• Journal of Service Research and Studies
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• v.3 no.2
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• pp.33-44
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• 2013

As the Internet and mobile services increase, the use of wired/wireless web contents services increase and the demand for various contents explosively grows. To survive in competitive market, and to minimize the errors and warnings for web accessibility and standardization, and then to maximize the web usability, the periodical evaluation for web site should be performed with the events of web marketing and campaign. Through the web evaluation, the errors for technical programming language and contents offering can be found and diagnosed. In this paper, the quantitative and qualitative evaluation method for web site providing web contents are presented, and the analytic results for the 138 home pages in domestic are evaluated to validate the quantitative methodology. The accessibility, standardization, and usability factor are adopted for the evaluation in which accessibility is evaluated for perceivable, operable, understandable, and robust discipline with K-WAH(Korea-Web Accessibility Helper) tool, the standardization are measured for the number of errors and warnings in technical language with the W3C validator, and finally the usability factor is analyzed for the number of visits, average visit duration, and bounce rate with Google Analytics. In addition to, the quantitative analysis is also performed with the consideration of cost for construction and operation of web site. From the results, in the case of total score of 100 in conversion with relative weight, the average and standard deviation are evaluated to be 55 and 14, respectively. The correlation analysis indicates that the coefficient is estimated as 0.058, and then correlation between the quantitative results and cost is evaluated to be a little positive.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.163-170
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• 2020

With the development of information and communication technologies, the growing volume of data is increasing exponentially, raising interest in big data. As technologies related to big data have developed, big data is being collected, stored, processed, analyzed, and utilized in many fields. Big data analytics in the health care sector, in particular, is receiving much attention because they can also have a huge social and economic impact. It is predicted that it will be able to use Big Data technology to analyze patients' diagnostic data and reduce the amount of money that is spent on simple hospital care. Therefore, in this thesis, patient data is analyzed to present to patients who are unable to go to the hospital or caregivers who do not have medical expertise with close care guidelines. First, the collected patient data is stored in HDFS and the data is processed and classified using R, a big data processing and analysis tool, in the Hadoop environment. Visualize to a web server using R Shiny, which is used to implement various functions of R on the web.

• Genomics & Informatics
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• v.16 no.2
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• pp.22-29
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• 2018

Incorporation of unique barcodes into fission yeast gene deletion collections has enabled the identification of gene functions by growth fitness analysis. For fine tuning, it is important to examine barcode sequences, because mutations arise during strain construction. Out of 8,708 barcodes (4,354 strains) covering 88.5% of all 4,919 open reading frames, 7,734 barcodes (88.8%) were validated as high-fidelity to be inserted at the correct positions by Sanger sequencing. Sequence examination of the 7,734 high-fidelity barcodes revealed that 1,039 barcodes (13.4%) deviated from the original design. In total, 1,284 mutations (mutation rate of 16.6%) exist within the 1,039 mutated barcodes, which is comparable to budding yeast (18%). When the type of mutation was considered, substitutions accounted for 845 mutations (10.9%), deletions accounted for 319 mutations (4.1%), and insertions accounted for 121 mutations (1.6%). Peculiarly, the frequency of substitutions (67.6%) was unexpectedly higher than in budding yeast (~28%) and well above the predicted error of Sanger sequencing (~2%), which might have arisen during the solid-phase oligonucleotide synthesis and PCR amplification of the barcodes during strain construction. When the mutation rate was analyzed by position within 20-mer barcodes using the 1,284 mutations from the 7,734 sequenced barcodes, there was no significant difference between up-tags and down-tags at a given position. The mutation frequency at a given position was similar at most positions, ranging from 0.4% (32/7,734) to 1.1% (82/7,734), except at position 1, which was highest (3.1%), as in budding yeast. Together, well-defined barcode sequences, combined with the next-generation sequencing platform, promise to make the fission yeast gene deletion library a powerful tool for understanding gene function.

• Journal of Korea Game Society
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• v.14 no.4
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• pp.27-36
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• 2014

Accurate information on players, namely player analytics is one of the key factors in a game development environment where a scientific approach to user-oriented game analysis is in the spotlight. This study is intended to examine effects of relations between choke point types and cover pattern properties on level difficulties in FPS games. As for FPS games, interaction between players' behaviors and game levels is higher compared to other genres and choke point types as well as cover pattern properties are key factors of level design. Choke point is the main crossroad that must to pass for achieving the goal and Cover Pattern is the type of object on the level except buildings. Two elements directly or indirectly affect the level of difficulty. This study analyzed 10 types of representative FPS gameplays to classify choke point types and assigned 4 types of cover pattern properties to organize 16 target levels for the experiment. In addition, this study collected and analyzed players' 800 behavior data (video clips) from 5 repetitive plays performed by 10 players. In conclusion, analytical results obtained from the empirical study will contribute to realizing systematic game level development by providing specific information for a game level design phase. The findings are also meaningful in that they suggest efficient and effective methods of utilizing the existing academic study results for industrial applications.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.13-20
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• 2020

One of the major issues surrounding big data is the availability of massive time-based or telemetry data. Now, the appearance of low cost capture and storage devices has become possible to get very detailed time data to be used for further analysis. Thus, we can use these time data to get more knowledge about the underlying system or to predict future events with higher accuracy. In particular, it is very important to define custom tailored contract offers for many households and businesses having smart meter records and predict the future electricity usage to protect the electricity companies from power shortage or power surplus. It is required to identify a few groups with common electricity behavior to make it worth the creation of customized contract offers. This study suggests big data transformation as a side effect and clustering technique to understand the electricity usage pattern by using the open data related to smart meter and KNIME which is an open source platform for data analytics, providing a user-friendly graphical workbench for the entire analysis process. While the big data components are not open source, they are also available for a trial if required. After importing, cleaning and transforming the smart meter big data, it is possible to interpret each meter data in terms of electricity usage behavior through a dynamic time warping method.

• Journal of the Korean School Mathematics Society
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• v.21 no.1
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• pp.1-18
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• 2018

This study aims to analyze the mathematics curriculum in the gifted school and obtain the understanding of the current situation of education for the math-gifted children in Korea, therefore providing a point of view for the improvements. In order to attain these purposes, the study examined the subject competency for the mathematics set by regular mathematics curriculum system and 2015 revision curriculum, and extracted the analytical standards, based on which the education plan documents of each gifted school were analyzed. The conclusion that has been made based on the analysis results is as follows. First of all, the curriculum of mathematics in the gifted schools in korea is heavily concentrated on analytics and algebra. Secondly, in mathematics curriculum for gifted children in Korea puts the most emphasis on the problem solving competency. Third, geometry subject in the mathematics curriculum of Korean gifted schools deals with the given content only at the level of regular high school curriculum. Fourth, learning materials in most gifted schools are not the ones especially revised and adapted for the gifted students but usually the ones for the college students. Lastly, gifted schools are running the curriculum featured with curriculum compacting and advance learning focusing on acceleration.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.1009-1019
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• 2017

Mobile Edge Computing(MEC) is a emerging technology to cope with mobile traffic explosion and to provide a variety of services having specific requirements by means of running some functions at mobile edge nodes directly. For instance, caching function can be executed in order to offload mobile traffics, and safety services using real time video analytics can be delivered to users. So far, a myriad of methods and architectures for personalized service recommendation have been proposed, but there is no study on the subject which takes unique characteristics of mobile edge computing into account. To provide personalized services, acquiring users' context is of great significance. If the conventional personalized service model, which is server-side oriented, is applied to the mobile edge computing scheme, it may cause context isolation and privacy issues more severely. There are some advantages at mobile edge node with respect to context acquisition. Another notable characteristic at MEC scheme is that interaction between users and applications is very dynamic due to temporal relation. This paper proposes the local service recommendation platform architecture which encompasses these characteristics, and also discusses the personalized service recommendation mechanism to be able to mitigate context isolation problem and privacy issues.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.466-472
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• 2020

The purpose of this study is to explore how to use location data for peer relations of infants in free-choice play activities. For this study, location data was collected using wearable devices for 14 students in one class at an early childhood education institution in Chungnam. For the pre-processing of the collected location data, a smoothing technique was applied to recover missing values during the collection process, and the data was visualized using Python's Matplotlib. Subsequently, the movement distance, distance between infants, and interaction types of infants were extracted from the location data using the formula. As a result of the study, it was possible to derive 1) change in moving distance, cumulative value, average value, 2) change in distance and average distance value between infants, and 3) change and trend in interaction type according to the passage of time. These results can provide valuable information on the process of forming peer groups for infants in situations where it is difficult for a teacher to closely observe all members, and can be used as meaningful information for the design and operation of educational programs.

• Smart Media Journal
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• v.9 no.3
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• pp.31-40
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• 2020

Currently, black-box-based machine learning algorithms are used to analyze big data in manufacturing. This algorithm has the advantage of having high analytical consistency, but has the disadvantage that it is difficult to interpret the analysis results. However, in the manufacturing industry, it is important to verify the basis of the results and the validity of deriving the analysis algorithms through analysis based on the manufacturing process principle. To overcome the limitation of explanatory power as a result of this machine learning algorithm, we propose a manufacturing big data analysis method using genetic programming. This algorithm is one of well-known evolutionary algorithms, which repeats evolutionary operators such as selection, crossover, mutation that mimic biological evolution to find the optimal solution. Then, the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. Through this, input and output variable relations are derived to formulate the results, so it is possible to interpret the intuitive manufacturing mechanism, and it is also possible to derive manufacturing principles that cannot be interpreted based on the relationship between variables represented by formulas. The proposed technique showed equal or superior performance as a result of comparing and analyzing performance with a typical machine learning algorithm. In the future, the possibility of using various manufacturing fields was verified through the technique.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.57-70
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• 2018

The advancement and the spread of information and communication technology (ICT) changes the way we live and act. Computer and ICT devices become smaller and invisible, and they are now virtually everywhere in the world. Many socio-economic activities are now subject to the use of computer and ICT devices although we don't really recognize it. Various socio economic activities supported by digital devices leave digital records, and a myriad of these records becomes what we call'big data'. Big data differ from conventional data we have collected and managed in that it holds more detailed information of socio-economic activities. Thus, they offer not only new insight for our society and but also new opportunity for policy analysis. However, the use of big data requires development of new methods and tools as well as consideration of institutional issues such as privacy. The goals of this research are twofold. Firstly, it aims to understand the opportunities and challenges of using big data for planning support. Big data indeed is a big sum of microscopic and dynamic data, and this challenges conventional analytical methods and planning support tools. Secondly, it seeks to suggest ways of visualizing such spatial big data for planning support. In this regards, this study attempts to develop a dynamic visualization model and conducts an experimental case study with mobile phone big data for the Jeju island. Since the off-the-shelf commercial software for the analysis of spatial big data is not yet commonly available, the roles of open source software and computer programming are important. This research presents a pilot model of dynamic visualization for spatial big data, as well as results from them. Then, the study concludes with future studies and implications to promote the use of spatial big data in urban planning field.