• Title/Summary/Keyword: multi-list structure

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CL-Tree: B+ tree for NAND Flash Memory using Cache Index List (CL 트리: 낸드 플래시 시스템에서 캐시 색인 리스트를 활용하는 B+ 트리)

  • Hwang, Sang-Ho;Kwak, Jong Wook
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.4
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    • pp.1-10
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    • 2015
  • 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.

Multi-Sized cumulative Summary Structure Driven Light Weight in Frequent Closed Itemset Mining to Increase High Utility

  • Siva S;Shilpa Chaudhari
    • Journal of information and communication convergence engineering
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    • v.21 no.2
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    • pp.117-129
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    • 2023
  • High-utility itemset mining (HIUM) has emerged as a key data-mining paradigm for object-of-interest identification and recommendation systems that serve as frequent itemset identification tools, product or service recommendation systems, etc. Recently, it has gained widespread attention owing to its increasing role in business intelligence, top-N recommendation, and other enterprise solutions. Despite the increasing significance and the inability to provide swift and more accurate predictions, most at-hand solutions, including frequent itemset mining, HUIM, and high average- and fast high-utility itemset mining, are limited to coping with real-time enterprise demands. Moreover, complex computations and high memory exhaustion limit their scalability as enterprise solutions. To address these limitations, this study proposes a model to extract high-utility frequent closed itemsets based on an improved cumulative summary list structure (CSLFC-HUIM) to reduce an optimal set of candidate items in the search space. Moreover, it employs the lift score as the minimum threshold, called the cumulative utility threshold, to prune the search space optimal set of itemsets in a nested-list structure that improves computational time, costs, and memory exhaustion. Simulations over different datasets revealed that the proposed CSLFC-HUIM model outperforms other existing methods, such as closed- and frequent closed-HUIM variants, in terms of execution time and memory consumption, making it suitable for different mined items and allied intelligence of business goals.

Multi-Exchange Neighborhood Search Heuristics for the Multi-Source Capacitated Facility Location Problem

  • Chyu, Chiuh-Cheng;Chang, Wei-Shung
    • Industrial Engineering and Management Systems
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    • v.8 no.1
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    • pp.29-36
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    • 2009
  • We present two local-search based metaheuristics for the multi-source capacitated facility location problem. In such a problem, each customer's demand can be supplied by one or more facilities. The problem is NP-hard and the number of locations in the optimal solution is unknown. To keep the search process effective, the proposed methods adopt the following features: (1) a multi-exchange neighborhood structure, (2) a tabu list that keeps track of recently visited solutions, and (3) a multi-start to enhance the diversified search paths. The transportation simplex method is applied in an efficient manner to obtain the optimal solutions to neighbors of the current solution under the algorithm framework. Two in-and-out selection rules are also proposed in the algorithms with the purpose of finding promising solutions in a short computational time. Our computational results for some of the benchmark instances, as well as some instances generated using a method in the literature, have demonstrated the effectiveness of this approach.

An Adaptive Virtual Machine Location Selection Mechanism in Distributed Cloud

  • Liu, Shukun;Jia, Weijia
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.12
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    • pp.4776-4798
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    • 2015
  • The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

Implementation of Kernel Module for Shared Memory in Dual Bus System (듀얼 버스 시스템에서의 공유 메모리 커널 모듈 구현)

  • Moon, Ji-Hoon;Oh, Jae-Chul
    • The Journal of the Korea institute of electronic communication sciences
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    • v.10 no.5
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    • pp.539-548
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    • 2015
  • In this paper, shared memory feature was developed in multi-core system with different OS for different processor-specific bus, while conducting an experiment on shared memory feature between the two processors based on embedded Linux system. For the purpose of developing shared memory in dual bus structure, memory controller was used, while managing shared memory segment through list data structure. For AMP multi-core test, Linux OS was installed in 2 processor cores. In addition, it verified the creation and use of shared memory by using kernel module implemented to test shared memory.

Relational 데이타 모형을 구현하는 씨스템 설계

  • 趙廷完;嚴基賢 = Um Ki Hyun
    • Communications of the Korean Institute of Information Scientists and Engineers
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    • v.4 no.2
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    • pp.34-44
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    • 1986
  • A data base system for a minicomputer, designed on the basis of the concept of Relational model, is proposed in this thesis. It is a module of reentrant programs, which can serve multi-users concurrently and interactivelv. Relational calculus is chosen as a data sublanguage. The inverted-list file structure is used for the physical storage structuse with the technique of seperating data and their relationships, while data model records, which contain the information of the logical data organization and linkage to a physical structure, con struct the data model. The retrieval is performed mainly with these.

Work Domain Analysis Based on Abstraction Hierarchy: Modelling Concept and Principles for Its Application (추상화계층에 기반한 작업영역분석의 모델링 개념 및 적용 원칙)

  • Ham, Dong-Han
    • Journal of the Korea Safety Management & Science
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    • v.15 no.3
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    • pp.133-141
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    • 2013
  • As a work analysis technique, Work Domain Analysis (WDA) aims to identify the design knowledge structure of a work domain that human operators interact with through human-system interfaces. Abstraction hierarchy (AH) is a multi-level, hierarchical knowledge representation framework for modeling the functional structure of any kinds of systems. Thus, WDA based on AH aims to identify the functional knowledge structure of a work domain. AH has been used in a range of work domains and problems to model their functional knowledge structure and has proven its generality and usefulness. However, many of researchers and system designers have reported that it is never easy to understand the concepts underlying AH and use it effectively for WDA. This would be because WDA is a form of work analysis that is different from other types of work analysis techniques such as task analysis and AH has several unique characteristics that are differentiated from other types of function analysis techniques used in systems engineering. With this issue in mind, this paper introduces the concepts of WDA based on AH and offers a comprehensive list of references. Next, this paper proposes a set of principles for effectively applying AH for work domain analysis, which are developed based on the author's experiences, consultation with experts, and literature reviews.

A Concurrency Control Method for Non-blocking Search Operation based on R-tree (논 블록킹 검색연산을 위한 R-tree 기반의 동시성 제어 기법)

  • Kim, Myung-Keun;Bae, Hae-Young
    • The KIPS Transactions:PartD
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    • v.11D no.4
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    • pp.809-822
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    • 2004
  • In this paper, we propose a concurrency control algorithm based on R-tree for spatial database management system. The previous proposed algorithms can't prevent problem that search operation is to be blocking during update operations. In case of multidimensional indexes like R-tree, locking of update operations may be locked to several nodes, and splitting of nodes have to lock a splitting node for a long time. Therefore search operations have to waiting a long time until update operations unlock. In this paper we propose new algorithms for lock-free search operation. First, we develop a new technique using a linked-list technique on the node. The linked-list enable lock-free search when search operations search a node. Next, we propose a new technique using a version technique. The version technique enable lock-free search on the node that update operations is to be splitting.

Public Sentiment Analysis of Korean Top-10 Companies: Big Data Approach Using Multi-categorical Sentiment Lexicon (국내 주요 10대 기업에 대한 국민 감성 분석: 다범주 감성사전을 활용한 빅 데이터 접근법)

  • Kim, Seo In;Kim, Dong Sung;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.22 no.3
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    • pp.45-69
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    • 2016
  • Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

MULTI-SCALE MODELS AND SIMULATIONS OF NUCLEAR FUELS

  • Stan, Marius
    • Nuclear Engineering and Technology
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    • v.41 no.1
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    • pp.39-52
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    • 2009
  • Theory-based models and high performance simulations are briefly reviewed starting with atomistic methods, such as Electronic Structure calculations, Molecular Dynamics, and Monte Carlo, continuing with meso-scale methods, such as Dislocation Dynamics and Phase Field, and ending with continuum methods that include Finite Element and Finite Volume. Special attention is paid to relating thermo-mechanical and chemical properties of the fuel to reactor parameters. By inserting atomistic models of point defects into continuum thermo-chemical calculations, a model of oxygen diffusivity in $UO_{2+x}$ is developed and used to predict point defect concentrations, oxygen diffusivity, and fuel stoichiometry at various temperatures and oxygen pressures. The simulations of coupled heat transfer and species diffusion demonstrate that including the dependence of thermal conductivity and density on composition can lead to changes in the calculated centerline temperature and thermal expansion displacements that exceed 5%. A review of advanced nuclear fuel performance codes reveals that the many codes are too dedicated to specific fuel forms and make excessive use of empirical correlations in describing properties of materials. The paper ends with a review of international collaborations and a list of lessons learned that includes the importance of education in creating a large pool of experts to cover all necessary theoretical, experimental, and computational tasks.