• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.101-121
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• 2010

This paper presents an intelligent prefetching technique that significantly improves performance of hybrid fash-disk storage, a combination of flash memory and hard disk. Since flash memory embedded in a hybrid device is much faster than hard disk in terms of I/O operations, it can be utilized as a 'cache' space to improve system performance. The basic strategy for prefetching is to utilize sequential pattern mining, with which we can extract the access patterns of objects from historical access sequences. We use two techniques for enhancing the performance of hybrid storage with prefetching. One of them is to modify a FAST algorithm for mapping the flash memory. The other is to extend the unit of prefetching to a block level as well as a file level for effectively utilizing flash memory space. For evaluating the proposed technique, we perform the experiments using the synthetic data and real UCC data, and prove the usability of our technique.

• Journal of the Korean Society for information Management
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• v.24 no.2
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• pp.89-104
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• 2007

This research attempts to give a personalized recommendation framework in large-sized music contents environment. Despite of existing studios and commercial contents for recommendation systems, large online shopping malls are still looking for a recommendation system that can serve personalized recommendation and handle large data in real-time. This research utilizes data mining technologies and new pattern matching algorithm. A clustering technique is used to get dynamic user segmentations using user preference to contents categories. Then a sequential pattern mining technique is used to extract contents access patterns in the user segmentations. And the recommendation is given by our recommendation algorithm using user contents preference history and contents access patterns of the segment. In the framework, preprocessing and data transformation and transition are implemented on DBMS. The proposed system is implemented to show that the framework is feasible. In the experiment using real-world large data, personalized recommendation is given in almost real-time and shows acceptable correctness.

• Journal of KIISE:Databases
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• v.36 no.3
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• pp.169-179
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• 2009

Communication log data consist of communication events such as sending and receiving e-mail or instance message and visiting web sites, etc. Many countries including USA and EU enforce the retention of these data on the communication service providers for the purpose of investigating or detecting criminals through the Internet. Because size of the retained data is very large, the efficient method for extracting valuable information from the data is needed for Law Enforcement Authorities to use the retained data. This paper defines the Interactive Communication Sequence Patterns(ICSPs) that is the important information when each communication event in communication log data consists of sender, receiver, and timestamp of this event. We also define a Mining(FDICSP) problem to discover such patterns and propose a method called Fast Discovering Interactive Communication Sequence Pattern(FDICSP) to solve this problem. FDICSP focuses on the characteristics of ICS to reduce the search space when it finds longer sequences by using shorter sequences. Thus, FDICSP can find Interactive Communication Sequence Patterns efficiently.

• The Journal of Information Systems
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• v.30 no.3
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• pp.21-45
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• 2021

Purpose This paper analyzed the impacts of domestic stock market by a global pandemic such as COVID-19. We investigated how the overall pattern of the stock market changed due to the impact of the COVID-19 pandemic. In particular, we analyzed in depth the pattern of stock price, as well, tried to find what factors affect on stock market index(KOSPI) in the healthcare industry due to the COVID-19 pandemic. Design/methodology/approach We built a data warehouse from the databases in various industrial and economic fields to analyze the changes in the KOSPI due to COVID-19, particularly, the changes in the healthcare industry centered on bio-medicine. We collected daily stock price data of the KOSPI centered on the KOSPI-200 about two years before and one year after the outbreak of COVID-19. In addition, we also collected various news related to COVID-19 from the stock market by applying text mining techniques. We designed four experimental data sets to develop decision tree-based prediction models. Findings All prediction models from the four data sets showed the significant predictive power with explainable decision tree models. In addition, we derived significant 10 to 14 decision rules for each prediction model. The experimental results showed that the decision rules were enough to explain the domestic healthcare stock market patterns for before and after COVID-19.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• Computers and Concrete
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• v.29 no.1
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• pp.31-45
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• 2022

Experimental and numerical simulation were used to investigate the effects of angle and number of T shape non-persistent crack on the shear behaviour of crack's bridge area under uniaxial compressive test. concrete samples with dimension of 150 mm×150 mm×40 mm were prepared. Within the specimen, T shape non-persistent notches were provided. 16 different configuration systems were prepared for T shape non-persistent crack based on two and three cracks. In these configurations, the length of cracks were taken as 4 cm and 2 cm based on the cracks configuration systems. The angle of larger crack related to horizontal axis was 0°, 30°, 60° and 90°. Similar to cracks configuration systems in the experimental tests, 28 models with different T shape non-persistent crack angle were prepared in numerical model. The length of cracks were taken as 4 cm and 2 cm based on the cracks configuration systems. The angle of larger crack related to horizontal axis was 0°, 15°, 30°, 45°, 60°, 75° and 90°. Tensile strength of concrete was 1 MPa. The axial load was applied to the model. Displacement loading rate was controlled to 0.005 mm/s. Results indicated that the failure process was significantly controled by the T shape non-persistent crack angle and crack number. The compressive strengths of the specimens were related to the fracture pattern and failure mechanism of the discontinuities. Furthermore, it was shown that the compressive behaviour of discontinuities is related to the number of the induced tensile cracks which are increased by increasing the crack number and crack angle. The strength of samples decreased by increasing the crack number. In addition, the failure pattern and failure strength are similar in both methods i.e. the experimental testing and the numerical simulation methods (PFC2D).

• Geomechanics and Engineering
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• v.29 no.4
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• pp.471-486
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• 2022

The effect of segmental joints is one of main importance for the segmental lining design when tunnels are excavated by a mechanized process. In this paper, segmental tunnel linings are analyzed by two numerical methods, namely the Beam-Spring Method (BSM) and the Solid-Interface Method (SIM). For this purpose, the Tehran Subway Line 6 Tunnel is considered to be the reference case. Comprehensive 2D numerical simulations are performed considering the soil's calibrated plastic hardening model (PH). Also, an advanced 3D numerical model was used to obtain the stress relaxation value. The SIM numerical model is conducted to calculate the average rotational stiffness of the longitudinal joints considering the joints bending moment distribution and joints openings. Then, based on the BSM, a sensitivity analysis was performed to investigate the influence of the ground rigidity, depth to diameter ratios, slippage between the segment and ground, segment thickness, number of segments and pattern of joints. The findings indicate that when the longitudinal joints are flexible, the soil-segment interaction effect is significant. The joint rotational stiffness effect becomes remarkable with increasing the segment thickness, segment number, and tunnel depth. The pattern of longitudinal joints, in addition to the joint stiffness ratio and number of segments, also depends on the placement of longitudinal joints of the key segment in the tunnel crown (similar to patterns B and B').

• Advances in concrete construction
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• v.13 no.2
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• pp.199-213
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• 2022

Experimental and discrete element approaches were used to examine the effects of F shape non-persistent joints on the failure behaviour of concrete under uniaxial compressive test. concrete specimens with dimensions of 200 cm×200 cm×50 cm were provided. Within the specimen, F shape non-persistent joint consisting three joints were provided. The large joint length was 6 cm, and the length of two small joints were 2 cm. Vertical distance between two small joints change from 1.5 cm to 4.5 cm with increment of 1.5 cm. In constant joint lengths, the angle of large joint change from 0° to 90° with increments of 30°. Totally 12 different models were tested under compression test. The axial load rate on the model was 0.05 mm/min. Concurrent with experimental tests, numerical simulation (Particle flow code in two dimension) were performed on the models containing F shape non-persistent joint. Distance between small joints and joint angles were similar to experimental one. the results indicated that the failure process was mostly governed by both of the Distance between small joints and joint angles. The axial loading rate on the model was 0.05 mm/min. The compressive strengths of the samples were related to the fracture pattern and failure mechanism of the discontinuities. Furthermore, it was shown that the compressive behaviour of discontinuities is related to the number of the induced tensile cracks which are increased by increasing the joint angle. In the first, there were only a few acoustic emission (AE) hits in the initial stage of loading, and then AE hits rapidly grow before the applied stress reached its peak. Furthermore, a large number of AE hits accompanied every stress drop. Finally, the failure pattern and failure strength are similar in both approaches i.e., the experimental testing and the numerical simulation approaches.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.59-62
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• 2001

This paper discusses a data mining technique for time series analysis of traffic data, which provides useful knowledge for network configuration management. Commonly, a network designer must employ a combination of heuristic algorithms and analysis in an interactive manner until satisfactory solutions are obtained. The problem of heuristic algorithms is that it is difficult to deal with large networks and simplification or assumptions have to be made to make them solvable. Various data mining techniques are studied to gain valuable knowledge in large and complex telecommunication networks. In this paper, we propose a traffic pattern association technique among network nodes, which produces association rules of traffic fluctuation patterns among network nodes. Discovered rules can be utilized for improving network topologies and dynamic routing performance.