• Journal of Digital Contents Society
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• v.16 no.4
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• pp.545-553
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• 2015

While all of the data has a value in itself, most of the data that is collected in the real world is a random and unstructured. In order to extract useful information from the data, it is need to use the data transform and analysis algorithms. Data mining is used for this purpose. Today, there is not only need for a variety of data mining techniques to analyze the data but also need for a computational requirements and rapid analysis time for huge volume of data. The method commonly used to store huge volume of data is to use the hadoop. A method for analyzing data in hadoop is to use the MapReduce framework. In this paper, we developed a tournament-based MapReduce method for high efficiency in developing an algorithm on a single machine to the MapReduce framework. This proposed method can apply many analysis algorithms and we showed the usefulness of proposed tournament based method to apply frequently used data mining algorithms k-means and k-nearest neighbor classification.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.293-298
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• 2014

Detecting and understanding the changes between RDF data is crucial in the evolutionary process, synchronization system, and versioning system on the web of data. However, current researches on detecting changes still remain unsatisfactory in that they did neither consider the large scale of RDF data nor accurately produce the RDF deltas. In this paper, we propose a scalable and effective change detection using a MapReduce framework which has been used in many fields to process and analyze large volumes of data. In particular, we focus on the structure-based change detection that adopts a strategy for the comparison of blank nodes in RDF data. To achieve this, we employ a method which is composed of two MapReduce jobs. First job partitions the triples with blank nodes by grouping each triple with the same blank node ID and then computes the incoming path to the blank node. Second job partitions the triples with the same path and matchs blank nodes with the Hungarian method. In experiments, we show that our approach is more accurate and effective than the previous approach.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.494-499
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• 2015

As the size of data increases, it becomes important to identify properties by analyzing big data. In this paper, we propose a k-Means based efficient clustering technique, called MCSKMeans (Multi centroid set k-Means), using distributed parallel processing framework MapReduce. A problem with the k-Means algorithm is that the accuracy of clustering depends on initial centroids created randomly. To alleviate this problem, the MCSK-Means algorithm reduces the dependency of initial centroids using sets consisting of k centroids. In addition, we apply the agglomerative hierarchical clustering technique for creating k centroids from centroids in m centroid sets which are the results of the clustering phase. In this paper, we implemented our MCSK-Means based on the MapReduce framework for processing big data efficiently.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.290-296
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• 2022

Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.834-837
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• 2009

There is a growing need for ad-hoc analysis of extremely large data sets, especially at e-Commerce companies which depend on recommender application. Nowadays, as the number of e-Commerce web pages grow to a tremendous proportion; vertical recommender services can help customers to find what they need. Recommender application is one of the reasons for e-Commerce success in today's world. Compared with general e-Commerce recommender application, obviously, general e-Commerce recommender application's processing scope is greatly narrowed down. MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. The objective of this paper is to explore MapReduce framework for the e-Commerce recommender application on major general and dedicated link analysis for e-Commerce recommender application, and thus the responding time has been decreased and the recommender application's accuracy has been improved.

• KIISE Transactions on Computing Practices
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• v.21 no.5
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• pp.387-392
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• 2015

MapReduce is a framework used to process large data sets in parallel on a large cluster. A group-by query is a query that partitions the input data into groups based on the values of the specified attributes, and then evaluates the value of the specified aggregate function for each group. In this paper, we propose an efficient method for processing multiple group-by queries using MapReduce. Instead of computing each group-by query independently, the proposed method computes multiple group-by queries in stages with one or more MapReduce jobs in order to reduce the total execution cost. We compared the performance of this method with the performance of a less sophisticated method that computes each group-by query independently. This comparison showed that the proposed method offers better performance in terms of execution time.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1155-1172
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• 2015

In this paper, we propose a novel saliency detection framework based on illumination invariant features to improve the accuracy of the saliency detection under the different light conditions. The proposed algorithm is divided into three steps. First, we extract the illuminant invariant features to reduce the effect of the illumination based on the local sensitive histograms. Second, a preliminary saliency map is obtained in the CIE Lab color space. Last, we use the region growing method to fuse the illuminant invariant features and the preliminary saliency map into a new framework. In addition, we integrate the information of spatial distinctness since the saliency objects are usually compact. The experiments on the benchmark dataset show that the proposed saliency detection framework outperforms the state-of-the-art algorithms in terms of different illuminants in the images.

• Journal of Information Technology and Architecture
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• v.9 no.4
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• pp.455-464
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• 2012

Due to the recent data explosion, methods which can meet the requirement of large data analysis has been studying. This paper proposes MRIterativeBUC algorithm which enables efficient computation of large data cube by distributed parallel processing with MapReduce framework. MRIterativeBUC algorithm is developed for efficient iterative operation of the BUC method with MapReduce, and overcomes the limitations about the storage size and processing ability caused by large data cube computation. It employs the idea from the iceberg cube which computes only the interesting aspect of analysts and the distributed parallel process of cube computation by partitioning and sorting. Thus, it reduces data emission so that it can reduce network overload, processing amount on each node, and eventually the cube computation cost. The bottom-up cube computation and iterative algorithm using MapReduce, proposed in this paper, can be expanded in various way, and will make full use of many applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1731-1737
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• 2017

K Nearest Neighbor Join (KNN Join) is a simple yet effective method in machine learning. It is widely used in small dataset of the past time. As the number of data increases, it is infeasible to run this model on an actual application by a single machine due to memory and time restrictions. Nowadays a popular batch process model called MapReduce which can run on a cluster with a large number of computers is widely used for large-scale data processing. Hadoop is a framework to implement MapReduce, but its performance can be further improved by a new framework named Spark. In the present study, we will provide a KNN Join implement based on Spark. With the advantage of its in-memory calculation capability, it will be faster and more effective than Hadoop. In our experiments, we study the influence of different factors on running time and demonstrate robustness and efficiency of our approach.