• The Journal of Society for e-Business Studies
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• v.12 no.2
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• pp.219-231
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• 2007

Based on a complex event processing technique, an event analysis method for Business Activity Monitoring(BAM) is developed to provide an early warning for on-going events so that process managers effectively detect and monitor potential risks prior to the completion of the events. In this study, process-based event monitoring procedures to extract events with significant risks are presented; Complex event patterns are defined from historical event log data and risks of events are evaluated based on the patterns. A process-based event monitoring architecture for BAM is also presented. The proposed method has been applied to a service process of a home shopping company.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.538-555
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• 2015

Event detection based on using features from a static grid can give poor results from the viewpoint of two main aspects: the position of the camera and the position of the event that is occurring in the scene. The former causes problems when training and test events are at different distances from the camera to the actual position of the event. The latter can be a source of problems when training events take place in any position in the scene, and the test events take place in a position different from the training events. Both issues degrade the accuracy of the static grid method. Therefore, this work proposes a method called a dynamic grid for event detection, which can tackle both aspects of the problem. In our experiment, we used the dynamic grid method to detect four types of event patterns: implosion, explosion, two-way, and one-way using a Multimedia Analysis and Discovery (MAD) pedestrian dataset. The experimental results show that the proposed method can detect the four types of event patterns with high accuracy. Additionally, the performance of the proposed method is better than the static grid method and the proposed method achieves higher accuracy than the previous method regarding the aforementioned aspects.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.570-592
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• 2019

The Event-B design pattern is an excellent way to quickly develop a formal model of the system. Researchers have proposed a number of Event-B design patterns, but they all lack formal behavior semantics. This makes the analysis, verification, and simulation of the behavior of the Event-B model very difficult, especially for the control-intensive systems. In this paper, we propose a novel method to transform the Event-B synchronous control flow design pattern into the labeled transition system (LTS) behavior model. Then we map the design pattern instantiation process of Event-B to the instantiation process of LTS model and get the LTS behavior semantic model of Event-B model of a multi-level complex control system. Finally, we verify the linear temporal logic behavior properties of the LTS model. The experimental results show that the analysis and simulation of system behavior become easier and the verification of the behavior properties of the system become convenient after the Event-B model is converted to the LTS model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.201-209
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• 2012

The point-based complex event processing handled an instantaneous event by using one time stamp in each event. However, the activity period of the event plays the important role in the field which is the same as the finance, multimedia, medicine, and meteorology. The point-based event is insufficient for expressing the complex temporal relationship in this field. In the application field of the real-time world, the event has the period. The events more than two kinds can be temporally overlapped. In addition, one event can include the other event. The relation about the events of kind of these can not be successive like the point-based event. This thesis designs and implements the method detecting the patterns of the complex event by using the interval-based events. The interval-based events can express the overlapping relation between events. Furthermore, it can include the others. By using the end point of beginning and end point of the termination, the operator of interval-based events shows the interval-based events. It expresses the sequence of the interval-based events and can detect the complex event patterns. This thesis proposes the algorithm using the active instance stack in order to raise efficiency of detection of the complex event patterns. When comprising the event sequence, this thesis applies the window push down technique in order to reduce the number of intermediate results. It raises the utility factor of the running time and memory.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.7-13
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• 2017

Many systems rely on reliable timestamps to determine the time of a particular action or event. This is especially true in digital investigations where investigators are attempting to determine when a suspect actually committed an action. The challenge, however, is that objects are not updated at the exact moment that an event occurs, but within some time-span after the actual event. In this work we define a simple model of digital systems with objects that have associated timestamps. The model is used to predict object update patterns for objects with associated timestamps, and make predictions about these update time-spans. Through empirical studies of digital systems, we show that timestamp update patterns are not instantaneous. We then provide a method for calculating the distribution of timestamp updates on a particular system to determine more accurate action instance times.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.24-30
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• 2020

As the number of internet-connected appliances and the variety of IoT services are rapidly increasing, it is hard to protect IT assets with traditional network security techniques. Most traditional network log analysis systems use rule based mechanisms to reduce the raw logs. But using predefined rules can't detect new attack patterns. So, there is a need for a mechanism to reduce congested raw logs and detect new attack patterns. This paper suggests enterprise security management for IoT services using graph and network measures. We model an event network based on a graph of interconnected logs between network devices and IoT gateways. And we suggest a network clustering algorithm that estimates the attack probability of log clusters and detects new attack patterns.

• Korean Journal of Computational Design and Engineering
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• v.15 no.1
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• pp.11-23
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• 2010

A flexible and correct model of the activity flows is required for workflows in product development environments. In particular, the design activity flows are not known until run-time, and conventional approaches have limit to handle this situation because they cannot predefine all the potentially reachable paths. Thus, the structure of the workflow model must be flexible enough to describe variety in workflow design and accommodate dynamic changes during workflow execution. In this paper, we provide the general primitive axioms and change patterns based on event calculus for dynamic workflow specification and execution mechanisms in product development environments. Also, we describe how to execute the workflow dynamically based on the workflow specification and workflow change patterns using abductive planning technique.

• Korean Journal of Computational Design and Engineering
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• v.18 no.4
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• pp.250-257
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• 2013

This paper introduces a failure analysis procedure that underpins real-time fault prognosis. In the previous study, we developed a systematic eventization procedure which makes it possible to reduce the original data size into a manageable one in the form of event logs and eventually to extract failure patterns efficiently from the reduced data. Failure patterns are then extracted in the form of event sequences by sequence-mining algorithms, (e.g. FP-Tree algorithm). Extracted patterns are stored in a failure pattern library, and eventually, we use the stored failure pattern information to predict potential failures. The two practical case studies (marine diesel engine and SIRIUS-II car engine) provide empirical support for the performance of the proposed failure analysis procedure. This procedure can be easily extended for wide application fields of failure analysis such as vehicle and machine diagnostics. Furthermore, it can be applied to human health monitoring & prognosis, so that human body signals could be efficiently analyzed.

• Korean Journal of Cognitive Science
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• v.10 no.1
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• pp.45-55
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• 1999

This paper argues that the basic modes of spatial cognition can be best identified in terms of argument/participant location, and shows that natural language uses‘simple’types of semantic denotations to encode spatial cognition, and further notes that spatial expressions should be interpreted not as locating an event/state as a whole but as locating arguments/participants of the event. The ways of locating events/states are identified in terms of argument orientation(AO), Which indicates semantic patterns of linkiarticipant location. and shows that natural langrage uses ng locatives to specific arguments. Four patterns of argument orientation described here reveal substantial modes of spatial cognition. and the AO patterns are mostly determined by the semantic classes of English verbs combining with locative expressions, i.e., by the event type of the predicate. As for the denotational constraint of locatives, the paper concludes that semantic denotations of locative PPs are restricted to the intersecting functions mapping relations to relations.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.11
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• pp.989-995
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• 2015

In the literature, various stochastic anomaly detection methods, such as limit checking and PCA-based approaches, have been applied to weld defect detection. However, it is still a challenge to identify meaningful defect patterns from very limited sensor signals of laser welding, characterized by intermittent, discontinuous, very short, and non-stationary random signals. In order to effectively analyze the physical characteristics of laser weld signals: plasma intensity, weld pool temperature, and back reflection, we first transform the raw data of laser weld signals into the form of event logs. This is done by multidimensional discretization and event-codification, after which the event logs are decoded to extract weld defect patterns by $Na{\ddot{i}}ve$ Bayes classifier. The performance of the proposed method is examined in comparison with the commercial solution of PRECITEC's LWM$^{TM}$ and the most recent PCA-based detection method. The results show higher performance of the proposed method in terms of sensitivity (1.00) and specificity (0.98).