• The Journal of Economics, Marketing and Management
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• v.11 no.4
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• pp.1-10
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• 2023

Purpose: This study investigated the relationship between product characteristics and customer purchase intention in live-streaming commerce. Research design, data and methodology: Six independent factors namely, scarcity, customization, discount, experimentalism, novelty, and informativeness were identified to analyze their effects on customer purchase intention in live-streaming commerce. The perceived value was accepted as a mediator between independent and dependent variables. Data were gathered from 643 respondents who experienced purchases in live-streaming commerce in China. Results: The results show that product characteristics strongly affect customer purchase intention, and perceived value plays an important mediating role in live-streaming commerce. Therefore, when developing a sales strategy in live-streaming commerce, product characteristics. Such as customization, discount, experimentalism, novelty, and information must be considered. Conclusions: The majority of live-streaming commerce research has focused on platform interactions and consumers. This study is meaningful in that it dealt with product characteristics and confirmed the mediating roles of perceived value in live-streaming commerce. The findings of this study have significant implications and offer valuable insights and practical guidance for both the academic community and practitioners engaged in the field of live-streaming commerce.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.569-578
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• 2016

With the recent advancement of surveying and technology, the spatial data acquisition rates and precision have been improved continually. As the updates of spatial data are rapid, and the size of data increases in line with the advancing technology, the LOD (Level of Detail) algorithm has been adopted to process data expressions in real time in a streaming format with spatial data divided precisely into separate steps. The existing GRID analysis utilizes the single DEM, as it is, in examining and analyzing all data outside the analysis area as well, which results in extending the analysis time in proportion to the quantity of data. Hence, this study suggests a method to reduce analysis time and data throughput by acquiring and analyzing DEM data necessary for GRID analysis in real time based on the area of analysis and the level of precision, specifically for streaming DEM data, which is utilized mostly for 3D geographic information service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2268-2283
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• 2013

Scalable video coding can handle peer heterogeneity of P2P streaming applications, but there is still a lack of comprehensive studies on how to use it to improve video playback quality. In this paper we propose a capacity aware scalable video coding mechanism for P2P on demand streaming system. The proposed mechanism includes capacity based neighbor selection, adaptive data scheduling and streaming layer adjustment, and can enable each peer to select appropriate streaming layers and acquire streaming chunks with proper sequence, along with choosing specific peers to provide them. Simulation results show that the presented mechanism can decrease the system's startup and playback delay, and increase the video playback quality as well as playback continuity, and thus it provides a better quality of experience for users.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.94-100
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• 2010

Data Mining is mainly used for pattern extracting and information discovery from collected data. However previous methods is difficult to reflect changing patterns with time. In this paper, we introduce Streaming Decision Tree(SDT) analyzing data with continuity, large scale, and changed patterns. SDT defines continuity data as blocks and extracts rules using a Decision Tree's learning method. The extracted rules are combined considering time of occurrence, frequency, and contradiction. In experiment, we applied time series data and confirmed resonable result.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.538-549
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• 2021

In this paper, we introduce a technique for 360-degree panoramic video streaming with multiple virtual cameras in real-time. The proposed technique consists of generating 360-degree panoramic video data by ORB feature point detection, texture transformation, panoramic video data compression, and RTSP-based video streaming transmission. Especially, the generating process of 360-degree panoramic video data and texture transformation are accelerated by CUDA for complex processing such as camera calibration, stitching, blending, encoding. Our experiment evaluated the frames per second (fps) of the transmitted 360-degree panoramic video. Experimental results verified that our technique takes at least 30fps at 4K output resolution, which indicates that it can both generates and transmits 360-degree panoramic video data in real time.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.35-42
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• 2018

In this paper, we propose an optimal streaming service method based on Hybrid CDN/P2P architecture. Recently, video streaming utilizes a CDN (Content Delivery Network) operation technique based on a Proxy Server, which is an end node located close to a user. However, since CDN has a fixed network traffic bandwidth and data information exchange among CDNs in the network is not smooth, it is difficult to guarantee traffic congestion and quality of image service. In the hybrid CDN/P2P network, a data selection technique is used to select only the data that is expected to be continuously requested among all the data in order to guarantee the QoS of the user who utilizes the limited bandwidth efficiently. In order to search user requested data, this technique effectively retrieves the storage information of the constituent nodes of CDN and P2P, and stores the new image information and calculates the deletion priority based on the request possibility as needed. Therefore, the streaming service scheme proposed in this paper can effectively improve the quality of the video streaming service on the network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9A
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• pp.746-753
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• 2003

In this paper, we propose an efficient algorithm for non-streaming synchronized data processing based on ATSC-DASE in terrestrial digital data broadcasting services. Non-streaming synchronized data is encapsulated in DSM-CC sections with PTS(presentation time stamp) values associated with A/V and it is transmitted in a form of MPEG-2 TS(transport stream). At the receiver, the transmitted A/V data are processed by PC based set-top box(STB) in real-time, and the transmitted non-streaming synchronized data is also stored at the STB and is displayed at right time by the proposed algorithm. To verity the proper operation of the proposed algorithm, we make a scenario for non-streaming synchronized data by XML, and finally we are able to display it properly by using declarative application(DA) browser.

• Journal of KIISE
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• v.44 no.5
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• pp.460-468
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• 2017

In a real-time cloud environment, the data analysis framework plays a pivotal role. Spark Streaming meets most real-time requirements among existing frameworks. However, the framework does not meet the second scale real-time fault recovery requirement. Spark Streaming fault recovery time increases in proportion to the transformation history length called lineage. This is because it recovers the last state data based on the cumulative lineage recorded during normal operation. Therefore, fault recovery time is not bounded within a limited time. In addition, it is impossible to achieve a second-scale fault recovery time because it costs tens of seconds to read initial state data from fault-tolerant storage. In this paper, we propose two techniques to solve the problems mentioned above. We apply the proposed techniques to Spark Streaming 1.6.2. Experimental results show that the fault recovery time is bounded and the average fault recovery time is reduced by up to 41.57%.

• Journal of Information Technology Applications and Management
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• v.27 no.6
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• pp.31-51
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• 2020

During the last decade, social live streaming service like Periscope, Ustream, and YouNow has developed from a niche market into a mainstream activity. In this media environment, social live streaming service has a tremendous impact on the social behaviors of users. Despite the rapid development, there are a lack of studies to make better understand the media environment changes through social live streaming service. This study adopted an affordances approach that leads us to identify six distinctive social affordances (visibility, accessibility, information sharing, social interaction, role-taking, interactive revenue) for user engagement in social live streaming service. Specifically, this study explores the impact of social affordances on perceived flow, followed by user engagement including passive and active engagement. Empirical data analysis with 258 questionnaires suggests that social affordances affected users' flow perception, and flow has an effect on active as well as passive engagement. Contrary to the expectation in a hypothesized research model, only the impact of accessibility on flow was rejected.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.361-368
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• 2016

While dimension reduction methods on high dimensional data have been widely studied, research on dimension reduction methods for high dimensional streaming data with concept drift is limited. In this paper, we review incremental dimension reduction methods and propose a method to apply dimension reduction efficiently in order to improve classification performance on high dimensional streaming data with concept drift.