• Spatial Information Research
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• v.17 no.1
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• pp.67-77
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• 2009

Mobile mapping systems with CCD cameras, GPS and IMU etc. can acquire massive photos and geographic informations along by roads. But it is easy to involve many errors or omissions of images and informations about roads and facilities with various files. And there were contained any conflicts or non-consistencies in massive mobile mapping data which were acquired by multiple survey teams in various survey regions. As an image tag standard, Exif helps us to encapsulate the precise GPS times and essential informations in the header of JPEG files and uses with the identification code for consistent managements of massive mobile mapping data in this paper. And Systematic management systems with data synchronization technology manage more consistently massive photos and their information.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.224-224
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• 2008

Recent advances in model acquisition, computer-aided design, and simulation technologies have resulted in massive databases of complex geometric data occupying multiple gigabytes and even terabytes. In various graphics/geometric applications, the major performance bottleneck is typically in accessing these massive geometric data due to the high complexity of such massive geometric data sets. However, there has been a consistent lower growth rate of data access speed compared to that of computational processing speed. Moreover, recent multi-core architectures aggravate this phenomenon. Therefore, it is expected that the current architecture improvement does not offer the solution to the problem of dealing with ever growing massive geometric data, especially in the case of using commodity hardware. In this tutorial, I will focus on two orthogonal approaches--multi-resolution and cache-coherent layout techniques--to design scalable graphics/geometric algorithms. First, I will discuss multi-resolution techniques that reduce the amount of data necessary for performing geometric methods within an error bound. Second, I will explain cache-coherent layouts that improve the cache utilization of runtime geometric applications. I have applied these two techniques into rendering, collision detection, and iso-surface extractions and, thereby, have been able to achieve significant performance improvement. I will show live demonstrations of view-dependent rendering and collision detection between massive models consisting of tens of millions of triangles on a laptop during the talk.

• Journal of Korea Multimedia Society
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• v.18 no.8
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• pp.915-924
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• 2015

With growing popularity of cloud computing services, users can more easily manage massive amount of data by outsourcing them to the cloud, or more efficiently analyse large amount of data by leveraging IT infrastructure provided by the cloud. This, however, brings the security concerns of sensitive data. To provide data security, it is essential to encrypt sensitive data before uploading it to cloud computing services. Although data encryption helps provide data security, it negatively affects the performance of massive data analytics because it forbids the use of index and mathematical operation on encrypted data. Thus, in this paper, we propose a novel algorithm which enables to efficiently process a large amount of encrypted data. In particular, we propose a novel top-k processing algorithm on the massive amount of encrypted data in the cloud computing environments, and verify the performance of the proposed approach with real data experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.57-60
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• 2015

Recently, a multi-user massive MIMO (MU-Massive MIMO) network has been attracting tremendous interest as one of technologies to accommodate explosively increasing mobile data traffic. The MU-Massive MIMO network can significantly enhance the network capacity because a base station (BS) equipped with large-scale transmit antennas can transmit high-rate data to multiple users simultaneously. In the MU-Massive MIMO network, transmit antenna selection schemes are generally used to decrease the computational complexity and cost of the BS. In this paper, we investigate the energy efficiency of the transmit antenna selection scheme in the MU-Massive MIMO network and the optimal number of selected transmit antennas for maximizing the energy efficiency.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.19-27
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• 2010

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.

• The Journal of Bigdata
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• v.1 no.2
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• pp.1-8
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• 2016

User activity log includes lots of hidden information, however it is not structured and too massive to process data, so there are lots of parts uncovered yet. Especially, it includes time series data. We can reveal lots of parts using it. But we cannot use log data directly to analyze users' behaviors. In order to analyze user activity model, it needs transformation process through extra framework. Due to these things, we need to figure out user activity model analysis framework first and access to data. In this paper, we suggest a novel framework model in order to analyze user activity model effectively. This model includes MapReduce process for analyzing massive data quickly in the distributed environment and data architecture design for analyzing user activity model. Also we explained data model in detail based on real online service log design. Through this process, we describe which analysis model is fit for specific data model. It raises understanding of processing massive log and designing analysis model.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.41.1-41.1
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• 2015

Hierarchical galaxy formation models under LCDM cosmology predict that the most massive structures such as galaxy clusters (M > $10^{14}M_{\odot}$) appear late (z < 1) in the history of the universe through hierarchical clustering of small objects. Galaxy formation is also expected to be accelerated in overdense environments, with the star formation rate-density relation to be established at z ~ 2. In this talk, we present our search of massive structures of galaxies at 0.7 < z < 4, using the data from GOODS survey and our own imaging survey, Infrared Medium-deep Survey (IMS). From these studies, we find that there are excess of massive structures of galaxies at z > 2 in comparison to the Millennium simulation data. At 1 < z < 2, the number density of massive structures is consistent with the simulation data, but the star formation history is more or less identical between field and cluster. The star formation quenching process is dominated by internal process (stellar mass). The environmental effect becomes important only at z < 1, which contributes to create the well known star formation-density relation in the local universe. Our results suggest that galaxy formation models under LCDM cosmology may require further refinements to match the observation.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1043-1061
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• 2013

Recent advances in computer technology introduce massive data and their analysis becomes important. The high performance computing is one of the most essential part in analysis of massive data. In this paper, we review the general purpose of the graphics processing unit and its application to parallel computing, which has been of great interest in statistics communities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.974-987
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• 2018

This paper presents a novel and efficient data transmission scheme based on mobile edge computing for the massive IoT environments which should support various type of services and devices. Based on an accurate and precise synchronization process, it maximizes data transmission throughput, and consistently maintains a flow's latency. To this end, the proposed efficient software defined data transmission scheme (ESD-DTS) configures and utilizes synchronization zones in accordance with the 4 usage cases, which are end node-to-end node (EN-EN), end node-to-cloud network (EN-CN), end node-to-Internet node (EN-IN), and edge node-to-core node (EdN-CN); and it transmit the data by the required service attributes, which are divided into 3 groups (low-end group, medium-end group, and high-end group). In addition, the ESD-DTS provides a specific data transmission method, which is operated by a buffer threshold value, for the low-end group, and it effectively accommodates massive IT devices. By doing this, the proposed scheme not only supports a high, medium, and low quality of service, but also is complied with various 5G usage scenarios. The essential difference between the previous and the proposed scheme is that the existing schemes are used to handle each packet only to provide high quality and bandwidth, whereas the proposed scheme introduces synchronization zones for various type of services to manage the efficiency of each service flow. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of throughput, control message overhead, and latency. Therefore, the proposed ESD-DTS is very suitable for upcoming 5G networks in a variety of massive IoT environments with supporting mobile edge computing (MEC).

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.17-22
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• 2018

In massive multiple-input multiple-output (MIMO) systems, linear channel estimation algorithms are widely applied owing to their simple structures. However, they may cause pilot contamination, which affects the subsequent data detection performance. Therefore, herein, for an uplink multicell massive multiuser MIMO system, we consider using an alternating projection (AP) for channel estimation to eliminate the effect of pilot contamination and improve the performance of data detection in terms of the bit error rates as well. Even though the AP is nonlinear, it iteratively searches the best solution in only one dimension, and the computational complexity is thus modest. We have analyzed the mean square error with respect to the signal-to-interference ratios for both the cooperative and non-cooperative multicell scenarios. From the simulation results, we observed that the channel estimation results via the AP benefit the following signal detection more than that via the least squares for both the cooperative and non-cooperative multicell scenarios.