2016, Vol.21, No.5

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

Magnetic disks have been used for decades in auxiliary storage devices of computer systems. In recent years, the use of NAND flash memory, which is called SSD, is increased as auxiliary storage devices. However, NAND flash memory, unlike traditional magnetic disks, necessarily performs the erase operation before the write operation in order to overwrite data and this leads to degrade the system lifetime and performance of overall NAND flash memory system. Moreover, NAND flash memory has the lower endurance, compared to traditional magnetic disks. To overcome this problem, this paper proposes EPET (Enhanced Prediction and Elapsed Time) wear leveling technique, which is especially efficient to portable devices. EPET wear leveling uses the advantage of PET (Prediction of Elapsed Time) wear leveling and solves long-term system failure time problem. Moreover, EPET wear leveling further improves space efficiency. In our experiments, EPET wear leveling prolonged the first bad time up to 328.9% and prolonged the system lifetime up to 305.9%, compared to other techniques.

Linked Data is a web standard data definition method devised to connect, expand resources with a standardized type. Linked Data built in various areas expands existing knowledge through an open data cloud like LOD(Linked Open Data). A project to link and service existing knowledge through LOD is under way worldwide. However, LOD project in domestic is being participated in a specific field to the level of research. In this paper, we suggests a method to build the area of technical knowledge like legislations in type of Linked Data, and distribute such Linked Data built to LOD. The construction method suggested by this paper divides knowledge of legislations in structural, semantic, and integrated perspective, and builds each of them by converting to Linked Data according to the perspective. Also, such built Linked Legal Data prepares to link knowledge in a standardized type by distributing them onto LOD. Built Linked Legal Data are equipped with schema for link service in various types, and give help increase understand the access type to existing legal information.

This paper suggests a method to refine a massive collective intelligence data, and visualize with multilevel sentiment network, in order to understand information in an intuitive and semantic way. For this study, we first calculated a frequency of sentiment words from each movie review. Second, we designed a Heatmap visualization to effectively discover the main emotions on each online movie review. Third, we formed a Sentiment-Movie Network combining the MDS Map and Social Network in order to fix the movie network topology, while creating a network graph to enable the clustering of similar nodes. Finally, we evaluated our progress to verify if it is actually helpful to improve user cognition for multilevel analysis experience compared to the existing network system, thus concluded that our method provides improved user experience in terms of cognition, being appropriate as an alternative method for semantic understanding.