• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.336-339
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• 2007

A new query model is required to match requirements of stream-based applications such as patient monitoring system, since traditional DBMSs are not designed to provide continuous queries over stream data. In the patient monitoring system, there are many types of biomedical signals such as blood pressure and temperature, and these signals gathered by biomedical sensors should be treated as a stream, that is an ordered set of signals. In this paper, we categorized all possible queries to be used in patient monitoring system by four types of queries. Then, we have proposed a new sliding window query model which is capable of expressing these four types of queries.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.610-613
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• 2022

센서 데이터의 중요성이 커지면서 센서 데이터 처리 연구의 수요가 증가하고 있다. 센서 데이터 기반의 딥러닝 모델 개발 시, 센서 데이터 단일 값에 의한 출력이 아닌 시계열적인 특성을 반영하여 연속적인 데이터 간의 연관성을 파악할 수 있는 슬라이딩 윈도우 기법을 통해 효율적으로 데이터를 분석하고 처리할 수 있다. 하지만, 기존의 방법들은 학습 성능(학습 시간 및 모델 성능)에 미치는 영향을 평가하는 기준 없이 입력 데이터의 윈도우 사이즈를 임의로 설정하여 데이터를 처리해 왔다. 따라서, 본 논문은 학습 시간과 모델 성능을 기준으로 센서 데이터의 윈도우 사이즈 최적화 기법을 제안한다. 제안한 방법은 전류를 이용하여 스위치와 다이오드 온도를 추정하는 가상 센서(virtual sensor) 실험 테스트베드에 적용하여, 학습 시간 중심으로는 5%의 윈도우 사이즈를, 모델 성능 중심으로는 R2 SCORE 의 값을 0.9295 로 갖는 8%의 윈도우 사이즈가 최적으로 도출되었다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.149-157
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• 2012

In this thesis, the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few result tuples as possible. An alternative scenario is to provide a random sample of the join result, e.g., if the output of the join is being aggregated. It is shown formally that neither approximation can be addressed effectively for a sliding-window join of arbitrary input streams. Previous work has addressed only the maximum-subset problem, and has implicitly used a frequency based model of stream arrival. There exists a sampling problem for this model. More importantly, it is shown that a broad class of applications for which an age-based model of stream arrival is more appropriate, and both approximation scenarios under this new model are addressed. Finally, for the case of multiple joins being executed with an overall memory constraint, an algorithm for memory allocation across the join that optimizes a combined measure of approximation in all scenarios considered is provided.

• 전자공학회논문지 IE
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• v.48 no.3
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• pp.16-19
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• 2011

Differentiated Service is an IP QoS ensuring method based on packet marking that allows packets to be prioritized according to user requirements. During the time of congestion, more low priority packets are dropped than high priority packets. Different policy models are used to determine how to mark the packet. This paper investigated the performance of Differentiated Service using time sliding window with 3 color marking (TSW3CM). Simulation results using NS-2 showed that Differentiated Service can provide the quality of service requirements.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.3
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• pp.509-516
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• 2008

Recently, the operator modules to control external devices are concerned about automatic management system to process continuously changed signals. These signals are the stream data of which characteristics are several numbers. a short report interval and asynchronous report time. It is necessary that the system brings about high accuracy and real time process for stream data. The typical queries of these systems consist of the current query to search the latest signal value, the snapshot query at a past time, the sliding window query from a past time to current. In this paper, we propose the efficient method to manage the above signals by using a file structured database in small-size operating systems. We also propose a query model to accommodate various queries including the sliding window query. The file database in the QNX adopts a delta version and a shared memory buffering method for the resource limit of a small storage and a low computing power.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.871-874
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• 2007

Stream data need a efficient data management with high reliability and real time processing. The characteristics of these data are a large volume, a short report interval and asynchronous report time. The typical queries of these systems consist of the current query to search the latest signal value, the snapshot query to search the signal value of a past time, the historical query to search the signal value of a past time to current. This paper proposes the efficient method to manage the above signals by using a file structured database in QNX operating systems. The query model to accommodate various query for stream data is proposed. The proposed methods are applied to reactive protection system to verify their usefulness. The COM(Cabinet Operator Module) based on the QNX employs file database that adopts a delta version and a buffering method for the resource limit of a small storage and a low computing power.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.673-682
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• 2009

XML data are widely used for data representation and exchange on the Web and the data type is an continuous stream in ubiquitous environment. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the sliding window. XML stream data are modeled as a tree set, called XFP_tree and we quickly extract the frequent structures over recent XML data in the XFP_tree.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.211_212
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• 2009

부하 모델링이 전력계통의 해석과 제어에 미치는 영향력은 매우 크다. 정확한 부하모델링을 위해 최근에는 측정기반의 부하 모델링이 많이 연구되고 있다. 부하모델링 알고리즘은 먼저 모델의 구조를 정하고, 그 모델에 적합한 파라미터를 산정하는 작업으로 이루어진다. 파라미터 산정에는 최소자승법이 사용되었다. 이 최적화 과정에서 알고리즘을 통해 나온 계산값과 측정값의 차이가 최소가 되도록 파라미터를 산정하였다. 슬라이딩 윈도우(sliding window)의 개념을 도입해, 실시간으로 변화하는 부하의 동특성을 반영할 수 있는 온라인 부하모델링을 수행하였다. 실측 데이터의 취득을 위해 학교 부하에 PMU를 설치하였다. 본 논문에서는 사례 연구를 위해 실시간 시뮬레이터의 데이터를 이용하였고, 정적 ZIP 모델을 통해 알고리즘의 정확성을 검토하였다.