• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.574-576
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• 2021

It is necessary to introduce micro-service techniques when constructing large-scale operating systems or systems that take into account scalability. Kafka is a message queue with the pub/sub model, which has features that are well applied to distributed environments and is also suitable for microservices in that it can utilize various data sources. In this paper, we propose a data sharing method for educational video sharing services using Apache's Kafka. The proposed system builds a Kafka cluster for the educational video sharing service to share various data, and also uses a spark cluster to link with recommendation systems based on similarities in educational videos. We also present a way to share various data sources, such as files, various DBMS, etc.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.709-713
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• 2009

Recently, a lot of research that applies data acquired from devices such as cameras and RFIDs to context aware services is being performed in the field on Life-Log and the sensor network. A variety of analytical techniques has been proposed to recognize various information from the raw data because video and audio data include a larger volume of information than other sensor data. However, manually watching a huge amount of media data again has been necessary to create supervised data for the update of a class or the addition of a new class because these techniques generally use supervised learning. Therefore, the problem was that applications were able to use only recognition function based on fixed supervised data in most cases. Then, we proposed a method of acquiring supervised data from a video sharing site where users give comments on any video scene because those sites are remarkably popular and, therefore, many comments are generated. In the first step of this method, words with a high utility value are extracted by filtering the comment about the video. Second, the set of feature data in the time series is calculated by applying functions, which extract various feature data, to media data. Finally, our learning system calculates the correlation coefficient by using the above-mentioned two kinds of data, and the correlation coefficient is stored in the DB of the system. Various other applications contain a recognition function that is used to generate collective intelligence based on Web comments, by applying this correlation coefficient to new media data. In addition, flexible recognition that adjusts to a new object becomes possible by regularly acquiring and learning both media data and comments from a video sharing site while reducing work by manual operation. As a result, recognition of not only the name of the seen object but also indirect information, e.g. the impression or the action toward the object, was enabled.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3357-3376
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• 2015

Wireless sensor network is an important research topic that has attracted a lot of attention in recent years. However, most of the interest has focused on wireless sensor network to gather scalar data such as temperature, humidity and vibration. Scalar data are insufficient for diverse applications such as video surveillance, target recognition and traffic monitoring. However, if we use camera sensors in wireless sensor network to collect video data which are vast in information, they can provide important visual information. Video sensor networks continue to gain interest due to their ability to collect video information for a wide range of applications in the past few years. However, how to efficiently store the massive data that reflect environmental state of different times in video sensor network and how to quickly search interested information from them are challenging issues in current research, especially when the sensor network environment is complicated. Therefore, in this paper, we propose a fast algorithm for extracting key frames from video and describe the design and implementation of key frame extraction and sharing in Android for wireless video sensor network.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.405-412
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• 2014

Due to the popularity of mobile phones, Peer-to-Peer (P2P) content sharing in mobile phones is highly desirable, especially in a mobile data connection. In addition, there has been an increasing demand for content sharing applications for P2P communication in Android-based smartphones as ubiquitous mobile devices. However, due to wireless network limits, unstable characteristic and restricted storage space, mobile users confront challenges in establishing connections and sharing video frames with other users. To solve this issue, we propose an architectural design and implementation of key frame sharing application that uses Chord, a distributed lookup protocol to establish a content sharing platform for mobile devices, combined with the Chord SDK based on Android. Our proposed system enables users easily to share key information on video data in real-time over a wireless network without manual processing of devices which join or leave the group and cost associated with the 3G/4G network.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.213-223
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• 2019

This study aims to prove the relationship between a TPB(theory of planned behavior) variables (subjective norms, attitudes, and self-efficacy), need to belong and personal growth initiatives to explain the reasons for users' shared behavior. 959 participants who had shared online video were collected as a sample through an online survey and the collected data were analyzed through structural equation modeling. The study found that need to belong affected attitudes to online video sharing and subjective norms, and that personal growth initiative also affected attitudes to online video sharing and self-efficacy. In addition, all three variables of TPB were affect the intend of online video sharing, and attitudes to online video sharing were affecting subjective norms and self-efficacy. This study is meaningful in that it demonstrated the user's intention to share online video through variables of TPB along with their need to belong and personal growth initiatives.

• Asia pacific journal of information systems
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• v.25 no.1
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• pp.119-141
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• 2015

With the rapid spread of social media, video-sharing social media like YouTube has emerged as a consumption and distribution channel for entertainment goods such as music videos and movie trailers. In tourism research, there has been a lot of research of how the visual media such as movies and soap operas induced tourism. However, no studies have attempted to examine the role of social media as a music consumption channel and its impact on tourism. Expanding a body of media-induced tourism, we analysed the impact of video-sharing social media on music-induced tourism with a case of Korean pop music and inbound tourism to Korea. Developing a Web-crawler, we collected YouTube users' comments data on 166 Korean pop music video clips which were released from 2009 to 2012 with over 1 million view counts. Controlling many of the determinants of tourism and analysing country-by-country impact of YouTube comments with the panel data, we found that engagement of Korean pop music video clips on YouTube is a significant predictor for the flow of inbound tourists to Korea.

• Smart Media Journal
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• v.12 no.11
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• pp.145-153
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• 2023

In a P2P-based distributed storage system where peers frequently join and leave, it is essential to guarantee not only data availability but also playback quality comparable to that provided by local storage devices when playing back video files with real-time constraints. In addition, cloud storage services based on distributed storage systems provide each user with the functionality to share their files with other users, so when multiple users request playback of the same video file at the same time, all playback should be supported seamlessly in real time. Therefore, in this paper, we propose a scheme that process multiple simultaneous playback requests for each video file in real time as well as data availability in a P2P-based distributed storage system that supports file sharing. This scheme can support real-time simultaneous playback and efficiently use storage space by adjusting the amount of redundant data encoded through erasure coding according to the number of concurrent playback requests for each video file.

• Journal of Platform Technology
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• v.11 no.1
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• pp.11-22
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• 2023

Various video conferencing and collaboration applications have emerged due to the global epidemic of new viral infections. In addition to real-time video conferencing, video conferencing applications provide features such as chat and file sharing on various platforms. Because various personal information is stored through functions such as chatting, file and screen sharing, these video conferencing applications are the major target of analysis from a digital forensic investigation. In the case of applications that provide cross-platform, the form of stored data is different depending on the platform. Therefore, to utilize data of video conferencing application for forensic investigation, preliminary research on artifacts stored by platform is required. In this paper, we used the video conferencing applications GoToWebinar and GoToMeeting and analyzed the artifacts generated. As a result, we list the main data from a digital forensic investigation. We identify data stored for each platform provided by GoToWebinar and GoToMeeting and organize artifacts that can estimate user behavior. Also, we classify the data that can be acquired according to the role and environment within the video conference.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.81-93
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• 2023

The popularity of short-form video platforms like TikTok has increased recently. Short-form videos are significantly shorter than traditional videos, and viewers regularly switch between different types of content to watch. Therefore, a successful prefetching strategy is essential for this novel type of video. This study provides a resource-effective prefetching technique for streaming short-form videos. The suggested solution dynamically adjusts the quantity of prefetched video data based on user viewing habits and network traffic conditions. The results of the experiments demonstrate that, in comparison to baseline approaches, our method may reduce data waste by 21% to 83%, start-up latency by 50% to 99%, and the total time of Re-buffering by 90% to 99%.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.357-367
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• 2004

In VOD systems the number of channels available to a video server is limited since the number of channels, the video server can support, is determined by the communication bandwidth of the video server. Multicast techniques such as batching, patching and batch-patching have been proposed to reduce I/O demand on the video server by sharing multicast data. In this paper, we propose a channel reservation batch-patching technique that first applies the batching technique to hot video requests and then applies the patching technique to the batches of the hot video requests. And the proposed technique reserves a part of the channel capacity of the video server for many hot video requests, so that the hot video requests can be served without defection. The performance of the proposed technique is compared with those of Patching and Batch-Patching techniques in terms of average service latency, defection rate, fairness and frame reduction rate by simulations.