• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.307-312
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• 2007

Wireless Sensor Network(WSN) is considered as a core technology necessary for Ubiquitous computing, with its numerous possible applications in many practical areas, is being researched and studied actively by many around the world. WSN utilizes wireless sensors spatially placed to gather information regarding temperature, light condition, motion and change in speed of the objects within their surrounding environment. This paper implements an environmental information monitoring and indexing system based on spatial indexing technique by constructing a WSN system. This Multi-Query Indexing Technique coupled with wireless sensors provides an output based on the pre-defined built-in data index and new input from the sensors. If environment data is occured, system have to perform a proper action after collecting and analyzing this data. This is the purpose of implementing environment data monitoring system. We constructed environmental application using TinyOS and built tested with MICAz sensor bords. We designed and implemented a monitoring system which detects and multi-indexing process environmental data from distributed sensors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.17-28
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• 2017

Recently, a study on data has been actively conducted because the value of the data has become more useful. Web crawler that is program of data collection recently spotlighted because it can take advantage of the various fields. Web crawler can be defined as a tool to analyze the web pages and collects the URL by traversing the web server in an automated manner. For the treatment of Big-data, distributed Web crawler is widely used which is based on the Hadoop MapReduce. But, it is difficult to use and has constraints on the performance. Apache spark that is the In-memory computing platform is an alternative to MapReduce. The search engine which is one of the main purposes of web crawler displays the information you search by keyword gathered by web crawler. If search engines implement a spark-based web crawler instead of traditional MapReduce-based web crawler, it would be a more rapid data collection.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.1-8
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• 2011

An underwater robot S/W framework consists of various sub-modules such as sensory data processing module, thruster control module, cognition module and behavior control module. Performance of a robot is determined by not only the efficiency of algorithms used but also effectiveness of their implementations. One most important factor of the effective implementation is the efficiency of data sharing module, as it transmits signals and data between the sub-modules and thus is directly related to the cycles of sensing and control The ideal data sharing module enables immediate access to any data source irrespective of system configurations. In reality, however, there are lots of obstacles including limitation of processing capacity of source modules, delay over network, and scheduling latency of operating systems. The paper proposes a new data sharing architecture and programming models to effectively handle such obstacles in implementation of underwater S/W framework on a small scale distributed computing system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.112-119
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• 2010

This paper defines the probability models for determining the disparity map given stereo images and derives the methods for solving the problem, which is proven to be equivalent to an energy-based stereo matching. Under the assumptions the difference between the pixel on the left image and the corresponding pixel on the right image and the difference between the disparities of the neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameter is proposed. Usually energy-based stereo matching methods are so sensitive to the parameter that it should be carefully determined. The proposed method alternates between estimating the parameter with the intermediate disparity map and estimating the disparity map with the estimated parameter, after computing it with random initial parameter. It is shown that the parameter estimated by the proposed method converges to the optimum and its performance can be improved significantly by adjusting the parameter and modifying the energy term.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.101-108
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• 2007

Parallel processing algorithms, coupled with advanced networking and distributed computing architectures, improve the overall computational performance, dependability, and versatility of a digital signal processing system In this paper, novel parallel algorithms are introduced and investigated for advanced sonar algorithm, conventional matched-field processing (CMFP). Based on a specific domain, each parallel algorithm decomposes the sequential workload in order to obtain scalable parallel speedup. Depending on the processing requirement of the algorithm, the computational performance of the parallel algorithm reveals different characteristics. The high-complexity algorithm, CMFP shows scalable parallel performance on the array of DSP processors. The impact on parallel performance due to workload balancing, communication scheme, algorithm complexity, processor speed, network performance, and testbed configuration is explored.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.263-270
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• 2014

In cloud environment, business intelligence data warehouse is used for decision making and enterprise policy. But if new system is added in cloud environment, much cost and time is needed due to heterogenous characteristics in data integration. This paper suggests a data cleaning system for business intelligence in cloud environment. The proposed system minimizes the effect of local system when it integrates distributed system using XMDR-DAI. And this system provides standardized information to generate information of data warehouse in real time. Also the proposed system saves cost and time by integrating the data without a change of existed system. And it can improve quality of information by generating coherent information through data extraction and cleaning work in real time.

• Journal of Internet Computing and Services
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• v.5 no.3
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• pp.25-33
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• 2004

Mobile IP Low Latency Handoffs(l) allow greater support for real-time services on a Mobile IP network by minimizing the period of time when a mobile node is unable to send or receive IP packets due to the delay in the Mobile IP Registration process. However, on Mobile IP network with AAA servers that are capable of performing Authentication, Authorization, and Accounting(AAA) services, every Registration has to be traversed to the home network to achieve new session keys, that are distributed by home AAA server, for a new Mobile IP session. This communication delay is the time taken to re-authentication the mobile node and to traverse between foreign and home network even if the mobile node has been previously authorized to old foreign agent. In order to reduce these extra time overheads, we present a method that performs Low Latency Handoffs without requiring further involvement by home AAA server. The method re-uses the previously assigned session keys. To provide confidentiality and integrity of session keys in the phase of key exchange between agents, it uses a key sharing method by gateway foreign agent that performs a trusted thirty party. The proposed method allows the mobile node to perform Low Latency Handoffs with fast as well as secure operation.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.45-53
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• 2014

UI-Mashup is widely used as a service method to add value, which is composed of distributed various contents on the internet and has turned out to be one of the latest trends in web application program development. Previous UI-Mashup-related studies have focused primarily on the dynamic service composition and have not been able to adapt to a rapidly changing Web Standard, thusthe end users conclude that UI-Mashups are slow, incompatible and poor security services. In this study, We propose an architecture for the performance improvements of UI-Mashup.In order to provide fast services and security enhancements, the proposed architecture collects UI fragments on the server in parallel, and sends layouts and contents of Mashups UI to the client through a special delivery channel supporting fast reaction and response time. In this study, the implementation and performance tests were proceeded to verify the proposed architecture experimentally. As a result of the performance testing, the proposed architecture has two to three times faster response time and more than four times throughput compared to the previous UI-Mashup technology.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.35-45
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• 2014

Due to the spread of unreliable online information on the social network services, the users are faced with a difficult problem for determining if the information is trustworthy or not. At present, the users should make a decision by themselves throughly for the trustworthiness of the information. Therefore, we need a way to systematically evaluate the trustworthiness of information on the social network services. In this paper, we design a trust index, called KTI (Korean Trust Index for SNS), as a criterion for measuring the trust degree of the information on the social network services. Using KTI, the users are readily able to determine whether the information is trustworthy. Consequently, we can estimate the social trust degree based on the variation of KTI. This paper derives the various factors affecting trust from the properties of the social network services, and proposes a model to evaluate the trustworthiness of information that is directly produced and distributed over the online network. Quantifying the trust degree of the information on the social network services allows the users to make efficient use of the social network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.204-226
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• 2018

Big data processing applications have been migrated into cloud gradually, due to the advantages of cloud computing. Hadoop Distributed File System (HDFS) is one of the fundamental support systems for big data processing on MapReduce-like frameworks, such as Hadoop and Spark. Since HDFS is not aware of the co-location of virtual machines in the cloud, the default scheme of block allocation in HDFS does not fit well in the cloud environments behaving in two aspects: data reliability loss and performance degradation. In this paper, we present a novel location-aware data block allocation strategy (LDBAS). LDBAS jointly optimizes data reliability and performance for upper-layer applications by allocating data blocks according to the locations and different processing capacities of virtual nodes in the cloud. We apply LDBAS to two stages of data allocation of HDFS in the cloud (the initial data allocation and data recovery), and design the corresponding algorithms. Finally, we implement LDBAS into an actual Hadoop cluster and evaluate the performance with the benchmark suite BigDataBench. The experimental results show that LDBAS can guarantee the designed data reliability while reducing the job execution time of the I/O-intensive applications in Hadoop by 8.9% on average and up to 11.2% compared with the original Hadoop in the cloud.