• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1017-1024
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• 2004

Ubiquitous system requires intelligent environment and system that perceives context in a proactive manner. This paper describes proactive retrieval method using context patterns in ubiquitous computing. And as the user's contexts change, new information is delivered proactively based on user's context patterns. For proactive retrieval, we extract context patterns based on sequential pattern discovery and association rule in data mining. By storing only information to be needed in near future using the context patterns, we solved the problem of speed and storage capacity of mobile devices in ubiquitous computing. We explain algorithms and an example. Several experiments are performed and the experimental results show that our method has a good information retrieval.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.8-13
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• 2007

The context-aware environment focuses on recognizing the context and physical entities. For this reason, there has been an increasement in research of context-aware computing environment. Ontology-based context models are widely used in ubiquitous environment because of context sharing and reusing. In this paper, we propose a proactive retrieval method using ontology in context-aware environment. The method use a concept level of hierarchical concept tree in ontology for more efficient retrieval. This paper describes the proactive retrieval method and ontology model. Several experiments are performed and the results verify that the proposed method's efficiency is better than other existing methods.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.721-738
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• 2012

The Internet has become an increasingly important source for finding the right employees, so more and more companies post their job openings on the Web. The large amount and dynamic nature of career recruiting information causes information overload problems for job seekers. To assist Internet users in searching for the right job, a range of research and commercial systems were developed over the past 10 years. Surprisingly, the majority of existing job search systems support just one, rarely two ways of information access. In contrast, our work focused on exploring a value of comprehensive access to job information in a single system (i.e., a system which supports multiple ways). We designed Proactive, a recommendation system providing comprehensive and personalized information access. To assist the varied needs of users, Proactive has four information retrieval methods - a navigable list of jobs, keyword-based search, implicit preference-based recommendations, and explicit preference-based recommendations. This paper introduces the Proactive and reports the results of a study focusing on the experimental evaluation of these methods. The goal of the study was to assess whether all of the methods are necessary for users to find relevant jobs and to what extent different methods can meet different users' information requirements.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.43-49
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• 2000

The World Wide Web has become a vast information resource where virtually any information can be found. There is a pressing need for appropriate Web search tools due to the very vastness of information space, the rate of growth, and the volatility of data. The agent technology has been studied to address these issues and led to the creation of an adaptive, Proactive. personalized search tools. This study looks into the mechanism of collaborative filtering of information and suggests a decentralized collaborating agents model for information discovery.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.628-645
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• 2014

In past few years, distributed hash table (DHT)-based P2P systems have been proven to be a promising way to manage decentralized index information and provide efficient lookup services. However, the skewness of users' preferences regarding keywords contained in a multi-keyword query causes a query load imbalance that combines both routing and response load. This imbalance means long file retrieval latency that negatively influences the overall system performance. Although index replication has a great potential for alleviating this problem, existing schemes did not explicitly address it or incurred high cost. To overcome this issue, we propose, in this paper, an integrated solution that consists of three replication schemes to alleviate query load imbalance while minimizing the cost. The first scheme is an active index replication that is used in order to decrease routing load in the system and to distribute response load of an index among peers that store replicas of the index. The second scheme is a proactive pointer replication that places location information of each index to a predetermined number of peers for reducing maintenance cost between the index and its replicas. The third scheme is a passive index replication that guarantees the maximum query load of peers. The result of simulations indicates that the proposed schemes can help alleviate the query load imbalance of peers. Moreover, it was found by comparison that our schemes are more cost-effective on placing replicas than PCache and EAD.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.255-263
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• 2015

In this paper, we suggest a plan to take advantage of the SNS data to proactively identify the information on crime risk factor and to prevent crime. Recently, SNS(Social Network Service) data have been used to build a proactive prevention system in a variety of fields. However, when users are collecting SNS data with simple keyword, the result is contain a large amount of unrelated data. It may possibly accuracy decreases and lead to confusion in the data analysis. So we present a method that can be efficiently extracted by improving the search accuracy through text mining analysis of SNS data.