• Journal of KIISE:Databases
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• v.33 no.2
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• pp.163-174
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• 2006

In distributed wireless 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. We propose a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes input as a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a standard text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Bayesian classifier and SVM, and for unsupervised, we tested Jaccard, TFIDF Jaro and Jaro Winkler. In our experiments, SVM and TFIDF outperformed other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.315-323
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• 2010

In Wireless Sensor Networks (WSNs), various Data-Centric Storages (DCS) schemes have been proposed to store the collected data and to efficiently process a query. A DCS scheme assigns distributed data regions to sensor nodes and stores the collected data to the sensor which is responsible for the data region to process the query efficiently. However, since the whole data stored in a node will be lost when a fault of the node occurs, the accuracy of the query processing becomes low, In this paper, we propose an in-network aggregation query processing method that assures the high accuracy of query result in the case of data loss due to the faults of the nodes in the DCS scheme. When a data loss occurs, the proposed method creates a compensation model for an area of data loss using the linear regression technique and returns the result of the query including the virtual data. It guarantees the query result with high accuracy in spite of the faults of the nodes, To show the superiority of our proposed method, we compare E-KDDCS (KDDCS with the proposed method) with existing DCS schemes without the data-loss correction method. In the result, our proposed method increases accuracy and reduces query processing costs over the existing schemes.

• Convergence Security Journal
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• v.4 no.3
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• pp.57-82
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• 2004

The confluence of computers, communications, and databases is quickly creating a distributed database where many applications require real-time access to temporally consistent sensor data. We have developed an object-oriented real-time database system called BeeHive to provide a significant improvement in performance and functionality over conventional non-real-time database and object management systems. In this paper, the performance of two data-deadline cognizant scheduling policies EDDF and EDF-DC and the baseline EDF policy with/without admission control are evaluated through extensive experiments on BeeHive. The ranges where data-deadline cognizant scheduling policies are effective and where admission control plays a role are identified.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1178-1184
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• 2010

In this paper, a mobile search engine architecture that predicts user's search words and recommends them to users in estimated preference order without having additional user inputs. Information obtained from sensors is first sent to the recommendation engine. The related contents are extracted from the context and history databases and recommending words are selected from the contents. Finally, the words are delivered to the mobile device for suggestion. To evaluate the implemented system response time is measured. A satisfaction survey is also performed for 50 users, and improvement in the proposed system is observed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.337-340
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• 2009

Wireless tele-home-care application gives new possibilities for ECG (electrocardiogram) monitoring system with wearable biomedical sensors. Thus, continuously development of high convenient ECG monitoring system for high-risk cardiac patients is essential. This paper describes to monitor a person's ECG using wearable approach. A wearable belt-type ECG electrode with integrated electronics has been developed and has proven long-term robustness and monitoring of all electrical components. The measured ECG signal is transmitted via an ultra low power consumption wireless sensor node. ECG signals carry a lot clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed thus it bring errors due to motion artifacts and signal size changes. Variable threshold method is used to detect the R-peak which is more accurate and efficient. In order to evaluate the performance analysis, R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research. This concept able to allow patient to follow up critical patients from their home and early detecting rarely occurrences of cardiac arrhythmia.

• Journal of Acupuncture Research
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• v.39 no.2
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• pp.77-88
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• 2022

The purpose of this review was to identify research trends in acupuncture training systems and models and to analyze acupuncture training using phantom models. Articles on acupuncture training were retrieved from domestic and foreign electronic databases (PubMed, CNKI, CiNii, NDSL, KISS, RISS and KMBase). The search included studies conducted from January 1, 2010 to October 1, 2021. Acupuncture training was analyzed by categorization into acupoint location training and needling training. Acupuncture training was most frequently studied in China, acupoint location training was the most studied in 2012, and needling training was the most studied in 2013 and 2020. Among them, a silicone model with a sensor was used for training in acupoint location, and silicone and agarose gel were frequently used for needling training. Classifications of the phantom models for needling training by topic included phantom development, phantom-based education and evaluation system, phantom-based quantitative measurement, comparison of kinematic characteristics of hand motion between experts and beginners, and phantom models for acupoint location and needling training. Further research on the development of acupuncture practice training systems to improve practical skills is needed.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.75-78
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• 2007

최근 센서네트워크에서 수집되는 방대한 양의 데이터를 효율적으로 처리하기 위하여 관계형 데이터베이스를 이용한 센서네트워크가 활발히 연구되고 있다. 센서네트워크에서는 제한된 에너지를 사용한다는 점, 스트림 데이터를 처리할 수 있어야 한다는 점 등에서 기존 데이터베이스와는 다른 연구가 필요하다. 정확히 일치하는 키 값에 대하여만 조인이 발생하는 조인연산 또한 센서네트워크에서 사용하기 위해서는 새로운 정의가 필요하다. 온도센서와 습도센서가 일정영역에 무작위로 뿌려져 있는 센서네트워크를 가정해 보자. 데이터베이스 관점에서는 온도릴레이션과 습도릴레이션이 존재하게 된다. 이때 위치에 따른 온도와 습도의 상관관계를 얻기 위하여 좌표를 키 값으로 하여 릴레이션을 조인하면 결과는 공집합이거나 아주 적은 수의 튜플만 얻게 되어 사용자가 원하는 결과를 얻을 수 없다. 그 이유는 동일한 좌표를 가지는 서로 다른 종류의 센서쌍이 존재할 확률이 매우 적기 때문이다. 본 논문에서는 이러한 문제를 해결하기 위하여 새로운 범위조인연산자를 제안한다. 이 범위조인연산자를 센서네트워크에 적용하면 좀 더 효율적인 데이터관리가 가능하고 데이터베이스에서 응용계층에 표준화된 인터페이스를 제공할 수 있다.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.620-630
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• 2006

Event-detection is an important application of sensor networks. Join operations can facilitate event-detection with a condition table predefined by a user. When join operations are used for event-detection, it is desirable, if possible, to do in-network join processing to reduce communication costs. In this paper, we propose an energy-efficient in-network join algorithm, called PBA. In PBA, each partition of a condition table is stored along the path from each node to the base station, and then in-network joins are performed on the path. Since each node can identify the parts to store in its storage by its level, PBA reduces the cost of disseminating a condition table considerably Moreover, while the existing method does not work well when the ratio of the size of the condition table to the density of the network is a little bit large, our proposed method PBA does not have such a restriction and works efficiently in most cases. The results of experiments show that PBA is efficient usually and especially provides significant cost reduction over existing one when a condition table is relatively large in comparison with the density of the network, or the routing tree of the network is high.

• 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.