• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.49-56
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• 2010

In this paper, we implement a Hadoop based cluster for cloud computing and evaluate the performance of this cluster based on application characteristics by executing RandomTextWriter, WordCount, and PI applications. A RandomTextWriter creates given amount of random words and stores them in the HDFS(Hadoop Distributed File System). A WordCount reads an input file and determines the frequency of a given word per block unit. PI application induces PI value using the Monte Carlo law. During simulation, we investigate the effect of data block size and the number of replications on the execution time of applications. Through simulation, we have confirmed that the execution time of RandomTextWriter was proportional to the number of replications. However, the execution time of WordCount and PI were not affected by the number of replications. Moreover, the execution time of WordCount was optimum when the block size was 64~256MB. Therefore, these results show that the performance of cloud computing system can be enhanced by using a scheduling scheme that considers application's characteristics.

• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.115-122
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• 2014

One of the problems with current youth generations is increasing rate of violence and suicide in their school lives, and this study aims at the design of a sentiment analysis system to prevent suicide by uising big data process. The main issues of the design are economical implementation, easy and fast processing for the users, so, the open source Hadoop system with MapReduce algorithm is used on the HDFS(Hadoop Distributed File System) for the experimentation. This study uses word count method to do the sentiment analysis with informal data on some sns communications concerning a kinds of violent words, in terms of text mining to avoid some expensive and complex statistical analysis methods.

• Journal of Information Technology and Architecture
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• v.10 no.3
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• pp.387-397
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• 2013

This paper proposes an approach to big data analytics for phon call data. The analytical models for phon call data is composed of the PVPF (Parallel Variable-length Phrase Finding) algorithm for identifying verbal phrases of natural language and the word count algorithm for measuring the usage frequency of keywords. In the proposed model, we identify words using the PVPF algorithm, and measure the usage frequency of the identified words using word count algorithm in MapReduce. The results can be interpreted from various viewpoints. We design and implement the model based HDFS (Hadoop Distributed File System), verify the proposed approach through a case study of phon call data. So we extract useful results through analysis of keyword correlation and usage frequency.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.959-961
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• 2003

In this paper, we describe implemented DMAC (Direct Memory Access Controller) architecture on Altera's $Excalibur^{TM}$ that includes industry-standard $ARM922T^{TM}$ 32-bit RISC processor core operating at 200 MHz. We implemented DMAC based on AMBA (Advanced Micro-controller Bus Architecture) AHB (Advanced Micro-performance Bus) interface. Implemented DMAC has 8-channel and can extend supportable channel count according to user application. We used round-robin method for priority selection. Implemented DMAC supports data transfer between Memory-to-Memory, Memory-to-Peripheral and Peripheral-to-Memory. The max transfer count is 1024 per a time and it can support byte, half-word and word transfer according to AHB protocol (HSIZE signals). We implemented with VHDL and functional verification using $ModelSim^{TM}$. Then, we synthesized using $LeonardoSpectrum^{TM}$ with Altera $Excalibur^{TM}$ library. We did FPGA P&R and targeting using $Quartus^{TM}$. We can use implemented DMAC module at any system that needs high speed and broad bandwidth data transfers.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.549-561
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• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.31-36
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• 2019

A chatbot is an important area of mobile service, which understands informal data of a user as a conversational form and provides a customized service information for user. However, there is still a lack of a service way to fully understand the user's natural language typed query dialogue. Therefore, in this paper, we extract meaningful words, such a region, a food category, and a restaurant name from user's dialogue sentences for recommending a restaurant. and by comparing the extracted words against the contents of the knowledge database that is built from the hashtag for recommending a restaurant in SNS, and provides user target information having statistically much the word-similarity. In order to evaluate the performance of the restaurant recommendation chatbot system implemented in this paper, we measured the accessibility of various user query information by constructing a web-based mobile environment. As a results by comparing a previous similar system, our chabot is reduced by 37.2% and 73.3% with respect to the touch-count and the cutaway-count respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.65-74
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• 2022

In natural language processing, commonly used words such as prepositions are referred to as stop-words; they have no inherent meaning and are therefore ignored in indexing and retrieval tasks. The removal of stop-words from Arabic text has a significant impact in terms of reducing the size of a cor- pus text, which leads to an improvement in the effectiveness and performance of Arabic-language processing systems. This study investigated the effectiveness of applying a stop-word lists elimination with normalization as a preprocessing step. The idea was to merge statistical method with the linguistic method to attain the best efficacy, and comparing the effects of this two-pronged approach in reducing corpus size for Ara- bic natural language processing systems. Three stop-word lists were considered: an Arabic Text Lookup Stop-list, Frequency- based Stop-list using Zipf's law, and Combined Stop-list. An experiment was conducted using a selected file from the Arabic Newswire data set. In the experiment, the size of the cor- pus was compared after removing the words contained in each list. The results showed that the best reduction in size was achieved by using the Combined Stop-list with normalization, with a word count reduction of 452930 and a compression rate of 30%.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.669-678
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• 2015

This study aims at investigating students' perceptions and peer feedback to practicum for teaching English in the English Department at a Korean university. The participants were forty-two students at an elective course, 'Method for teaching English', and the data comprised questionnaire, 12 teams of practicum, and 15 sets of PF. A 'Word Count System (WCS)' was adopted to analyze the data. The findings show that students regarded 'practicum' (52.4%) as more important than 'teacher's lectures' (42.8%), and most students (80%) applied more than 70% of lesson plans to their practicums. The practicum gave them experience of a teacher, development of confidence, recognition on their weaknesses and values of teaching. While the strengths shown in PF were mainly 'teaching methods and technique', 'use of multimedia', and 'teaching materials', the weaknesses were 'classroom interactions', 'teaching methods and techniques' and 'use of blackboard'. Overall praises were 1.8 times more than the matters which needed to be developed. The conclusion suggest that the students had their own insights toward teaching practices and how learners can be motivated.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.1
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• pp.9-18
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• 2018

Enhancing performance of big data analytics in distributed environment has been issued because most of the big data related applications such as machine learning techniques and streaming services generally utilize distributed computing frameworks. Thus, optimizing performance of those applications at Spark has been actively researched. Since optimizing performance of the applications at distributed environment is challenging because it not only needs optimizing the applications themselves but also requires tuning of the distributed system configuration parameters. Although prior researches made a huge effort to improve execution performance, most of them only focused on one of three performance optimization aspect: application design, system tuning, hardware utilization. Thus, they couldn't handle an orchestration of those aspects. In this paper, we deeply analyze and model the application processing procedure of the Spark. Through the analyzed results, we propose performance optimization schemes for each step of the procedure: inner stage and outer stage. We also propose appropriate partitioning mechanism by analyzing relationship between partitioning parallelism and performance of the applications. We applied those three performance optimization schemes to WordCount, Pagerank, and Kmeans which are basic big data analytics and found nearly 50% performance improvement when all of those schemes are applied.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.