• Journal of the Korean Society for Library and Information Science
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• v.45 no.2
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• pp.209-228
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• 2011

Web search behavior has evolved. People now search using many diverse information devices in various situations. To monitor these scattered and shifting search patterns, an improved way of learning and analysis are needed. Traditional web search studies relied on the server transaction logs and single query instance analysis. Since people use multiple smart devices and their searching occurs intermittently through a day, a bundled query research could look at the whole context as well as penetrating search needs. To observe and analyze bundled queries, we developed a proprietary research software set including a log catcher, query bundling tool, and bundle monitoring tool. In this system, users' daily search logs are sent to our analytic server, every night the users need to log on our bundling tool to package his/her queries, a built in web survey collects additional data, and our researcher performs deep interviews on a weekly basis. Out of 90 participants in the study, it was found that a normal user generates on average 4.75 query bundles a day, and each bundle contains 2.75 queries. Query bundles were categorized by; Query refinement vs. Topic refinement and 9 different sub-categories.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.123-138
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• 2009

In recent years, the web services composition search has become an issue of great interest. The web services composition search is the process of integrating individual web services to yield desired behavior. Through the web services composition search, more sophisticated functionalities can be provided. Current solutions can be classified into three main classes: forward chaining approach, backward chaining approach and two-phase approach. However one-way chaining approaches, such as forward chaining approach and backward chaining approach have limitations of searching irrelevant web services. And two-phase approach has limitations of including redundant web services. In this paper, we propose an unredundant web services composition search based on the two-phase algorithm. The algorithm consists of a forward phase and a backward phase. In the forward phase, the candidate web services participating composition will be found efficiently by searching the Link Index. In the backward phase, unredundant web services compositions will be generated from candidate web services by using the Token Manager. The experimental results show that our proposed algorithm is more efficient than one-way chaining approaches. The experimental results also show that our algorithm can provide more solutions than previous two-phase approach and is comparable to previous one in execution time.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.7
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• pp.159-172
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• 2016

This paper presents a novel algorithm for searching evacuation paths in indoor disaster environments. The proposed method significantly improves the time complexity to find the paths to the evacuation exit by introducing a light-weight Disaster Evacuation Graph (DEG) for a building in terms of the size of the graph. With the DEG, the method also considers load balancing and bottleneck capacity of the paths to the evacuation exit simultaneously. The behavior of the algorithm consists of two phases: horizontal tiering (HT) and vertical tiering (VT). The HT phase finds a possible optimal path from anywhere of a specific floor to the evacuation stairs of the floor. Thus, after finishing the HT phases of all floors in parallel the VT phase begins to integrate all results from the previous HT phases to determine a evacuation path from anywhere of a floor to the safety zone of the building that could be the entrance or the roof of the building. It should be noted that the path produced by the algorithm. And, in order to define the range of graph to process, tiering scheme is used. In order to test the performance of the method, computing times and evacuation times are compared to the existing path searching algorithms. The result shows the proposed method is better than the existing algorithms in terms of the computing time and evacuation time. It is useful in a large-scale building to find the evacuation routes for evacuees quickly.

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.661-670
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• 2006

With the development of e-Commerce and the proliferation of easily accessible information, information filtering systems such as recommender and SDI systems have become popular to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. Until now, many information filtering methods have been proposed to support filtering systems. XML is emerging as a new standard for information. Recently, filtering systems need new approaches in dealing with XML documents. So, in this paper our system suggests a method to create multiform user profiles with XML's ability to represent structure. This system consists of two parts; one is an administrator profile definition part that an administrator defines to analyze users purchase pattern before a transaction such as purchase happens directly. an other is a user profile creation part module which is applied by the defined profile. Administrator profiles are made from DTD information and it is supposed to point the specific part of a document conforming to the DTD. Proposed system builds user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile.

• The Journal of Korean Association of Computer Education
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• v.7 no.3
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• pp.79-89
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• 2004

Social problems are coming to light due to the dysfunction which is caused by the improvement of ICT and the increasing information with computers and one of these problems is teenagers' computer toxicosis. In order to measure such an toxicosis, test paper that is developed by Kimberly S. Young is typically used. In this study, along with the test paper, localized Korean Internet addiction test (K-index) was used and the study was also made on the basis of such result. With results of sorting the data on the basis of K-index diagnosis criteria, it showed general users were 534 (64.8%), with potential risk users were 229 (27.8%) and high risk users were 61 (7.4%). This study was for the purpose of finding out sufficient and essential conditions ( outer behaviors ) of computer toxicosis for high risk users and searching for preventive measures for them.

• Fashion & Textile Research Journal
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• v.24 no.1
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• pp.81-94
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• 2022

The purpose of this study is to analyze the trends of MZ generation's lifestyle-related research from 2010 to 2020. As a result of searching keywords such as MZ generation's and lifestyle using academic database search sites, a total of 218 cases were used as analysis data to conduct frequency and content analysis. First, research type was 74 dissertations(34.6%), 144 journals(65.4%). The study of MZ generation was relatively active in journals. Second, the current status of academic field was 85(39.7%) in the social field, followed by 66(30.8%) in the arts/physical education, 21(9.8%) in the complex studies, 16(7.5%) in education, 15(7.0%) in nature, 6(2.8%) in engineering, 4(1.9%) in humanities, 1(0.5%) in agriculture/marine. Third, the current status of MZ generation research topics is 54 social participations(25.3%), 35 fashion/beauty(16.3%), 31 social/organizational adaptations(14.5%), 25 cultural/leisure activities(11.7%), 24 design/development projects(11.2%), 21 economic/employment/job projects(9.8%), 11 educational/career/experiences(5.1%), 9 self-concepts(4.2%), 4 welfare services(1.9%). Fourth, the current status of MZ generation research methods was quantitative research(survey/experiment) 125(58.4%), qualitative research(depth interview/participant observation) 42(19.6%), theory/literature research 35(16.4%) and mixed research 12(5.6%). Fifth, the study on the lifestyle of the MZ generation was conducted in four cases, one in 2016, one in 2019, two in 2020. This study is meaningful in that it grasped the overall flow of data of information exchange that can share the research trends of the MZ generation and suggested the basic data on the direction of future research, the individual tendency, behavior, and lifestyle characteristics of the MZ generation.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.1-9
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• 2018

The purpose of this exploratory study is to identify the mobile phone applications (apps) used by foreign tourists visiting South Korea through a pilot study using focus groups and individual interviews. Concentrating on tourist mobile app use in a smart tourism environment and categorized through a taxonomy of mobile applications lays the framework and determines the factors boosting tourism smartphone app trends by foreign tourists visiting South Korea. Researchers collected data through ethnographic methods and analyzed it through qualitative research to uncover major themes within the smart tourism app use phenomenon. The researchers coded, counted, analyzed, and then divided the findings gleaned from a pilot study and interviews into a taxonomy of seven logical smartphone app categories. The labeling and coding of all the data accounting for similarities and differences can be recognized and are logically discussed in the implications of the apps used by tourists to assist tourist destinations. More specifically these findings will assist smart tourism destinations by better understanding foreign tourist smartphone app use behavior. Tourists visiting South Korea interviewed in this study exhibited significant mastery of Internet of Things (IoT) technologies, craved free WiFi access, and utilized smartphone apps for all facets of their travel. Findings show major concentrations of app use in bookings of accommodations, tourist attractions, online shopping, navigation, wayfinding, augmented reality, information searching, language translation, gaming, and online dating while traveling in South Korea.

• Journal of Distribution Research
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• v.12 no.3
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• pp.69-90
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• 2007

This study is set out to investigate the factors that influence customers' behavior of choice and switching between online and offline channels, separating the purchase decision into two stages, i.e., information search and purchase. Factors influencing channel choice are found to differ from stage to stage. The main results of this study are as follows. At the information search stage, customers' channel knowledge had impacts on the choice of the channel. Customers are more likely to visit offline bookstores when they have hedonic shopping orientation and higher involvement level with books. On the contrary, customers are more apt to search online when they have a lot of online shopping experiences. At the purchase stage, the results varied according to the search channel. When customers search for information online, the following variables lead to online purchases: online shopping experiences with books, price-focused shopping orientation, and time availability for shopping. Perceived risk made customers purchase offline even though they searched online. In case of offline searching, customers with more convenience-focused, hedonic-focused shopping orientation and less tim availability purchased offline.

• 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.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.