• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.155-175
• /
• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• The Journal of Society for e-Business Studies
• /
• v.13 no.3
• /
• pp.85-101
• /
• 2008

The semantic web is taken as next generation standards of information exchange on the internet to overcome the limitations of the current web. To utilize the information on the semantic web, tools are required the functionality of query search and reasoning for the ontology. However, most of semantic web management tools cannot efficiently support the search for the complex query because they apply Triple-based storage structure about RDF metadata. We design the storage structure of the ontology in corresponding with the structure of ontology data and develop the search system(SMART-DLTriple) to support complex query search efficiently in this research. The performance of the system using new storage structure is evaluated to compare with the popular semantic web management systems. The proposed method and system make a contribution to enhancement of a practical ontology reasoning systems due to improved performance of the ontology search.

• Journal of Korea Multimedia Society
• /
• v.15 no.9
• /
• pp.1165-1173
• /
• 2012

One of the most popular technologies in Web2.0 is tagging, and it widely applies to Web content as well as multimedia data such as image and video. Web users have expected that tags by themselves would be reused in information search and maximize the search efficiency, but wrong tag by irresponsible Web users really has brought forth a incorrect search results. In past papers, we have gathered various information resources and tags scattered in Web, mapped one tag onto other tags, and clustered these tags according to the corelation between them. A 3-tag based search algorithm which use the clustered tags of past papers, is proposed in this paper. For performance evaluation of the proposed algorithm, our algorithm is compared with image search result of Flickr, typical tag based site, and is evaluated in accuracy and recall factor.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.1-9
• /
• 2005

Today, according to tremendous development of B2B e-commerce, B2B e-marketplaces which accomplish various types of transactions through a number of buyers and sellers on online are embossed importantly. However, buyers are unable to search correct products because of inconsistency of product information between buyers and sellers. This paper solved this problem as semantic web technology. Semantic Web is an extension of current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. The Semantic Web aims at machine-processable information. Its underlying technologies are RDF, RDF Schema, and ontology as the shared formal conceptualization of particular domains. In this paper, we present an implementation of Semantic Web enabled search system for B2B E-Marketplace domains. The system exploits OWL as the standard ontology language proposed by W3C and the Jena which is a Semantic Web toolkit, namely a Java framework writing Semantic Web applications. Finally, we summarize our experiences and discuss future research topics.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.266-272
• /
• 2002

Recently various of information suppliers provide information via WWW so the necessary of search engine grows larger. However the efficiency of most search engines is low comparatively because of using simple pattern match technique between user's query and web document. And a manifest contents of query for special expert field so much worse A specialized search engine returns the specialized information depend on each user's search goal. It is trend to develop specialized search engines in many countries. For example, in America, there are a site that searches only the recently updated headline news and the federal law and the government and and so on. However, most such engines don't satisfy the user's needs. This paper proposes the specialized search engine for ocean information that uses user's query related with ocean and search engine uses the association rules in web data mining. So specialized search engine for ocean provides more information related to ocean because of raising recall about user's query

• Journal of the Korean Society for information Management
• /
• v.21 no.2
• /
• pp.89-106
• /
• 2004

There are lots of Web materials in the field of medicine and many search engines classify the medical materials on the Web through directories. But the organization of these directories are wanting in consistency and systematization. In order for manager of search engines to organize medical materials on the Web systematically, this paper suggests several guidelines. NLMC, a special classification system for medicine, need to be applied to develop directories of medicine in search engines. Also, items of the directories should be arranged based on the relevance of subjects among subfields of medical science. For classifying an item to several directories repeatedly, clear criteria should be established. In addition to, controlled vocabularies or glossaries for medicine such as MeSH and the English-Korean, Korean-English Medical Terminology Collection should be used for selection of the name of items in medical directories.

• Journal of the Korean Society for information Management
• /
• v.22 no.2 s.56
• /
• pp.205-228
• /
• 2005

This research analyzed changes of the intellectual structure of web information by examining time changes and search engines using the co-links analysis. According to the results, the co-links web information clusters on the two maps appeared to contain changes in the intellectual structure over the two time periods. The intellectual structure that appeared in the information map for AltaVista and MSN Search engines was relatively similar. However. there were also cases where the clusters of some web information was different. The results of the research revealed that the cocitation analysis could be applied simultaneously to diachronous analysis in the web information.

• Journal of the Korean Society for information Management
• /
• v.32 no.1
• /
• pp.7-22
• /
• 2015

This paper describes a Web search optimization study that investigates both static and dynamic tuning methods for optimizing system performance. We extended the conventional fusion approach by introducing the "dynamic tuning" process with which to optimize the fusion formula that combines the contributions of diverse sources of evidence on the Web. By engaging in iterative dynamic tuning process, where we successively fine-tuned the fusion parameters based on the cognitive analysis of immediate system feedback, we were able to significantly increase the retrieval performance. Our results show that exploiting the richness of Web search environment by combining multiple sources of evidence is an effective strategy.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.6
• /
• pp.402-406
• /
• 2007

Rich Internet Application(RIA) is one of the Web 2.0 technologies and is expected to be a next generation user interface technique which allows flexible and dynamic manipulation for Web searches. This paper addresses a personalization mechanism for advanced Web search using RIA for abundant user interactions. We devised a dynamic and graphical user interface instead of previous text-based searches and a client side application for storing personal preference information. In this research, we implemented the graphical personalized search manager using Yahoo web search API and widget, and demonstrated its effectiveness by performing some experiments with various query terms and representative predicates.

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