• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.531-536
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• 2006

Recently, many of researchers have been studied in data processing oriented middleware for wireless sensor networks with the rapid advances on sensor and wireless communication technologies. In a wireless sensor network, a middleware should handle the data loss problem at an intermediate sensor node caused by instantaneous data burstness to support efficient processing and fast delivering of the sensing data. To handle this problem, a simple data discarding or data compressing policy for reducing the total amount of data to be transferred is typically used. But, data discarding policy decreases the correctness of a collected data, in other hand, data compressing policy requires additional processing overhead with the high complexity of the given algorithm. In this paper, it proposes a data-average method for enhancing the efficiency of data aggregation and correctness where the sensed data should be delivered only with the limited computing power and energy resource. With the proposed method, unnecessary data transfer of the overlapped data is eliminated and data correctness is enhanced by using the proposed averaging scheme when an instantaneous data burstness is occurred. Finally, with the TOSSTM simulation results on TinyBB, we show that the correctness of the transferred data is enhanced.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.586-594
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• 2004

Because web documents become creation and disappearance rapidly, users require the recommend system that offers users to browse the web document conveniently and correctly. One largely untapped source of knowledge about large data collections is contained in the cumulative experiences of individuals finding useful information in the collection. Recommendation systems attempt to extract such useful information by capturing and mining one or more measures of the usefulness of the data. The existing Information Filtering system has the shortcoming that it must have user's profile. And Collaborative Filtering system has the shortcoming that users have to rate each web document first and in high-quantity, low-quality environments, users may cover only a tiny percentage of documents available. And dynamic recommendation system using the user browsing pattern also provides users with unrelated web documents. This paper classifies these web documents using the similarity between the web documents under the web document type and extracts the user browsing sequential pattern DB using the users' session information based on the web server log file. When user approaches the web document, the proposed Dynamic recommendation system recommends Top N-associated web documents set that has high similarity between current web document and other web documents and recommends set that has sequential specificity using the extracted informations and users' session information.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.17-22
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• 2010

As deployments of Flash memory are spreading out rapidly from tiny USB storages to large DB servers, interoperability become an indispensable requirement for Flash memory software architecture. For the purpose, many systems make use of the conventional FAT file system and FTL (Flash Translation Layer) software as a de facto standard. However, the tactless combination of the FAT file system and FTL does not satisfy diverse other requirements of a variety of systems. In this paper, we propose a novel reconfigurable integrated Flash memory software architecture, named INFLAWARE (INtegrated FLAsh softWARE) that supports not only interoperability but also reconfigurability and performance enhancement. Real implementation based experimental results have shown that INFLAWARE can achieve improvements of memory footprint up to 27% with an average of 19%, compared with the conventional FAT and FTL combination. Also, by using map_destroy technique, it can reduce response times of various applications up to 21% with an average of 10%.