• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.353-356
• /
• 2007

With advances in sensor technology, current researches on the pertinent techniques are actively directed toward the way which enables the USN computing service. For many applications using sensor networks, the incoming data are by nature characterized as high-speed, continuous, real-time and infinite. Due to such uniqueness of sensor data characteristics, for some instances a finite-sized buffer may not accommodate the entire incoming data, which leads to inevitable loss of data, and requirement for fast processing makes it impossible to conduct a thorough investigation of data. In addition to the potential problem of loss of data, incoming data in its raw form may exhibit high degree of complexity which evades simple query or alerting services for capturing and extracting useful information. Furthermore, as traditional mining techniques are developed to handle fixed, static historical data, they are not useful and directly applicable for analyzing the sensor data. In this paper, (1) describe how three mining techniques (sensor data outlier analysis, sensor pattern analysis, and sensor data prediction analysis) are appropriate for the USN middleware structure, with their application to the stream data in ocean environment. (2) Another proposal is a middleware structure based on USN environment adaptive to above mining techniques. This middleware structure includes sensor nodes, sensor network common interface, sensor data processor, sensor query processor, database, sensor data mining engine, user interface and so on.

• Journal of Intelligence and Information Systems
• /
• v.15 no.3
• /
• pp.67-80
• /
• 2009

By a number of sensors and sensor networks, we can collect environmental information from a certain sensor space. To discover more useful information and knowledge, we want to employ data mining methodologies to sensor data stream from such sensor spaces. In this paper, we present a novel data preprocessing scheme to improve the performances of the data mining algorithms. Especially, ontologies are applied to represent meanings of the sensor data. For evaluating the proposed method, we have collected sensor streams for about 30 days, and simulated them to compare with other approaches.

• Journal of Multimedia Information System
• /
• v.2 no.2
• /
• pp.233-240
• /
• 2015

Mobile devices are becoming increasingly sophisticated and the latest generation of smartphones now incorporates many diverse and powerful sensors. These sensors include acceleration sensor, magnetic field sensor, light sensor, proximity sensor, gyroscope sensor, pressure sensor, rotation vector sensor, gravity sensor and orientation sensor. The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper, we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity that a user is performing. To implement our system, we collected labeled accelerometer data from 10 users as they performed daily activities such as "phone detached", "idle", "walking", "running", and "jumping", and then aggregated this time series data into examples that summarize the user activity 5-minute intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users-just by having them carry cell phones in their pockets.

• Spatial Information Research
• /
• v.19 no.1
• /
• pp.127-133
• /
• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.3
• /
• pp.312-317
• /
• 2005

Ubiquitous computing has had an effect to politics, economics, society, culture, education and so forth. For effective management of huge Ubiquitous networks environment, various computers which are connected to networks has to decide automatic optimum with intelligence. Currently in many areas, data mining has been used effectively to construct intelligent systems. We proposed a hybrid support vector machine for Ubiquitous data mining which realized intelligent Ubiquitous computing environment. Many data were collected by sensor networks in Ubiquitous computing environment. There are many noises in these data. The aim of proposed method was to eliminate noises from stream data according to sensor networks. In experiment, we verified the performance of our proposed method by simulation data for Ubiquitous sensor networks.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2018.07a
• /
• pp.37-38
• /
• 2018

최근 인터넷 기술의 발달로 다수의 센서 노드로 구성된 Wireless Sensor Network (WSN) 환경이 증가하고 있으며, 이에 따라 무선으로 연결된 수많은 노드에 의해 생성되는 데이터의 양이 방대해지고 있지만, 데이터의 특성 및 패턴이 불규칙하여 기존 정적 분류 기법으로는 한계가 있다. 따라서 본 논문에서는 이러한 WSN 환경에서 생성되는 방대한 양의 데이터를 효율적으로 처리하기 위해 기계학습을 이용한 데이터 마이닝(Data mining) 기법에 대해 서술한다. 데이터 마이닝이란 데이터의 패턴 및 데이터 간의 관계를 이용하여 의사결정에 필요한 정보를 추출하는 것으로 다양한 기계 학습 알고리즘이 존재한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.238-241
• /
• 2019

In this paper, a "Vehicle Analysis System(VAS) using cloud and data mining" is proposed that store all the sensor data measured in the vehicle in the cloud, analyze the stored data using the classification model, and provide the analyzed data in real time to the driver's display. The VAS consists of two modules. First, Sensor Data Communication Module(SDCM) stores the sensor data measured in the vehicle in a table of the cloud server and transfers the stored data to the analysis module. Second, Sensor Data Analysis Module(SDAM) analyzes the received data using the genetic algorithm and provides analyzed result to the driver in real time. The VAS stores sensor data collected in the vehicle in the cloud server without accumulating it in the vehicle, and stored data is analyzed in the cloud server, so that the sensor data can be quickly and efficiently managed without overloading the vehicle. In addition, the information desired by the driver can be visualized on the display, thereby increasing the stability of the autonomous vehicle.

• Tunnel and Underground Space
• /
• v.28 no.6
• /
• pp.547-554
• /
• 2018

Using crowdsourced sensor data collection technique, it is possible to collect high-density ground vibration data which is difficult to obtain by conventional methods. In this study, we have developed a crowdsourced ground vibration data collection system using MEMS sensors mounted on small electronic devices including smartphones, and implemented client and server based on the proposed infrastructure system design. The system is designed to gather vibration data quickly through Android-based smartphones or fixed devices based on Android Things, minimizing the usage of resource like power usage and data transmission traffic of the hardware.

• Journal of Multimedia Information System
• /
• v.1 no.1
• /
• pp.67-75
• /
• 2014

Nowadays as video services grow rapidly, it is important for the service providers to provide customized services. Video ranking plays a key role for the service providers to attract the subscribers. In this paper we propose a weekly video ranking mechanism based on the quantified user engagement. The traditional QoE ranking mechanism is relatively subjective and usually is accomplished by grading, while QoS is relatively objective and is accomplished by analyzing the quality metrics. The goal of this paper is to establish a ranking mechanism which combines the both advantages of QoS and QoE according to the third-party data collection platform. We use data mining method to classify and analyze the collected data. In order to apply into the actual situation, we first group the videos and then use the regression tree and the decision tree (CART) to narrow down the number of them to a reasonable scale. After that we introduce the analytic hierarchy process (AHP) model and use Elo rating system to improve the fairness of our system. Questionnaire results verify that the proposed solution not only simplifies the computation but also increases the credibility of the system.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.3
• /
• pp.145-153
• /
• 2009

Trajectory data are ubiquitous in the real world. Recent progress on satellite, sensor, RFID, video, and wireless technologies has made it possible to systematically track object movements and collect huge amounts of trajectory data. Accordingly, there is an ever-increasing interest in performing data analysis over trajectory data. In this paper, we develop a data mining tool for massive trajectory data. This mining tool supports three operations, clustering, classification, and outlier detection, which are the most widely used ones. Trajectory clustering discovers common movement patterns, trajectory classification predicts the class labels of moving objects based on their trajectories, and trajectory outlier detection finds trajectories that are grossly different from or inconsistent with the remaining set of trajectories. The primary advantage of the mining tool is to take advantage of the information of partial trajectories in the process of data mining. The effectiveness of the mining tool is shown using various real trajectory data sets. We believe that we have provided practical software for trajectory data mining which can be used in many real applications.