• Proceedings of the CALSEC Conference
• /
• 2003.09a
• /
• pp.242-248
• /
• 2003

◎ Uncover the hidden pattern from massive data(data warehouse) -Builds a reasonable model to predict the future for business advantage -Decision Making based on the learned models(omitted)

• 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.

• Tunnel and Underground Space
• /
• v.27 no.6
• /
• pp.366-376
• /
• 2017

In this paper, we investigated the subsidence possibility and countermeasures according to the current mining method through investigation of the subsidence condition in operation mine. Most of the metal mine were broken, investigating to subsidence pattern of the Sink-hole. Coal mines are becoming more and more deep, investigating to Trough type subsidence patterns in existing mining areas. History of nonmetallic mines have not been developed for over 30 years, but large and small ground deformation problems have been investigated. Mining also has ground subsidence functionality due to time dependence by relying more heavily on empirical methods than technical methods. Therefore, it is necessary to carry out the various researches on systematic development method and prevention of subsidence of nonmetallic mines.

• The Journal of the Korea Contents Association
• /
• v.13 no.4
• /
• pp.399-406
• /
• 2013

Recently, studies about process mining for creating and analyzing business process models from log data have received much attention from BPM (Business Process Management) researchers. Process mining is a kind of method that extracts meaningful information and hidden rules from the event log of enterprise information systems such as ERP and BPM. In this paper, repair processes of electronic devices are analyzed using ProM which is a process mining tool. And based on the analysis of repair processes, the method for finding major failure patterns is proposed by multi-dimensional data analysis beyond simple statistics. By using the proposed method, the reliability of electronic device can be increased by providing the identified failure patterns to design team.

• Nuclear Engineering and Technology
• /
• v.56 no.9
• /
• pp.3750-3757
• /
• 2024

Rapidly obtaining the coverage characteristics of leaching solution in In-situ Leaching Area of Sandstone Uranium Mines is a necessary condition for optimizing well locations reasonably. In the presented study, the improved algorithm of the Fast Marching Method (FMM) was studied for rapidly solving coverage characteristics to replace the groundwater numerical simulator. First, the effectiveness of the FMM was verified by simulating diffusion characteristics of the leaching solution in In-situ Leaching Area. Second, based on the radial flow pressure equation and the interaction mechanism of the front diffusion of production and injection well flow field, an improved FMM which is suitable for In-situ Leaching Mining, was developed to achieve the co-simulation of production and injection well. Finally, the improved algorithm was applied to engineering practice to guide the design and production. The results show that the improved algorithm can efficiently solve the coverage characteristics of leaching solution, which is consistent with those obtained from traditional numerical simulators. In engineering practice, the improved FMM can be used to rapidly analyze the leaching process, delineate Leaching Blind Spots, and evaluate the rationality of well pattern layout. Furthermore, it can help to achieve iterative optimization and rapid decision-making of production and injection well locations under largescale mining area models.

• Geomechanics and Engineering
• /
• v.11 no.5
• /
• pp.607-624
• /
• 2016

Rockburst is becoming more serious in Chinese coal mine. One of the effective methods to control rockburst is blasting. In the paper, we monitored and analyzed the blasting waves at different blast center distances by the Hilbert-Huang transform (HHT) in a coal mine. Results show that with the increase of blast center distance, the main frequency and amplitude of blasting waves show the decreasing trend. The attenuation of blasting waves is slower in the near blast field (10-75 m), compared with the far blast field (75-230 m). Besides, the frequency superposition phenomenon aggravates in the far field. A majority of the blasting waves energy at different blast center distances is concentrated around the IMF components 1-3. The instantaneous energy peak shows attenuation trend with the blast center distance increase, there are two obvious energy peaks in the near blast field (10-75 m), the energy spectrum appears "fat", and the total energy is greater. By contrast, there is only an energy peak in the far blast field, the energy spectrum is "thin", and the total energy is lesser. The HHT three dimensional spectrum shows that the wave energy accumulates in the time and frequency with the increasing of blast center distance.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.38 no.4
• /
• pp.45-55
• /
• 2015

In this paper, we consider curriculum mining as an application of process mining in the domain of education. The basic objective of the curriculum mining is to construct a registration pattern model by using logs of registration data. However, subject registration patterns of students are very unstructured and complicated, called a spaghetti model, because it has a lot of different cases and high diversity of behaviors. In general, it is typically difficult to develop and analyze registration patterns. In the literature, there was an effort to handle this issue by using clustering based on the features of students and behaviors. However, it is not easy to obtain them in general since they are private and qualitative. Therefore, in this paper, we propose a new framework of curriculum mining applying K-means clustering based on subject attributes to solve the problems caused by unstructured process model obtained. Specifically, we divide subject's attribute data into two parts : categorical and numerical data. Categorical attribute has subject name, class classification, and research field, while numerical attribute has ABEEK goal and semester information. In case of categorical attribute, we suggest a method to quantify them by using binarization. The number of clusters used for K-means clustering, we applied Elbow method using R-squared value representing the variance ratio that can be explained by the number of clusters. The performance of the suggested method was verified by using a log of student registration data from an 'A university' in terms of the simplicity and fitness, which are the typical performance measure of obtained process model in process mining.

• Computers and Concrete
• /
• v.17 no.6
• /
• pp.723-737
• /
• 2016

In this paper an approach was described for determination of direction of sliding block in rock slopes containing planar non-persistent open joints. For this study, several gypsum blocks containing planar non-persistent open joints with dimensions of $15{\times}15{\times}15cm$ were build. The rock bridges occupy 45, 90 and $135cm^2$ of total shear surface ($225cm^2$), and their configuration in shear plane were different. From each model, two similar blocks were prepared and were subjected to shearing under normal stresses of 3.33 and $7.77kg/cm^{-2}$. Based on the change in the configuration of rock-bridges, a factor called the Effective Joint Coefficient (EJC) was formulated, that is the ratio of the effective joint surface that is in front of the rock-bridge and the total shear surface. In general, the failure pattern is influenced by the EJC while shear strength is closely related to the failure pattern. It is observed that the propagation of wing tensile cracks or shear cracks depends on the EJC and the coalescence of wing cracks or shear cracks dominates the eventual failure pattern and determines the peak shear load of the rock specimens. So the EJC is a key factor to determine the sliding direction in rock slopes containing planar non-persistent open joints.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.11
• /
• pp.2866-2879
• /
• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.7
• /
• pp.113-130
• /
• 2014

In this paper, we propose BLOCS algorithm to find sequence of data that should be saved in cache device of hybrid storage system which uses SSD as a cache device. BLOCS algorithm which uses a sequence pattern mining scheme, creates a set of frequently requested sectors with respect to requested order of sectors. To compare the performance of the proposed scheme, we introduce Distance (DIST) based scheme, Request Frequency (FREQ) based scheme, and Frequency times Size (F-S) based scheme. We measure the hit ratio and I/O latency of different caching schemes using hybrid storage caching simulator. We acquired booting workload along with ten scenarios of launching applications and use the workloads as input to the cache simulator. After experiment with booting workload, we find that BLOCS scheme gives hit ratio of 61% which is about 15% higher than the least performing DIST scheme.