• Journal of the Korea Society of Computer and Information
• /
• v.26 no.2
• /
• pp.221-227
• /
• 2021

In this paper, we tried to explore factors influencing the diffusion of online events through SNS by analyzing the online footprint of consumers. To this end, log data of online events conducted by "C" beer brands were collected and analyzed. The analysis unit of log data was set for each one hour, and the analyzing method used descriptive and regression analysis. Results are as follows. First, factors influencing the diffusion of the view of SNS-based online events were like, friend used coupon, and friend size. In particular, the size of friends had the greatest impact on the diffusion, which again suggests the importance of social hubs in online events. Second, factors influencing the diffusion of the number of inflows were also like, friend used coupon, and size of friends. Third, it was found that the number of reply did not affect the diffusion of views and inflows. This study is meaningful that it suggested an alternative plan to increase the effect of online events by using real data.

• 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.

• Korean Journal of Cognitive Science
• /
• v.20 no.4
• /
• pp.535-554
• /
• 2009

Information collected on mobile devices might be utilized to support user's memory, but it is difficult to effectively retrieve them because of the enormous amount of information. In order to organize information as an episodic approach that mimics human memory for the effective search, it is required to detect important event like landmarks. For providing new services with users, in this paper, we propose the prediction model to find landmarks automatically from various context log information based on attributed Bayesian networks. The data are divided into daily and weekly ones, and are categorized into attributes according to the source, to learn the Bayesian networks for the improvement of landmark prediction. The experiments on the Nokia log data showed that the Bayesian method outperforms SVMs, and the proposed attributed Bayesian networks are superior to the Bayesian networks modelled daily and weekly.

• The Journal of Bigdata
• /
• v.4 no.1
• /
• pp.11-27
• /
• 2019

There is gradually being a decrease in earnings rate of the container terminals because of worsened business environment. To enhance global competitiveness of terminal, operators of the container terminal have been attempting to deal with problems of operations through analyzing overall the terminal operations. For improving operations of the container terminal, the operators try to efforts about analyzing and utilizing data from the database which collects and stores data generated during terminal operation in real time. In this paper, we have analyzed the characteristics of operating processes and defined the event log data to generate container processes and CKO processes using stored data in TOS (terminal operating system). And we have explained how imperfect event logs creating non-normal processes are refined effectively by analyzing the container and CKO processes. We also have proposed the framework to refine the event logs easily and fast. To validate the proposed framework we have implemented it using python2.7 and tested it using the data collected from real container terminal as input data. In consequence we could have verified that the non-normal processes in the terminal operations are greatly improved.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.3
• /
• pp.401-406
• /
• 2021

Recently, mean-teacher models based on convolutional recurrent neural networks are popularly used in audio event detection. The mean-teacher model is an architecture that consists of two parallel CRNNs and it is possible to train them effectively on the weakly-labelled and unlabeled audio data by using the consistency learning metric at the output of the two neural networks. In this study, we tried to improve the performance of the mean-teacher model by using additional derivative features of the log-mel spectrum. In the audio event detection experiments using the training and test data from the Task 4 of the DCASE 2018/2019 Challenges, we could obtain maximally a 8.1% relative decrease in the ER(Error Rate) in the mean-teacher model using proposed derivative features.

• Journal of Internet Computing and Services
• /
• v.19 no.6
• /
• pp.83-89
• /
• 2018

In workflow process intelligence and systems, workflow process mining and analysis issues are becoming increasingly important. In order to improve the quality of workflow process intelligence, it is essential for an efficient and effective data center storing workflow enactment event logs to be provisioned in carrying out the workflow process mining and analytics. In this paper, we propose a three-dimensional process-aware datacube for organizing workflow enterprise data centers to efficiently as well as effectively store the workflow process enactment event logs in the XES format. As a validation step, we carry out an experimental process mining to show how much perfectly the process-aware datacubes are suitable for discovering workflow process patterns and its analytical knowledge, like enacted proportions and enacted work transferences, from the workflow process enactment event histories. Finally, we confirmed that it is feasible to discover the fundamental control-flow patterns of workflow processes through the implemented workflow process mining system based on the process-aware data cube.

• 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.

• Journal of Microbiology and Biotechnology
• /
• v.29 no.10
• /
• pp.1553-1560
• /
• 2019

In a previous study, the antimicrobial peptides extracted from Lactobacillus plantarum UTNGt2 of wild-type fruits of Theobroma grandiflorum (Amazon) were characterized. This study aimed to investigate the antimicrobial mechanisms of peptides in vitro and its protective effect on fresh tomatoes. The addition of partially purified Gt2 peptides to the E. coli suspension cells at the exponential ($OD_{605}=0.7$) growth phase resulted in a decrease with 1.67 (log10) order of magnitude compared to the control without peptide. A marginal event (< 1 log10 difference) was recorded against Salmonella, while no effect was observed when combined with EDTA, suggesting that the presence of a chelating agent interfered with the antimicrobial activity. The Gt2 peptides disrupted the membrane of E. coli, causing the release of ${\beta}$-galactosidase and leakage of DNA/RNA molecules followed by cell death, revealing a bacteriolytic mode of action. The tomatoes fruits coated with Gt2 peptides showed growth inhibition of the artificially inoculated Salmonella cocktail, demonstrating their preservative potential.

• Smart Media Journal
• /
• v.7 no.4
• /
• pp.70-78
• /
• 2018

Current trends in information system have led many companies to adopt security solutions. However, even with a large budget, they cannot function properly without proper security monitoring that manages them. Security monitoring necessitates a quick response in the event of a problem, and it is needed to design appropriate visualization dashboards for monitoring purposes so that necessary information can be delivered quickly. This paper shows how to visualize a security log using the open source program Elastic Stack and demonstrates that the proposed method is suitable for network security monitoring by implementing it as a appropriate dashboard for monitoring purposes. We confirmed that the dashboard was effectively exploited for the analysis of abnormal traffic growth and attack paths.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.5
• /
• pp.303-314
• /
• 2018

In the past few years, government agencies and corporations have succumbed to stealthy, tailored cyberattacks designed to exploit vulnerabilities, disrupt operations and steal valuable information. Security Information and Event Management (SIEM) is useful tool for cyberattacks. SIEM solutions are available in the market but they are too expensive and difficult to use. Then we implemented basic SIEM functions to research and development for future security solutions. We focus on collection, aggregation and analysis of real-time logs from host. This tool allows parsing and search of log data for forensics. Beyond just log management it uses intrusion detection and prioritize of security events inform and support alerting to user. We select Elastic Stack to process and visualization of these security informations. Elastic Stack is a very useful tool for finding information from large data, identifying correlations and creating rich visualizations for monitoring. We suggested using vulnerability check results on our SIEM. We have attacked to the host and got real time user activity for monitoring, alerting and security auditing based this security information management.