• Journal of Korean Library and Information Science Society
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• v.43 no.2
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• pp.247-271
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• 2012

The institutional repository and linked data share a purpose, co-creation and joint usage of information resources. Therefore, a new approach linking these two concepts can be utilized for co-production and utilization of resources. This study hoped to configure the Korean dCollection repository as linked data. For this purpose, first, we analyzed the current data structure of dColleciton. Second, we investigated the resource types which dCollection is targeting. Third, we focused and analyzed a case study of resource types targeted by dCollection constructed as linked data. Fourth, this study examined in detail how to build the linked dCollection data and how to connect this linked data to the linked cloud. Finally, we discussed the problems that might oc cur in the process of building the linked data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.62-65
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• 2018

This paper comes out with the study on sensing data collection strategy in a Software-Defined Mobile Edge vehicular networking. The two cooperative data dissemination are Direct Vehicular cloud mode and edge cell trajectory prediction decision mode. In direct vehicular cloud, the vehicle observe its neighboring vehicles and sets up vehicular cloud for cooperative sensing data collection, the data collection output can be transmitted from vehicles participating in the cooperative sensing data collection computation to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation out-put and send to the closest RSU. The SDMEVN (Software Defined Mobile Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution.

• 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.

• Wind and Structures
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• v.2 no.3
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• pp.133-150
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• 1999

Significant costs to the public and private sectors due to recent extreme wind events have motivated the need for systematic post-hurricane damage data collection and analysis. Current post disaster data are collected by many different interested groups such as government agencies, voluntary disaster relief agencies, representatives of media companies, academicians and companies in the private sector. Each group has an interest in a particular type of data. However, members of each group collect data using different techniques. This disparity in data is not conducive to quantifying damage data and, therefore, inhibits the statistical and spatial description of damage and comparisons of damage among different extreme wind events. The data collection does not allow comparisons of data or results of analyses within a group and also prohibits comparison of damage data and information among different groups. Typically, analyses of data from a given event lead to different conclusion depending upon the definition of damage used by individual investigators and the type of data collected making it difficult for members of groups to compare the results of their analyses with a common language and basis. A formal method of data collection and analysis-within any single group-would allow comparisons to be made among different individuals, hazardous events and eventually among different groups, thus facilitating the management and reduction of damage due to future disaster. This research introduces a definition of damage to single family dwellings, and a common method of data collection and analysis suited for groups interested in regional characterization of damage. The current state-of-data is presented and a method for data collection is recommended based on these existing data collection methods. A fixed-scale damage index is proposed to consider the damage to a dwelling's feature. Finally, the damage index is applied to three dwellings damaged by Hurricane Iniki (1992). The damage index reflects the reduced functionality of a structure as a single family detached dwelling and provides a means to evaluate regional damage due to a single event or to compare damage due to events of different severity. Evaluation of the damage index and the data available support recommendation for future data collection efforts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.3
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• pp.199-206
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• 2013

This paper proposes a modified tag collection algorithm that improves the drawback of the identified slot scan-based tag collection algorithm presented in a previous paper to improve the tag collection performance in active RFID systems. The previous identified slot scan-based tag collection algorithm is optimized in situations where all the tags store the fixed size of data, so it could not result in a good performance improvement with tags having the variable size of data. The improved tag collection algorithm proposed in this paper first collects the slot size information required for the data transmission from each tag via the identified slot scan phase, and then performs the tag collection phase using the information, which resolves the problem of the previous identified slot scan-based tag collection algorithm. The simulation results for performance evaluation showed that the proposed tag collection algorithm resulted in the almost same tag collection performance as the previous algorithm when all the tags have the same size of data and led a large improvement of the tag collection performance in ISO/IEC 18000-7 unlike the previous algorithm when each tag has a random size of data.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.479-488
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• 2022

In recent years, with the rapid development of machine learning technology, research utilizing machine learning has been actively conducted in fields such as cognition, reasoning and judgment, and action among various technologies constituting intelligent systems. In order to utilize this machine learning, it is indispensable to collect data for learning. However, the types of data generated vary according to the environment in which the data is generated, and the types and forms of data required are different depending on the learning model to be used for machine learning. Due to this, there is a problem that the existing data collection method cannot be reused in a new environment, and a specialized data collection module must be developed each time. In this paper, we propose a specification-based methology for data collection in artificial intelligence system to solve the above problems, ensure the reusability of the data collection method according to the data collection environment, and automate the implementation of the data collection function.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.51-58
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• 2010

In data collection using a mobile sink, the time that sensor nodes are included in its communication radius is not uniform. The data collection schedule in non-uniform time is needed between a mobile sink and sensor nodes for efficient data collection. The existing data collection schemes using a mobile sink considered staying time in its communication range and data collected by the mobile sink. However, they did not consider the characteristics of data collected in sensor networks. In this paper, we propose a TDMA based schedule scheme that consists of the data collection period by each sensor nodes and the data collection period between a mobile sink and sensor nodes. Moreover, we propose a data collection scheme considering the variability of data in sensor networks. The proposed data collection scheme collects only data that changed larger than the threshold set by the user. In order to show the superiority of the proposed scheme, we compare it with DWEDF that aims to collect data uniformly. As a result, our experimental results show that the proposed scheme reduces about 23% energy consumption and the data collection failure of sensor nodes over the DWEDF.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.2
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• pp.125-134
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• 2016

NAND flash memory has unique characteristics like as 'out-place-update' and limited lifetime compared with traditional storage systems. According to out-of-place update scheme, a number of invalid (or called dead) pages can be generated. In this case, garbage collection is needed to reclaim invalid pages. Because garbage collection results in not only erase operations but also copy operations of valid (or called live) pages to other blocks, many garbage collection techniques have proposed to reduce the overhead and to increase the lifetime of NAND Flash systems. This techniques sometimes select victim blocks including cold data for the wear leveling. However, most of them overlook the cost of selecting victim blocks including cold data. In this paper, we propose a garbage collection technique named CAPi (Cost Age with Proportion of invalid pages). Considering the additional overhead of what to select victim blocks including cold data, CAPi improves the response time in garbage collection and increase the lifetime in memory systems. Additionally, the proposed scheme also improves the efficiency of garbage collection by separating cold data from hot data in valid pages. In experimental evaluation, we showed that CAPi yields up to, at maximum, 73% improvement in lifetime compared with existing garbage collections.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.4
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• pp.206-216
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• 2010

Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. A spatial clustering may reduce energy consumption of data collection by partitioning the WSN into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to a base station (BS). The BS may predict the missed data of non-samplers using the spatial correlations between sensor nodes. ASAP is a representative data collection algorithm using the spatial clustering. It periodically reconstructs the entire network into new clusters to accommodate to the change of spatial correlations, which results in high message overhead. In this paper, we propose a new data collection algorithm, name EPDC (Energy-efficient Periodic Data Collection). Unlike ASAP, EPDC identifies a specific cluster consisting of many dissimilar sensor nodes. Then it reconstructs only the cluster into subclusters each of which includes strongly correlated sensor nodes. EPDC also tries to reduce the message overhead by incorporating a judicious probabilistic model transfer method. We evaluate the performance of EPDC and ASAP using a simulation model. The experiment results show that the performance improvement of EPDC is up to 84% compared to ASAP.