• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1242-1251
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• 2009

A DSMS(Data Stream Management System) in ubiquitous environment processes huge data that input from a number of sensor. The existed system is used with a load shedding method that is eliminated with a part of huge data stream when it doesn't process the huge data stream. The Load shedding method has to filter a part of input data. This is because, data completeness or reliability is decreased. In this paper, we proposed the overload prediction load balancing to maintain data completeness when the system has an overload. The proposed method predicts the overload time. and than it is decreased with data loss when achieves the prediction overload time. The performance evaluation shows that the proposed method performs better than the existed method.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.95-113
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• 1997

This paper outlines a framework for a diagnosis of a complex system with uncertain information. Sensor validation ploys a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Here, emphases are put on the heuristic technology and post-processor for reasoning. Heuristic Sensor Validation (HSV) exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. Finally the modified probability distributions and validated data are used as input to the reasoning scheme which is the runtime version of the influence diagram. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in $HEATXPRT^{TM}$ a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants [1].

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.267-270
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• 2021

본 논문에서는 기차의 운행 중 철도에서의 여러 위험 요소를 관리하기 전, 이를 테스트 하기 위해 기차 모형의 특정 부위나 철도 혹은 주변 요소에 아두이노 센서를 부착하여 감지된 값을 제공하고, 수집한 스트림 데이터를 브라우저 화면에 실시간으로 출력하여 모니터링하는 웹 애플리케이션을 설계하고 구현한다. 이를 통해 사용자는 웹을 이용하여 손쉽고 간편하게 철도에서의 상황 정보가 변화하는 것을 파악할 수 있고, 문제 발생 시 데이터를 분석하여 어떤 문제가 있는지 알 수 있다. 이를 이용하여 여러 애플리케이션과 연동해서 사용자에게 편의성과 편리성을 제공한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8A
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• pp.683-690
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• 2011

The conventional sensor tag data filtering algorithm uses time window based data filtering for each tag data. However, this approach shows many performance problems such as low error and event detection rate and larger storage size requirement. In this paper, we propose a collaborative sensor tag data filtering algorithm to improve sensor data processing performance. simulation study shows that the proposed sensor tag filtering algorithm outperforms the conventional filtering algorithm in terms of the processing time, the size of required data storage memory and accuracy of error and event detection rate.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.327-341
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• 2006

As we are getting to deal with more applications that generate streaming data such as sensor network, monitoring, and SDI (selective dissemination of information), active research is being conducted to support efficient processing of queries over streaming data. The applications on the Web environment like SDI, among others, require query processing over streaming XML data, and its investigation is very important because XML has been established as the standard for data exchange on the Web. One of the major problems with the previous systems that support query processing over streaming XML data is that they cannot deal adaptively with dynamically changing stream because they rely on static query plans. On the other hand, the stream query processing systems based on relational data model have achieved adaptiveness in query processing due to query operator routing. In this paper, we propose a system of adaptive query processing over streaming XML data in which the model of adaptive query processing over streaming relational data is applied. We compare our system with YFiiter, one of the representative systems that provide XML stream query processing capability, to show efficiency of our system.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.406-409
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• 2008

Clustering data streams has an importance over many applications like sensor networks. Existing hierarchical methods follow a semi fuzzy clustering that yields duplicate clusters. In order to solve the problems, we propose an extended online divisive agglomerative clustering on data streams. It builds a tree-like top-down hierarchy of clusters that evolves with data streams using geometric time frame for snapshots. It is an enhancement of the Online Divisive Agglomerative Clustering (ODAC) with a pruning strategy to avoid duplicate clusters. Our main features are providing update time and memory space which is independent of the number of examples on data streams. It can be utilized for clustering sensor data and network monitoring as well as web click streams.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.137-145
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• 2010

Digital vessel have to accurate and efficient mange the digital data from various sensors in the digital vessel. But, In sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. In this paper, We propose efficient processing method that arrange some sensors (temperature, humidity, lighting, voice) and process query based on sliding window for efficient input stream and pre-clustering using multiple Support Vector Machine(SVM) algorithm and manage hash table to summarized information. Processing performance improve as store and search and memory using hash table and usage reduced so maintain hash table in memory. We obtained to efficient result that accuracy rate and processing performance of proposal method using 35,912 data sets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.1014-1021
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• 2009

In an environment of home network where a number of technologies including heterogeneous hardware platforms, networking and protocols, middleware systems, and etc, exist, OSGi provides a platform for deployment and sharing of services managed in hardware and guarantees compatibility among applications. However, only simple control and processing of event data are considered in a home network using OSGi, and the consideration about real time processing of data stream generated by sensors is not enough. Therefore, researches allowing users to effectively develop OSGi applications by using various kinds of sensors generating data streams in the home network environment using OSGi are needed. In this paper, we propose an effective method of processing various types of real time data streams supplied to OSGi applications, including filtering, grouping, and counting, etc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.321-344
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• 2017

Smart homes are the new generation of homes where pervasive computing is employed to make the lives of the residents more convenient. Human activity recognition (HAR) is a fundamental task in these environments. Since critical decisions will be made based on HAR results, accurate recognition of human activities with low uncertainty is of crucial importance. In this paper, a novel HAR method based on a difference of convex programming (DCP) problem is represented, which manages to handle uncertainty. For this purpose, given an input sensor data stream, a primary belief in each activity is calculated for the sensor events. Since the primary beliefs are calculated based on some abstractions, they naturally bear an amount of uncertainty. To mitigate the effect of the uncertainty, a DCP problem is defined and solved to yield secondary beliefs. In this procedure, the uncertainty stemming from a sensor event is alleviated by its neighboring sensor events in the input stream. The final activity inference is based on the secondary beliefs. The proposed method is evaluated using a well-known and publicly available dataset. It is compared to four HAR schemes, which are based on temporal probabilistic graphical models, and a convex optimization-based HAR procedure, as benchmarks. The proposed method outperforms the benchmarks, having an acceptable accuracy of 82.61%, and an average F-measure of 82.3%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.