• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.589-594
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• 2013

As the concept of web characteristics represented by openness and mind sharing grows more and more popular, device log data generated by both users and developers have become increasingly complicated. For such reasons, a log data processing mechanism that automatically produces meaningful data set from large amount of log records have become necessary for mobile device UX(User eXperience) analysis. In this paper, we define the attributes of to-be-analyzed log data that reflect the characteristics of a mobile device and collect real log data from mobile device users. Along with the MapReduce programming paradigm in Hadoop platform, we have performed a mobile device User eXperience analysis in a distributed processing environment using the collected real log data. We have then demonstrated the effectiveness of the proposed analysis mechanism by applying the various combinations of Map and Reduce steps to produce a simple data schema from the large amount of complex log records.

• 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.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.761-767
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• 2001

In this paper, filtering techniques to reduce the adverse effects of motor leads on high-frequency PWM inverter fed AC motor drives will be examined. The filter was designed to keep the motor terminal from the cable surge impedance to reduce overvoltage reflections, ringing, and the dv/dt, di/dt. Therefore, filtering techniques are investigated to reduce the motor terminal overvoltage, ringing, and EMI noise in inverter fed ac motor drive systems. The output filter is used to limit the rate of the inverter output voltage and reduce EMI(common mode noise) to the motor. The performance of the output filter is evaluated through simulations (PSIM) and experiment on PWM inverter-fed ac motor drive(3phase, 3hp(2.2kw), input voltage 220/380V, induction motor). An experimental PWM drive system reduction of conducted EMI was implemented on an available TMS320C31 microprocessor control board. Finally, experimental results showed that the inverter output filter reduces more CM noise than the LPF(low pass filter) and reduce overvoltage and ringing at the motor terminal.

• Journal of Nutrition and Health
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• v.34 no.8
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• pp.850-857
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• 2001

The study was designed to observe whether the conjugated linoleic acid supplemented to diet could reduce plasma levels of glucose and lipids which were increased in 27-weeks old Otsuka Long Evans Tokushima Fatty(OLETF) rats. Twenty male OLETF rats of 7 weeks old were fed an experimental diet containing 4.5%(w/w) total fat including 1% CLA and six of twenty rats were sacrified at 6 weeks feeding. The rest of OLETF rats was divided into 2 groups, one group was continuously fed for 14 weeks more the same experimental diet containing 1% CLA and the other group was fed control diet which eliminated CLA. CLA did not significantly reduce food intake and body weight gain in OLETF obese rats. Plasma triglyceride and total cholesterol level were significantly increased at older age of OLETF obese rats, but CLA could significantly reduce plasma cholesterol and triglyceride increased in obese rats. However, CLA was not strong enough to reduce the increased plasma glucose level and hepatic lipogenic enzyme acitivies. CLA was mostly deposited in epididymal fat pad and could be incorporated into hepatic microsomal membrane and did interfere the conversion of C18 : 0 into C18 : 1 in liver. In conclusion, CLA could have anti-atherogenic effect by reducing plasma cholesterol and triglyceride which was increased in genetically obese rats, but CLA(1%) was not good source of dietary fatty acid to reduce body fatness and plasma glucose which was increased by obese gene in older rats.

• Journal of KIISE
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• v.43 no.6
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• pp.700-709
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• 2016

Recently, with the rapid growth of LOD (Linked Open Data) existing methods based on a single machine have limitation in performance. Existing solutions use distributed framework such as Mapreduce in order to improve the performance. However, the MapReduce framework for processing SPARQL queries involves multiple MapReduce jobs and additional costs incurred. In addition, the problem of unnecessary data processing arises. In this study, we proposed a method to reduce the number of MapReduce jobs during SPARQL query processing and join indexes based on Bitmap for minimizing the costs of processing unnecessary data.

• The KIPS Transactions:PartD
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• v.18D no.2
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• pp.81-88
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• 2011

Sequential pattern mining that determines frequent patterns appearing in a given set of sequences is an important data mining problem with broad applications. For example, sequential pattern mining can find the web access patterns, customer's purchase patterns and DNA sequences related with specific disease. In this paper, we develop the sequential pattern mining algorithms using MapReduce framework. Our algorithms distribute input data to several machines and find frequent sequential patterns in parallel. With synthetic data sets, we did a comprehensive performance study with varying various parameters. Our experimental results show that linear speed up can be achieved through our algorithms with increasing the number of used machines.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.609-619
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• 2016

RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. In this paper, we implement K-Means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. The main idea introduces a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. We showed that our K-Means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases. We also implemented Elbow method with MapReduce for finding the optimum number of clusters for K-Means clustering on large dataset. Comparison with our MapReduce implementation of Elbow method and classical kmeans() in R with small data showed similar results.

• Journal of Digital Contents Society
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• v.15 no.3
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• pp.365-371
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• 2014

In the era of big data, algorithms for the existing IT environment cannot accept on a distributed architecture such as hadoop. Thus, new distributed algorithms which apply a distributed framework such as MapReduce are needed. Lloyd's algorithm commonly used for vector quantization is developed using MapReduce recently. In this paper, we proposed a decombined distributed VQ codebook generation algorithm based on a distributed VQ codebook generation algorithm using MapReduce to get a result more fast. The result of applying the proposed algorithm to big data showed higher performance than the conventional method.

• ETRI Journal
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• v.36 no.6
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• pp.931-941
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• 2014

Many methodologies for clock mesh networks have been introduced for two-dimensional integrated circuit clock distribution networks, such as methods to reduce the total wirelength for power consumption and to reduce the clock skew variation through consideration of buffer placement and sizing. In this paper, we present a methodology for clock mesh to reduce both the clock skew and the total wirelength in three-dimensional integrated circuits. To reduce the total wirelength, we construct a smaller mesh size on a die where the clock source is not directly connected. We also insert through-silicon vias (TSVs) to distribute the clock signal using an effective clock TSV insertion algorithm, which can reduce the total wirelength on each die. The results of our proposed methods show that the total wirelength was reduced by 12.2%, the clock skew by 16.11%, and the clock skew variation by 11.74%, on average. These advantages are possible through increasing the buffer area by 2.49% on the benchmark circuits.

• ETRI Journal
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• v.42 no.5
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• pp.748-760
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• 2020

MapReduce is a framework that can process huge datasets in parallel and distributed computing environments. However, a single machine failure during the runtime of MapReduce tasks can increase completion time by 50%. MapReduce handles task failures by restarting the failed task and re-computing all input data from scratch, regardless of how much data had already been processed. To solve this issue, we need the computed key-value pairs to persist in a storage system to avoid re-computing them during the restarting process. In this paper, the task failure resilience (TFR) technique is proposed, which allows the execution of a failed task to continue from the point it was interrupted without having to redo all the work. Amazon ElastiCache for Redis is used as a non-volatile cache for the key-value pairs. We measured the performance of TFR by running different Hadoop benchmarking suites. TFR was implemented using the Hadoop software framework, and the experimental results showed significant performance improvements when compared with the performance of the default Hadoop implementation.