• KIPS Transactions on Computer and Communication Systems
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• v.2 no.11
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• pp.461-470
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• 2013

This paper proposes high speed kernel data collection method for analysis of memory workload, using technique of direct access to process's memory management structure. The conventional analysis tools have a slower data collection speed and they are lack of scalability due to collection only formalized memory information. The proposed method collects kernel data much faster than the conventional methods using technique of direct collect to process's memory information, page table, page structure in the memory management structure, and it can collect data which user wanted. We collect memory management data of the running process, and analyze its memory workload.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.319-322
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• 2019

There are many techniques for locating users in an indoor spot. Among them, WiFi fingerprinting technique which is widely used is phased into a data collection step and a positioning step. In the data collection step, all surrounding Wi-Fi signals are collected and managed as a list. The more data collected, the better the accuracy of the indoor position based on Wi-Fi fingerprint. Existing high-quality data collection and management methods are time consuming and costly, and many operations are required to extract and generate data necessary for machine learning. Therefore, we research how to collect and manage large amount of data in limited resources. This paper presents efficient data collection methods and data generation for learning.

• Education of Primary School Mathematics
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• v.25 no.4
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• pp.329-341
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• 2022

Data collection is crucial to the process of statistical problem solving since it influences the quality of statistical data. However, there is little instruction on data collection in the Korean mathematics curriculum. In this study, we examined how the data were collected and how the data collection method was taught in the Korean mathematics textbooks for 3rd and 4th grades. As a result, the data appeared in these textbooks were collected by using a variety of methods, including surveys, experiments, observations, and secondary data collections. There were not enough instructions on experiments and observations, compared to surveys and secondary data collection. Additionally, as each textbook works with a distinct contents while teaching data collection, it is expected that there would be variations in the levels that students learn in relation to data collection. Based on these findings, we draw some discussion points to determine how to improve the mathematics curriculum in order to effectively teach data collection in the elementary school.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.69-76
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• 2024

Recent advances in sensor and mobile technologies have made it possible to collect user location data. This location information is used as a valuable asset in various industries, resulting in increased demand for location data collection and sharing. However, because location data contains sensitive user information, indiscriminate collection can lead to privacy issues. Recently, geo-indistinguishability (Geo-I), a method of differential privacy, has been widely used to protect the privacy of location data. While Geo-I is powerful in effectively protecting users' locations, it poses a problem because the utility of the collected location data decreases due to data perturbation. Therefore, this paper proposes a method using Geo-I technology to effectively collect user location data while maintaining its data utility. The proposed method utilizes the prior distribution of users to improve the overall data utility, while protecting accurate location information. Experimental results using real data show that the proposed method significantly improves the usefulness of the collected data compared to existing methods.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.43-49
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• 2019

Today, with the development of the internet of things, wearable devices related to personal health care have become widespread. Various global information and communication technology companies are developing various wearable health devices, which can collect personal health information such as heart rate, steps, and calories, using sensors built into the device. However, since individual health data includes sensitive information, the collection of irrelevant health data can lead to personal privacy issue. Therefore, there is a growing need to develop technology for collecting sensitive health data from wearable health devices, while preserving privacy. In recent years, local differential privacy (LDP), which enables sensitive data collection while preserving privacy, has attracted much attention. In this paper, we develop a technology for collecting vast amount of health data from a smartwatch device, which is one of popular wearable health devices, using local difference privacy. Experiment results with real data show that the proposed method is able to effectively collect sensitive health data from smartwatch users, while preserving privacy.

• Journal of Korean Society of Water and Wastewater
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• v.24 no.5
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• pp.573-580
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• 2010

Average pressure in a pipe network is one of critical factors to estimate the flow distribution and to calculate UARL (Unavoidable Annual Real Losses) value in ILI (Infrastructure Leakage Index). While its collection period and measuring location are essential to obtain average pressure, their standard method have not been established so far. In this study, proper method including its procedure for data collection period and measuring point for average pressure were suggested using non-exceedance probability concept in the water distribution network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3396-3411
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• 2015

Sensors used in Wireless Sensor Networks (WSN) have mostly limited capacity which affects the performance of their applications. One of the data-gathering methods is to use mobile sinks to visit these sensors so that they can save their limited battery energies from forwarding data packages to static sinks. The main disadvantage of employing mobile sinks is the delay of data collection due to relative low speed of mobile sinks. Since sensors have very limited memory capacities, whenever a mobile sink is too late to visit a sensor, that sensor's memory would be full, which is called a 'memory overflow', and thus, needs to be purged, which causes loss of collected data. In this work, a method is proposed to generate mobile sink tours, such that the number of overflows and the amount of lost data are minimized. Moreover, the proposed method does not need either the sensor locations or sensor memory status in advance. Hence, the overhead stemmed from the information exchange of these requirements are avoided. The proposed method is compared with a previously published heuristic. The simulation experiment results show the success of the proposed method over the rival heuristic with respect to the considered metrics under various parameters.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.91-100
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• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.181-186
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• 2006

Estimation based on current data of construction performances have become one of the critical subjects which many researchers have been interested in for the past decades. In order to accomplish accurate measurement and estimation of construction performances, the method of data collection stands the highest priority. However, there are many difficulties in data collection from construction jobsite due to the characteristics of the construction industry. With developments of new technologies in other industries, several technologies has recently initiated to be applied to construction field. Electronic tags based on the identification technology, automatic volume measurement based on laser scanning technology, and Global Positioning System (GPS) have been represented the technologies which show the high opportunity for being used in construction. This study reviews specific aspects of these technologies focused on the utilization in construction jobsite. Also, the challenges which these technologies need to overcome are discussed.

• The Korean Journal of Community Living Science
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• v.25 no.4
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• pp.449-460
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• 2014

This study examines research trends in Korean journal articles covering the role of fathers with young children. For this study, 45 research papers published from 2000 to present were analyzed according to research periods, research topics, research types, data collection methods, and data analysis methods. First, the largest number of papers was written since 2010. Second, the largest number of papers in terms of research topics focused on the father's child-rearing involvement and behavior. Third the most frequently used research type was the quantitative study. Fourth, the most frequently used data collection method was the questionnaire method. Fifth, the most frequently used data analysis method was the frequency and mean method. Future research should consider broader age groups of father and children by using various types of data collection and analysis methods. In addition, it should be useful to scrutinize general research trends in Korean journal articles highlighting the importance of roles of fathers with young children in a rapidly changing society.