2015, Vol.20, No.4

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

NAND flash systems require deletion operation and do not support in-place update, so the storage systems should use Flash Translation Layer (FTL). However, there are a lot of memory consumptions using mapping table in the FTL, so recently, many studies have been proposed to resolve mapping table overhead. These studies try to solve update propagation problem in the nand flash system which does not use mapping table. In this paper, we present a novel index structure, called CL-Tree(Cache List Tree), to solve the update propagation problem. The proposed index structure reduces write operations which occur for an update propagation, and it has a good performance for search operation because it uses multi-list structure. In experimental evaluation, we show that our scheme yields about 173% and 179% improvement in insertion speed and search speed, respectively, compared to traditional B+tree and other works.

Data center, not only the development of globally of cloud-based IT solutions, As the big data, education, use of development electronic equipment of communication means is increased, Demand of domestic and international data center has increased steadily. Due to the increase of data center demand, heat generation of server rack due to the development of IT equipment is also increasing continuously. Calculating heat value of the data center, because more than 99% of the power usage of IT server is converted into heat, the calorific value as the size and capacity of the server is larger will increase. In this paper, to center the cooling system of air-cooled, and research and development of air conditioning systems for energy reduction of data center, through performance analysis and simulation, it has been analyzed that there is energy savings.

In this paper, we propose a mobile app clustering method using word clusters. Considering the quick change of mobile app trends, the proposed method divides the mobile apps into some semantically similar mobile apps by applying a clustering algorithm to the mobile app set, rather than the predefined category system. In order to alleviate the data sparseness problem in the short mobile app description texts, the proposed method additionally utilizes the unigram, the bigram, the trigram, the cluster of each word. For the purpose of accurately clustering mobile apps, the proposed method manages to avoid exceedingly small or large mobile app clusters by using the word clusters. Experimental results show that the proposed method improves 22.18% from 57.48% to 79.66% on overall accuracy by using the word clusters.