• International journal of advanced smart convergence
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• 제10권4호
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.700-713
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• 2015

It is critical to design energy-efficient routing protocols for battery-limited mobile ad hoc networks, especially in which the energy-consuming MIMO techniques are employed. However, there are several challenges in such a design: first, it is difficult to characterize the energy consumption of a MIMO-based link; second, without a careful design, the broadcasted RREP packets, which are used in most energy-efficient routing protocols, could flood over the networks, and the destination node cannot decide when to reply the communication request; third, due to node mobility and persistent channel degradation, the selected route paths would break down frequently and hence the protocol overhead is increased further. To address these issues, in this paper, a novel Greedy Energy-Efficient Routing (GEER) protocol is proposed: (a) a generalized energy consumption model for the MIMO-based link, considering the trade-off between multiplexing and diversity gains, is derived to minimize link energy consumption and obtain the optimal transmit model; (b) a simple greedy route discovery algorithm and a novel adaptive reply strategy are adopted to speed up path setup with a reduced establishment overhead; (c) a lightweight route maintenance mechanism is introduced to adaptively rebuild the broken links. Extensive simulation results show that, in comparison with the conventional solutions, the proposed GEER protocol can significantly reduce the energy consumption by up to 68.74%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• 제39권1호
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• pp.1-13
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• 2002

In Distributed Batch-processing Systems (DBSs), CPU-intensive jobs are automatically transferred and executed using idle computers across a network, there by increasing the resource usage and throughput. To be successful, the systems must guarantee the scalablility, fault-tolerance, and flexibility of dynamic configurations. In practice, however, it is very difficult to provide such capabilities in a non-deterministic environment in which the available set of resources is unpredictable because of network failures, computer failures, or voluntary withdrawal from a pool by a machine owner. In this paper, we present the design and implementation of the J-JDBS (Jini-based Java Distributed Batch-processing System) system which is based on the core Jini services like Discovery service, Lookup service, Lease service and etc. We show that the Jini core services can be very effectively used to build reliable, scalallle, fault-tolerant, and flexible DBS systems with little effort.

• The KIPS Transactions:PartA
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• 제9A권1호
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• pp.45-52
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• 2002

This paper proposes the fuzzy closed RCMP queueing network model using fuzzy set theory for the performance evaluation of centralized distributed processing system with ambiguous system factors in the network environments. This model can derive the measures for system performances such as the job spending time, the system throughput, average job number and server utilizations using fuzzy mean value analysis which can process the fuzzy factors. Computer simulation has been performed centralized distributed system with fuzzy service requirement time for verifying the effectiveness of derived equations of performance evaluation according to the numbers of clients, and the results were analyzed. The proposed model provides more and flexible realistic than performance evaluation of conventional method when we evaluated system performance with ambiguous factors.

• Journal of KIISE
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• 제42권9호
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• pp.1109-1116
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• 2015

In this paper, we propose a query processing approach that uses the Spark functional programming and distributed memory system to solve the computational overhead of SPARQL. In the semantic web, RDF ontology data is produced at large scale, and the main challenge for the semantic web is to query and manipulate such a large ontology with a high throughput. The most existing studies on SPARQL have focused on deploying the Hadoop MapReduce framework, and although approaches based on Hadoop MapReduce have shown promising results, they achieve a low level of throughput due to the underlying distributed file processes. Therefore, in order to speed up the query processes, we suggest query- processing methods that are based on memory caching in distributed memory system. Our approach is also integrated with a clause unification method for propagating between the clauses that exploits Spark join, map and filter methods along with caching. In our experiments, we have achieved a high level of performance relative to other approaches. In particular, our performance was nearly similar to that of Sempala, which has been considered to be the fastest query processing system.

• Journal of the Korea Society for Simulation
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• 제18권2호
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• pp.9-17
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• 2009

A face database has contained a large amount of facial information data since face recognition was widely used. With the increase of facial information data, the face recognition based on distributed processing method has been noticed as a major topic. In existing studies, there were lack of discussion about the transferring method for large data. So, we proposed a fuzzy logic-based bit compression rate selection method for distributed face recognition. The proposed method selects an effective bit compression rate by fuzzy inference based on face recognition rate, processing time for recognition, and transferred bit length. And, we compared the facial recognition rate and the recognition time of the proposed method to those of facial information data with no compression and fixed bit compression rates. Experimental results demonstrates that the proposed method can reduce processing time for face recognition with a reasonable recognition rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• 제41권6호
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• pp.23-37
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• 2004

Since java thread is an object which is treated as independent process within one execution space in the multiprocessing environment, we can use it for independent process of parallel processing. Using thread and synchronization mechanism of java enables us to write parallel application program easily. Therefore, a lot of results are exist which is apply the feature of java that support parallel processing to the distributed computing environment. In this paper, we introduce a system of environment that support parallel execution of thread which is included in legacy java program. The system named TORB(Transparent Object Request Broker) enables us parallel execution of legacy java program after simple converting process, since it support the feature of programming transparency. TORB is extended version of distributed programming tool that is published by our research team. And it had only typical distributed processing feature that is execute a specified function at the specified computer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.479-482
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• 2014

Nowadays open-source hadoop systems have been using widely to efficiently manage a fast-growing big data. Hadoop systems consist of distributed file processing system called HDFS (Hadoop Distributed File System) and distributed parallel processing system called MapReduce. The MapReduce reads and processes big data from HDFS and then processed results are written in HDFS again by the MapReduce. Such a processing method has different system structure respectively according to hadoop version. Therefore, this paper shows analysis results for performance of hadoop systems. For this, we devise a way which monitors hadoop systems and measure occurrence frequency of processes, threads, and variables generated in hadoop system itself using the devised way. So, by using the measured results as analysis indicator, we help the indicator predict inner performance of hadoop systems.

• Journal of the Korea Society of Computer and Information
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• 제15권11호
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• pp.11-20
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• 2010

XML view is proposed to integrate between XML data and heterogeneous data over distributed environment and global XML view is used to search distributed heterogeneous data. At this time, standard query language for user is XQuery and the method for processing global XQuery queries over distributed environment is one of the new research topics. One of the basic and simple methods to process distributed SQL queries is that generates local queries for processing a global query and constructs the result of the global query from the results of the local queries. However, the syntax of XQuery differs from SQL because the XQuery contains some special expressions like FOR clauses for querying to semi-structured data, of course, FOR clauses are not used in SQL. Therefore, there are some problems to adopt the method for processing global SQL queries for generating local XQuery queries. This paper defines some problems when generates local XQuery queries for processing global XQuery queries and proposes a method for generating local XQuery queries considered these problems. Also we implement and evaluate a Global XQuery Processor which uses our method.

• Journal of KIISE
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• 제45권1호
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• pp.53-60
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• 2018

This study describes a spatial web object retrieval system using Spark, an in - memory based distributed processing system. Development of social networks has created massive amounts of spatial web objects, and retrieval and analysis of data is difficult by using exist spatial web object retrieval systems. Recently, development of distributed processing systems supports the ability to analyze and retrieve large amounts of data quickly. Therefore, a method is promoted to search a large-capacity spatial web object by using the distributed processing system. Data is processed in block units, and one of these blocks is converted to RDD and processed in Spark. Regarding the discussed method, we propose a system in which each RDD consists of spatial web object index for the included data, dividing the entire spatial region into non-overlapping spatial regions, and allocating one divided region to one RDD. We propose a system that can efficiently use the distributed processing system by dividing space and increasing efficiency of searching the divided space. Additionally by comparing QP-tree with R-tree, we confirm that the proposed system is better for searching the spatial web objects; QP-tree builds index with both spatial and words information while R-tree build index only with spatial information.