• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.579-583
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• 2008

Web pages with complex layout and small font size do not display well on large screen display such as TV because it has limited capabilities: long distance view, passive user attitude, limited input device like a legacy remote controller. We have designed and implemented new semantic zoom browsing facilities to support effective navigation on Internet-connected digital television with limited capabilities. Our browser performs partitioning of an HTML document content into semantic blocks. Semantic blocks present summarized information with more readable style and modified layout for optimal reading and browsing. Individual blocks can be selected by the user and zoomed in more detail information by the user. The scrolling on large display device needs more user interaction. Our browser modifies the layout of an HTML document with removing horizontal scrolling and minimizing vertical scrolling. This method allows users to easily view the web page by converting into optimal reading style and layout and to easily seek the information just with zooming.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.326-331
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• 2008

We present the seamless browsing - a new Zooming technique based on the zoom ratios and the distances of the hyperlinks from the pointer. In most cases, users have to activate the interesting link after guessing the target content of it, just based on the insufficient information from the link label or title. We propose that a web browser displays a limited number of hyperlinks in an area around the pointer in a distinguished way, e.g. in different sizes of thumbnails, transparency or style. If a user zooms in on the pointer area, to new web browser displays the varying images of hyperlink-targets according to the zoom ratio, and finally it transfers to the new target page which was nearest to the pointer position. This method allows users to easily select a hyperlink based on rich information given by zoomable thumbnails and seamlessly to transit through web pages just with zooming. 1n our experiments, results show that the seamless browsing significantly outperforms the legacy way.

• The Journal of Society for e-Business Studies
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• v.8 no.2
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• pp.77-98
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• 2003

A web-based information system, that is a dominant type of information systems, suffers from the "web crisis" in development and maintenance of the system. To cope with the problem, a technology of software clustering to web-based application, which is one of web engineering, is strongly needed. In this paper, we propose a Form Clustering Algorithm along with an application example, which are used for internal-system reengineering to web-based information system. A Form Clustering Algorithm focuses on Page-model which is the feature of the web among the various web-based information system's structural model. Specially, we applying distance matrix to navigation model of graph form for easily analyzing, and web log analysis for identifying core function object that have a highly loading. Also, we create web software structure that can be used to maximize reusability and assign hardware effectively through 2-phase clustering step. Form Clustering Algorithm might be used at web-based information system development and maintenance for reusable web component development and hardware assignment, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.5
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• pp.179-186
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• 2014

Current networks such as social network, web page link, traffic network are big data which have the large numbers of nodes and edges. Many applications such as social network services and navigation systems use these networks. Since big networks are not fit into the memory, existing in-memory based analysis techniques cannot provide high performance. Frontier-Expansion-Merge (FEM) framework for graph search operations using three corresponding operators in the relational database (RDB) context. FEM exploits an index table that stores pre-computed partial paths for efficient shortest path discovery. However, the index table of FEM has low hit ratio because the indices are determined by distances of indices rather than the possibility of containing a shortest path. In this paper, we propose an method that construct index table using high degree nodes having high hit ratio for efficient shortest path discovery. We experimentally verify that our index technique can support shortest path discovery efficiently in real-world datasets.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.515-523
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• 2012

Objective: The aim of this study is to investigate users' intuitive stereotypes on non-touch gestures and establish the gesture dictionary that can be applied to gesture-based interaction designs. Background: Recently, the interaction based on non-touch gestures is emerging as an alternative for natural interactions between human and systems. However, in order for non-touch gestures to become a universe interaction method, the studies on what kinds of gestures are intuitive and effective should be prerequisite. Method: In this study, as applicable domains of non-touch gestures, four devices(i.e. TV, Audio, Computer, Car Navigation) and sixteen basic operations(i.e. power on/off, previous/next page, volume up/down, list up/down, zoom in/out, play, cancel, delete, search, mute, save) were drawn from both focus group interview and survey. Then, a user participatory design was performed. The participants were requested to design three gestures suitable to each operation in the devices, and they evaluated intuitiveness, memorability, convenience, and satisfaction of their derived gestures. Through the participatory design, agreement scores, frequencies and planning times of each distinguished gesture were measured. Results: The derived gestures were not different in terms of four devices. However, diverse but common gestures were derived in terms of kinds of operations. In special, manipulative gestures were suitable for all kinds of operations. On the contrary, semantic or descriptive gestures were proper to one-shot operations like power on/off, play, cancel or search. Conclusion: The touchless gesture dictionary was established by mapping intuitive and valuable gestures onto each operation. Application: The dictionary can be applied to interaction designs based on non-touch gestures. Moreover, it will be used as a basic reference for standardizing non-touch gestures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.119-126
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• 2002

Recently, as advancing the technologies for Web 3D and Virtual Reality, the studies have been progressed actively to provide three dimensional information on the web. Especially, the various applications for providing urban information in 3D space have been developed using EAI(External Authoring Interface) that serves an interface between VRML(Virtual Reality Modeling Language), standard language for embodying virtual reality, and JAVA applet in HTML. In this study, as constructing 3D virtual city information using Digital Map, IKONOS satellite image, VRML and so on, we could provide users which need several information with building location and various urban living information. In addition, applying 3D skills such as texturing, panorama and navigation, users were enabled to perform various route searching and scenery analysis. Finally, to serve urban living information in real time, we designed to search information faster through interfacing database and to update data using ASP(Active Server Page) on web.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Archives of design research
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• v.15 no.1
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• pp.143-152
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• 2002

As the Internet has been a daily instrument of our lives, the numbers of Internet users are increasing rapidly. Especially, we have to pay special attention to about rapid increasing of juvenile users. In the 1990's, Kids are growing up literally surrounded by new technologies and mu1timedia experiences. For these kids, most of the techno1ologies that we adults find surprising or even incredible are a part of their everyday landscape, a fact of life. Currently, only few of research and discussion has gone into understanding this field. And most of these web sites, set importance on furnishing information only. So educational characters of web are not manifested fully as well as children soon get board with learning with Internet so that feel difficulties in searching and accepting information. At this point, we must try to develop educational sites Not only to show information but also to offer a rich and entertaining time for kids while providing playful teaming and increased technological fluency. Fer this purpose, Web site should be all about combining play with learning. Site navigation should be easy and the pages load quickly. The page download time is also being considerable, which could send kids withy mouse-fingers looking for entertainment elsewhere. Everything about the site must have a familiar feel, uses adequate colors to be satisfied with the juveniles. Multimedia can help the communications in the websites. To maximize the educational effect, technological research and continues invest are need, in addition to usability test.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.