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AFTL: An Efficient Adaptive Flash Translation Layer using Hot Data Identifier for NAND Flash Memory (AFTL: Hot Data 검출기를 이용한 적응형 플래시 전환 계층)

  • Yun, Hyun-Sik;Joo, Young-Do;Lee, Dong-Ho
    • Journal of KIISE:Computer Systems and Theory
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    • v.35 no.1
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    • pp.18-29
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    • 2008
  • NAND Flash memory has been growing popular storage device for the last years because of its low power consumption, fast access speed, shock resistance and light weight properties. However, it has the distinct characteristics such as erase-before-write architecture, asymmetric read/write/erase speed, and the limitation on the number of erasure per block. Due to these limitations, various Flash Translation Layers (FTLs) have been proposed to effectively use NAND flash memory. The systems that adopted the conventional FTL may result in severe performance degradation by the hot data which are frequently requested data for overwrite in the same logical address. In this paper, we propose a novel FTL algorithm called Adaptive Flash Translation Layer (AFTL) which uses sector mapping method for hot data and log-based block mapping method for cold data. Our system removes the redundant write operations and the erase operations by the separating hot data from cold data. Moreover, the read performance is enhanced according to sector translation that tends to use a few read operations. A series of experiments was organized to inspect the performance of the proposed method, and they show very impressive results.

Development of Sorption Database (KAERI-SDB) for the Safety Assessment of Radioactive Waste Disposal (방사성폐기물 처분안전성 평가 자료 제공을 위한 핵종 수착 데이터베이스(KAERI-SDB) 개발)

  • Lee, Jae-Kwang;Baik, Min-Hoon;Jeong, Jongtae
    • Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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    • v.11 no.1
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    • pp.41-54
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    • 2013
  • Radionuclide sorption data is necessary for the safety assessment of radioactive waste disposal. However the use of sorption database is often limited due to the accessability. A web-based sorption database program named KAERI-SDB has been developed to provide information on the sorption of radionuclides onto geological media as a function of geochemical conditions. The development of KAERI-SDB was achieved by improving the performance of pre-existing sorption database program (SDB-21C) developed in 1998 and considering user's requirements. KAERI-SDB is designed that users can access it by using a web browser. Main functions of KAERI-SDB include (1) log-in/member join, (2) search and store of sorption data, and (3) chart expression of search results. It is expected that KAERI-SDB could be widely utilized in the safety assessment of radioactive waste disposal by enhancing the accessibility to users who wants to use sorption data. Moreover, KAERI-SDB opened to public would also improve the reliability and public acceptance on the radioactive waste disposal programs.

A Content-Aware toad Balancing Technique Based on Histogram Transformation in a Cluster Web Server (클러스터 웹 서버 상에서 히스토그램 변환을 이용한 내용 기반 부하 분산 기법)

  • Hong Gi Ho;Kwon Chun Ja;Choi Hwang Kyu
    • Journal of Internet Computing and Services
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    • v.6 no.2
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    • pp.69-84
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    • 2005
  • As the Internet users are increasing rapidly, a cluster web server system is attracted by many researchers and Internet service providers. The cluster web server has been developed to efficiently support a larger number of users as well as to provide high scalable and available system. In order to provide the high performance in the cluster web server, efficient load distribution is important, and recently many content-aware request distribution techniques have been proposed. In this paper, we propose a new content-aware load balancing technique that can evenly distribute the workload to each node in the cluster web server. The proposed technique is based on the hash histogram transformation, in which each URL entry of the web log file is hashed, and the access frequency and file size are accumulated as a histogram. Each user request is assigned into a node by mapping of (hashed value-server node) in the histogram transformation. In the proposed technique, the histogram is updated periodically and then the even distribution of user requests can be maintained continuously. In addition to the load balancing, our technique can exploit the cache effect to improve the performance. The simulation results show that the performance of our technique is quite better than that of the traditional round-robin method and we can improve the performance more than $10\%$ compared with the existing workload-aware load balancing(WARD) method.

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Development of Identity-Provider Discovery System leveraging Geolocation Information (위치정보 기반 식별정보제공자 탐색시스템의 개발)

  • Jo, Jinyong;Jang, Heejin;Kong, JongUk
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.9
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    • pp.1777-1787
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    • 2017
  • Federated authentication (FA) is a multi-domain authentication and authorization infrastructure that enables users to access nationwide R&D resources with their home-organizational accounts. An FA-enabled user is redirected to his/her home organization, after selecting the home from an identity-provider (IdP) discovery service, to log in. The discovery service allows a user to search his/her home from all FA-enabled organizations. Users get troubles to find their home as federation size increases. Therefore, a discovery service has to provide an intuitive way to make a fast IdP selection. In this paper, we propose a discovery system which leverages geographical information. The proposed system calculates geographical proximity and text similarity between a user and organizations, which determines the order of organizations shown on the system. We also introduce a server redundancy and a status monitoring method for non-stop service provision and improved federation management. Finally, we deployed the proposed system in a real service environment and verified the feasibility of the system.

Multi User-Authentication System using One Time-Pseudo Random Number and Personal DNA STR Information in RFID Smart Card (RFID 스마트카드내 DNA STR Information과 일회용 의사난수를 사용한 다중 사용자 인증시스템)

  • Sung, Soon-Hwa;Kong, Eun-Bae
    • The KIPS Transactions:PartC
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    • v.10C no.6
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    • pp.747-754
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    • 2003
  • Thia paper suggests a milti user-authentication system comprises that DNA biometric informatiom, owner's RFID(Radio Frequency Identification) smartcard of hardware token, and PKI digital signqture of software. This system improved items proposed in [1] as follows : this mechanism provides one RFID smartcard instead of two user-authentication smartcard(the biometric registered seal card and the DNA personal ID card), and solbers user information exposure as RFID of low proce when the card is lost. In addition, this can be perfect multi user-autentication system to enable identification even in cases such as identical twins, the DNA collected from the blood of patient who has undergone a medical procedure involving blood replacement and the DNA of the blood donor, mutation in the DNA base of cancer cells and other cells. Therefore, the proposed system is applied to terminal log-on with RFID smart card that stores accurate digital DNA biometric information instead of present biometric user-authentication system with the card is lost, which doesn't expose any personal DNA information. The security of PKI digital signature private key can be improved because secure pseudo random number generator can generate infinite one-time pseudo randon number corresponding to a user ID to keep private key of PKI digital signature securely whenever authenticated users access a system. Un addition, this user-authentication system can be used in credit card, resident card, passport, etc. acceletating the use of biometric RFID smart' card. The security of proposed system is shown by statistical anaysis.

A Multimodal Profile Ensemble Approach to Development of Recommender Systems Using Big Data (빅데이터 기반 추천시스템 구현을 위한 다중 프로파일 앙상블 기법)

  • Kim, Minjeong;Cho, Yoonho
    • Journal of Intelligence and Information Systems
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    • v.21 no.4
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    • pp.93-110
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    • 2015
  • The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

Growth Performance, Humoral Immune Response and Carcass Characteristics of Broiler Chickens Fed Alkali Processed Karanj Cake Incorporated Diet Supplemented with Methionine

  • Panda, K.;Sastry, V.R.B.;Mandal, A.B.
    • Asian-Australasian Journal of Animal Sciences
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    • v.18 no.5
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    • pp.677-681
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    • 2005
  • A study was conducted to see the effect of dietary incorporation of alkali (1.5% NaOH, w/w) processed solvent extracted karanj cake (SKC) supplemented with methionine on growth performance, humoral immune response and carcass characteristics of broiler chickens from 0 to 8 weeks of age. One hundred and twenty, day- old broiler chicks were wing banded, vaccinated against Marek' disease and distributed in a completely randomized design (CRD) into 3 groups of 40 chicks each, which was further replicated to 4 and fed on diet containing soybean meal and those of test groups were fed diets containing alkali (1.5% NaOH) treated SKC partially replacing soybean meal nitrogen of reference diet (12.5%) without or with supplementation of methionine (0.2%). Individual body weight of chicks and replicate-wise feed intakes were recorded at weekly intervals throughout the experimental period. Feed consumption from 1 to 14, 28, 42 and 56 d of age was recorded for each replicate and feed conversion efficiency (weight gain/feed intake) for the respective period was calculated. Mortality was monitored on daily basis. On 28$^{th}$ day of experimental feeding, two birds of each replicate in each dietary group (8 birds/diet) were inoculated with 0.1 ml of a 1.0% suspension of sheep red blood cells (SRBC) and the antibody titre (log 2) was measured after 5 days by the microtitre haemmagglutination procedure. After 42 days of experimental feeding, a retention study of 4 days (43-47 d) duration was conducted on all birds to determine the retention of various nutrients such as DM, N, Ca, P and GE. On 43$^{rd}$ day of experimental feeding, one representative bird from each replicate of a dietary treatment (4/dietary group) was sacrificed, after fasting for two hours with free access to water, through cervical dislocation to observe the weight of dressed carcass, primal cuts (breast, thigh, drumstick, back, neck and wing), giblet (liver, heart and gizzard), abdominal fat and digestive organs. The body weight gain of chicks fed reference diet and those fed diet incorporated with NaOH treated SKC (12.5% replacement) with or without methionine supplementation was comparable during 0 to 4 weeks of age. However, dietary incorporation of alkali processed SKC replacing 12.5% nitrogen moiety of soybean meal resulted in growth retardation, subsequently as evidenced by significantly (p<0.05) lowered body weight gain during 0 to 6 weeks of age in birds fed diet incorporated with alkali processed SKC at 6.43% without methionine as compared to those supplemented with methionine or reference diet. Dietary incorporation of alkali (1.5% NaOH) processed SKC replacing 12.5% of soybean meal nitrogen in the diet of broiler chickens had no adverse effect on feed conversion ratio during all the weeks of experimental feeding. The humoral immune response (HIR) as measured by the antibody titre in response to SRBC inoculation was comparable among all the dietary groups. No significant difference in the intake and retention of DM, N, Ca, P or GE was noted among the chicks fed reference and alkali processed SKC incorporated diets with or without methionine supplementation. None of the carcass traits varied significantly due to dietary variations, except the percent weight of liver and giblet. The percent liver weight was significantly (p<0.05) higher in the birds fed diet incorporated with alkali processed SKC as compared to that in other two groups. Thus solvent extracted karanj cake could be incorporated after alkali (1.5% NaOH, w/w) processing at an enhanced level of 6.43%, replacing 12.5% of soybean meal nitrogen, in the broiler diets up to 4 weeks of age, beyond which the observed growth depression on this diet could be alleviated by 0.2% methionine supplementation.

Intelligent Brand Positioning Visualization System Based on Web Search Traffic Information : Focusing on Tablet PC (웹검색 트래픽 정보를 활용한 지능형 브랜드 포지셔닝 시스템 : 태블릿 PC 사례를 중심으로)

  • Jun, Seung-Pyo;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.19 no.3
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    • pp.93-111
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    • 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.