• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.261-266
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• 2016

Chart suggestion method provided by various existing data visualization tools makes chart recommendations without considering the user intention. Data visualization is not properly carried out and thus, unclear in some tools because they do not follow the segmented quantitative data classification policy. This paper provides a guideline that clearly classifies the quantitative input data and that effectively suggests charts based on user intention. The guideline is two-fold; the analysis guideline examines the quantitative data and the suggestion guideline recommends charts based on the input data type and the user intention. Following this guideline, we excluded charts in disagreement with the user intention and confirmed that the time user spends in the chart selection process has decreased.

• Information Systems Review
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• v.16 no.3
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• pp.49-68
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• 2014

These days, consumers have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the consumers. Accordingly, sales of music in compact disk formats have steadily declined. In this regards, online digital music has become a new communication channel to listen musics, where digital files can be delivered over various online networks to people's computing devices. The majority of online digital music distributors has Music Recommender Systems for sales of digital music on their websites. Music Recommender Systems are parts of information filtering systems that provide the ratings or preferences that users give to music. Korean online digital music distributors have Music Recommender Systems. But those online music distributors didn't provide any rules or clear procedures that recommend music. Therefore, we raise important questions as follows: "Is Music Recommender Systems Fair?", "What is the impact of Music Recommender Systems on online music rankings and sales?" While previous studies have focused on usefulness of Music Recommender Systems, this study investigates not only fairness of Current Music Recommender Systems but also Relationship between Music Recommender Systems and online Music Charts. This study examines these issues based on Bandwagon effect, ranking effect, Slot effect theories. For our empirical analysis, we selected the most famous five online digital music distributors in terms of market shares. We found that all recommended music is exposed to the top of 'daily music charts' in online digital music distributors' websites. We collected music ranking data and recommended music data from 'daily music chart' during a one month. The result shows that online music recommender systems are not fair, since they mainly recommend particular music that supported by a specific music production company. In addition, the recommended music are always exposed to the top of music ranking charts. We also find that recommended music usually appear at the top 20 ranking charts within one or two days. Also, the most music in the top 50 or 100 ranks are the recommended music. Moreover, recommended music usually remain the ranking charts more than one month while non-recommended music often disappear at the ranking charts within two week. Our study provides an important implication to online music industry. Because music recommender systems and music ranking charts are closely related, music distributors may improperly use their recommender systems to boost the sales of music that related to their own companies. Therefore, online digital music distributor must clearly announce the rules and procedures about music recommender systems for the better music industry.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.23-26
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• 2015

도서 정보 양이 급증하면서 사용자 성향과 선호도에 맞는 정보를 추천해주는 서비스의 중요성이 높아지고 있으며, 이와 관련하여 도서를 추천해주는 플랫폼 연구가 활발하게 진행되고 있다. 독자에게 성향과 선호도에 맞는 추천을 해주기 위해서는 사용자, 도서, 저자 등을 대상으로 하는 분석이 필요하며, 분석된 정보를 사용자에게 직관적으로 제공해주는 것이 필요하다. 따라서 본 논문에서는 저자에 대한 도서 정보를 시계열적으로 분석하고, 분석된 결과를 사용자에게 직관적으로 제공하는 시각화 방법을 제안한다. 제안한 방법은 저자의 도서를 시계열 방식으로 분석하고, 이를 시간 시각화와 레이더차트를 사용하여 도서정보를 제공한다. 또한 시간 시각화와 레이더 차트를 통해 두 저자의 도서 일대기와 분류의 변화를 직관적으로 확인할 수 있다.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.83-107
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• 2020

Selecting business charts based on the nature of the data and the purpose of the visualization is useful in business analysis. However, current visualization tools lack the ability to help choose the right business chart for the context. Also, soliciting expert help about visualization methods for every analysis is inefficient. Therefore, the purpose of this study is to propose an accessible method to improve business chart productivity by creating rules for selecting business charts from online published documents. To this end, Korean, English, and Chinese unstructured data describing business charts were collected from the Internet, and the relationships between the contexts and the business charts were calculated using TF-IDF. We also used a Galois lattice to create rules for business chart selection. In order to evaluate the adequacy of the rules generated by the proposed method, experiments were conducted on experimental and control groups. The results confirmed that meaningful rules were extracted by the proposed method. To the best of our knowledge, this is the first study to recommend customizing business charts through open unstructured data analysis and to propose a method that enables efficient selection of business charts for office workers without expert assistance. This method should be useful for staff training by recommending business charts based on the document that he/she is working on.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.309-316
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• 2016

While consumption patterns have changed online music, online music platform began to emerge. While people prefer popular music recommendation, they use the online music platform chart or use the SNS Platform to share information. Online platform Ranking is different because of different properties held by members. Meanwhile, we need music charts characteristics of SNS users. So there were a lot of attempts to chart a comprehensive variety of platforms. And continue to emerge theses linking the musical characteristics and SNS. In this paper, We have developed a new chart using the behavior of Twitter Users who listen to music, and studies comparing the results with existing chart.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.103-108
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• 2021

It is difficult to predict an increase or decrease of stock price because of uncertainty. Researches for prediction of stock price using AI technology have been done for a long time. Recently stock buy/sell recommend programs called by Robot Advisor using AI machine learning technology are used. In this paper, to develop a stock buy/sell recommend system using AI technology, an core engine of this system is designed. An analysis and design method of a stock buy/sell recommend system software using AI machine learning technology will be presented by showing user requirement analysis using object-oriented analysis method, flowchart and screen design.

• Korean Chemical Engineering Research
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• v.45 no.6
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• pp.638-647
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• 2007

In this study, we present the evaluation chart for assessing the applicability of $CO_2$ flooding method to oil reservoirs. The evaluation chart consists of four categories as source availability, miscibility, applicability and injecting method of miscible flooding. The applicability of reservoir and oil in the chart has basic items of the properties such as oil gravity, viscosity, oil saturation, reservoir temperature and permeability, and these are quantitatively graded. Meanwhile, for additional items of $CO_2$ purity, reservoir thickness and formation dip, they are graded as "highmediumlow". In the case of evaluating the injection method of either continuous injection or WAG ($CO_2$), the qualitative decision will be made according to formation dip, vertical permeability, reservoir thickness, etc. The recommended score in the chart was assigned by utilizing 51 oil producing fields which $CO_2$ flooding is successfully being applied. The evaluation chart developed in this work has been applied to the Captain oil producing field located in Scotland as well as to the Onado oil field of Venezuela, which Korean oil companies have participated in. For the Captain field, the reservoir quality in terms of permeability and porosity is considered to be very excellent to flow the oil. The oil in captain field contains heavier component of $C_{21+}$ as 54%. Therefore, this heavy oil could be immiscibly displaced, hence the evaluating result with the basis of immiscible criteria shows that $CO_2$ immiscible flooding in this field could be properly applied. In the case of Onado oil producing field, since the estimated minimum miscibility pressure is lower than the reservoir pressure, it was assessed that the Onado field would be efficiently conducted for $CO_2$ miscible flooding.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.527-535
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• 2021

Traditional business intelligence (BI) systems have been used widely as tools for better decision-making on time. On the other hand, building a data warehouse (DW) for the efficient analysis of rapidly growing data is time-consuming and complex. In particular, the ETL (Extract, Transform, and Load) process required to build a data warehouse has become much more complex as the BI platform moves to a cloud environment. Various BI solutions based on the NoSQL database, such as MongoDB, have been proposed to overcome these ETL issues. Decision-makers want easy access to data without the help of IT departments or BI experts. Recently, self-service BI (SSBI) has emerged as a way to solve these BI issues. This paper proposes a self-service BI system with farming data using the MongoDB cloud as DW to support the selection of new crops by return-farmers. The proposed system includes functions to provide insights to decision-makers, including data visualization using MongoDB charts, reporting for advanced data search, and monitoring for real-time data analysis. Decision makers can access data directly in various ways and can analyze data in a self-service method using the functions of the proposed system.