjunction of only literals previously. So, the scope of the rules that can be derived from the learning process is limited. In this paper, we propose a method to increase expressiveness of rules through internal disjunctive representation of attribute values. Also, we analyze the characteristics of existing subgroup discovery algorithms and propose an improved algorithm that complements their defects and takes advantage of them. Experiments are conducted with the traffic accident data given from Busan metropolitan city. The results shows that performance of the proposed method is better than that of existing methods. Rule set learned by proposed method has interesting and general rules more.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.285-301
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• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.290-293
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• 2000

Knowledge discovery refers to the overall process of discovering useful knowledge from data. The linear programming model is a special form of useful knowledge that is embedded in a database. Since formulating models from scratch requires knowledge-intensive efforts, knowledge-based formulation support systems have been proposed in the DSS area. However, they rely on the strict assumption that sufficient domain knowledge should already be captured as a specific knowledge representation form. Hence, the purpose of this paper is to propose a methodology that finds useful knowledge on building linear programming models from a database. The methodology consists of two parts. The first part is to find s first-cut model based on a data dictionary. To do so, we applied the GPS algorithm. The second part is to discover a second-cut model by applying neural network technique. An illustrative example is described to show the feasibility of the proposed methodology.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.95-117
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• 2011

Today, IT governance has also become a subject of attention along with recent technologies such as ITSM (IT Service Management), PPM (Project Portfolio Management) and Compliance. At the national level, the market is fairly recent. and therefore, lacks detailed research in the field. Models specifically related to EIM has not yet been presented to this day, hence, firms that are considering EIM as a potential part of their information management system may fall into a state of disorder in the process of its implementation. To this end, this research attempts to construct an IT governance model for EIM based on existing models, surveys and interviews. In particular, E-discovery has been applied as means of protecting information assets and its use as evidence. In addition, by applying the research model to a particular global firm and then assessing its documentation management system, the overall feasibility of the research model has been tested.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.104-111
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• 2008

A process mining is considered to support the discovery of business process for unstructured process model, and a process mining algorithm by using the associated rule and sequence pattern of data mining is developed to extract information about processes from event-log, and to discover process of alternative, concurrent and hidden activities. Some numerical examples are presented to show the effectiveness and efficiency of the algorithm.

• Management & Information Systems Review
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• v.32 no.5
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• pp.287-300
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• 2013

In this paper, price discovery between spot and futures in crude oil markets investigated using the Gonzalo and Granger and Hasbrouck common-factor models. The main findings are as follows. 1) Crude oil futures and spot market are cointegrated. 2) Following the preceding studies, we judged that Dubai(WTI) futures markets contribute to the price discovery process than Dubai(WTI) spot market when this Gonzalo-Granger and Hasbrouck information ratio for Dubai(WTI) market are larger than 0.5. In other words, the futures markets of Dubai and WTI plays a more dominant role in price discovery than the spot market. 3) But Brent futures market does not contribute to the price discovery process.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.49-55
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• 2019

Organizations design and operate business process models to achieve their goals efficiently and systematically. With the advancement of IT technology, the number of items that computer systems can participate in and the process becomes huge and complicated. This phenomenon created a more complex and subdivide flow of business process.The process instances that contain workcase and events are larger and have more data. This is an essential resource for process mining and is used directly in model discovery, analysis, and improvement of processes. This event log is getting bigger and broader, which leads to problems such as capacity management and I / O load in management of existing row level program or management through a relational database. In this paper, as the event log becomes big data, we have found the problem of management limit based on the existing original file or relational database. Design and apply schemes to archive and analyze large event logs through Hadoop, an open source distributed file system, and HBase, a NoSQL database system.

• YAKHAK HOEJI
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• v.59 no.4
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• pp.151-157
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• 2015

The purpose of this study is to demonstrate how lead compounds are best optimized with the application of in silico QSAR and PBPK modeling at the early drug discovery stage. Several predictive QSAR models such as $IC_{50}$ potency model, intrinsic clearance model and brain penetration model were built and applied to a set of virtually synthesized library of the BACE1 inhibitors. Selected candidate compounds were also applied to the PBPK modeling for comparison between the predicted animal pharmacokinetic parameters and the observed ones in vivo. This novel lead optimization strategy using QSAR and PBPK modelings could be helpful to expedite the drug discovery process.

• Environmental and Resource Economics Review
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• v.15 no.3
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• pp.479-504
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• 2006

This paper analyzes a price discovery process for international crude oils including the WTI, Brent and Dubai. Error correction model is employed considering non-stationarity property of crude oil price and the contemporaneous causality is constructed by graph theory to analyze the short-term causality. The empirical analysis for January 4., 1999 to July 15., 2005 reveals that the Brent price interconnects between the WTI price and the Dubai price. This result implies the substantial influence of the Brent price as a marker oil.