• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.647-653
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• 2022

Data mining is a technique that explores knowledge such as relationships and patterns between data by exploring and analyzing data. Data that occurs in the real world includes a temporal attribute. Temporal data mining research to find useful knowledge from data with temporal properties can be effectively utilized for predictive judgment that can predict the future. In this paper, we propose an algorithm using time-unit grouping to classify the database into regular time period units and discover frequent pattern itemsets in time units. The proposed algorithm organizes the transaction and items included in the time unit into a matrix, and discovers frequent items in the time unit through grouping. In the experimental results for the performance evaluation, it was found that the execution time was 1.2 times that of the existing algorithm, but more than twice the frequent pattern itemsets were discovered.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.365-374
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• 2011

Recently, malicious video classification and filtering techniques are of practical interest as ones can easily access to malicious multimedia contents through the Internet, IPTV, online social network, and etc. Considerable research efforts have been made to developing malicious video classification and filtering systems. However, the malicious video classification and filtering is not still being from mature in terms of reliable classification/filtering performance. In particular, the most of conventional approaches have been limited to using only the spatial features (such as a ratio of skin regions and bag of visual words) for the purpose of malicious image classification. Hence, previous approaches have been restricted to achieving acceptable classification and filtering performance. In order to overcome the aforementioned limitation, we propose new malicious video classification framework that takes advantage of using both the spatial and temporal features that are readily extracted from a sequence of video frames. In particular, we develop the effective temporal features based on the motion periodicity feature and temporal correlation. In addition, to exploit the best data fusion approach aiming to combine the spatial and temporal features, the representative data fusion approaches are applied to the proposed framework. To demonstrate the effectiveness of our method, we collect 200 sexual intercourse videos and 200 non-sexual intercourse videos. Experimental results show that the proposed method increases 3.75% (from 92.25% to 96%) for classification of sexual intercourse video in terms of accuracy. Further, based on our experimental results, feature-level fusion approach (for fusing spatial and temporal features) is found to achieve the best classification accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.462-464
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• 1998

이질적인 환경 (Heterogoneous Environments)에서 실시간 멀티미디어 서비스가 확산됨에 따라 비디오 데이터로 인한 트래픽이 통신망을 오가는 트래픽의 대부분을 차지하게 되었다. 이에 통신망 자원의 효율화를 위해서 사용자의 통신망 환경을 고려한, 비디오 데이터 스케러블 전송이 필요하다. 이를 위해 MPEG-2에서는 비디오 데이터를 Base layer와 Enhancement layer로 나누는 layered coding 방식을 채택할 수 있게 하여 낮은 대역폭인 경우는 Base layer만 보내고 높은 대역폭인 경우 Base layer와 Enhancement layer를 모두 보내는 방식을 사용할 수 있도록 하였으나, 실질적으로는 Base layer만으로도 데어터 량이 많아 이를 적용하는 인코더는 현재 전무한 실정이다. 따라서 본 논문에서는 Base layer를 MPEG-2비디오 데이터 스케러블 전송에 맞게 초당 디스플레이하는 프레임 수를 동적으로 조정하는 temporal layered coding을 통해 사용자 통신망 환경에 맞게 비디오 데이터를 보내는 방법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.217-220
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• 2001

이질적인 환경인 인터넷을 통한 멀티미디어 서비스가 확산됨에 따라 다량의 데이터를 가지는 비디 스트림의 전송이 늘어나게 되었다. 이로 인해 네트워크 트래픽의 대부분을 멀티미디어 데이터가 차지하게 되었고, 통신망 자원의 효율적인 사용을 위하여 사용자의 컴퓨터 환경이나 통신망의 환경을 고려한 비디오 데이터 전송에 관한 연구가 필요하게 되었다. 이에 본 논문에서는 네트워크 QoS 를 고려하여 MPEG 비디오 데이터를 전송하도록 하기 위하여 Temporal Scaling 기법과 QoS 에 따라 frame-rate 을 조절하여 전송할 수 있는 Scalable 전송 기법을 제안한다. 본 논문에서 제안하는 기법을 사용하여 MPEG 비디오 데이터를 전송할 때 보다 효율적으로 통신망 자원을 사용할 수 있게 된다.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.99-110
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• 2010

An event means a flow which has a time attribute such as a symptom of patient. Stream data collected by sensors can be summarized as an interval event which has a time interval between the start-time point and the end-time point in multiple stream data environment. Most of temporal mining techniques have considered only the frequent events. However, these approaches may ignore the infrequent event even if it is important. In this paper, we propose a new temporal data mining that can find association rules for the significant temporal relation based on interval events in multidimensional stream data environment. Our method considers the weight of events and stream data on the sensing time point of abnormal events. And we can discover association rules on the significant temporal relation regardless of the occurrence frequency of events. The experimental analysis has shown that our method provide more useful knowledge than other conventional methods.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.215-224
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• 2011

U-Health collects patient information with various kinds of sensor. Stream data can be summarized as an interval event which has aninterval between start-time-point and end-time-point. Most of temporal mining techniques consider only the event occurrence-time-point and ignore stream data change of state. In this paper, we propose the temporal mining technique considering the event change of state in U-Health. Our method overcomes the restrictions of the environment by sending a significant event in U-Health from sensors to a server. We define four event states of stream data and perform the temporal data mining considered the event change of state. Finally, we can remove an ambiguity of discovered rules by describing cause-and-effect relations among events in temporal relation sequences.

• Spatial Information Research
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• v.16 no.2
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• pp.219-236
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• 2008

Data stream collected from sensors contain a large amount of useful information including causality relationships. The causality join query for data stream is to retrieve a set of pairs (cause, effect) from streams of data. A part of causality pairs may however be lost from the query result, due to the delay from sensors to a data stream management system, and the limited size of sliding windows. In this paper, we first investigate spatial, temporal, and spatio-temporal aspects of the causality join query for data stream. Second, we propose several strategies for sliding window management based on these observations. The accuracy of the proposed strategies is studied by intensive experiments, and the result shows that we improve the accuracy of causality join query in data stream from simple FIFO strategy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.909-916
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• 2010

In ubiquitous GIS services is possible to use the spatio-temporal data using a mobile device at anytime. Also, client is transmitted latest spatio-temporal data from server. But traditional systems have a problem that the time of transmitting latest information from server to client takes long time because of collecting data periodically. In this paper, we proposed Two-way Synchronization system supporting field update to solve the existing problem. This system uses mobile device for collecting changed data in the real world and sending collected data to server.

• Spatial Information Research
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• v.18 no.1
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• pp.77-87
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• 2010

Spatio-temporal join operators are essential to the management of spatio-temporal data such as moving objects. For example, the join operators are parts of processing to analyze movement of objects and search similar patterns of moving objects. Various studies on spatio-temporal join queries in outdoor space have been done. Recently with advance of indoor positioning techniques, location based services are required in indoor space as well as outdoor space. Nevertheless there is no one about processing of spatio-temporal join query in indoor space. In this paper, we introduce continuous spatio-temporal self-join queries in indoor space and propose a method of processing of the join queries over stream data of moving objects. The continuous spatio-temporal self-join query is to update the joined result set satisfying spatio-temporal predicates continuously. We assume that positions of moving objects are represented by symbols such as a room or corridor. This paper proposes a data structure, called Candidate Pairs Buffer, to filter and maintain massive stream data efficiently and we also investigate performance of proposed method in experimental study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.574-575
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• 2018

The generation of redundant data according to the spatial-temporal correlation in a wireless sensor network that reduces the network lifetime by consuming unnecessary energy. In this paper, data collection experiment through the particulate matter sensor is carried out to confirm the spatial-temporal data redundancy and we propose permitted limit setting method for data transmission to solve this problem. In the proposed method, the data transmission permitted limit is set by using the integrated average value in the cluster. The set permitted limit reduces the redundant data of the member node and it is shows that redundant data reduction is possible even in a variable environment of collected data by resetting the permitted limit in the cluster head.