• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.77-83
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• 2022

Nand flash memory requires an Erase-Before-Write operation structurally. In order to solve this problem, it can be solved by classifying a page (hot data page) where data update operation occurs frequently and storing it in a separate block. The MHF (Multi Hash Function Framework) technique records the frequency of data update requests in the system memory, and when the recorded value exceeds a certain standard, the data update request is judged as hot data. However, the method of simply counting only the frequency of the data update request has a limit in judging it as accurate hot data. In addition, in the case of a technique that determines the persistence of a data update request, the fact of the update request is recorded sequentially based on a time interval and then judged as hot data. In the case of such a persistence-based method, its implementation and operation are complicated, and there is a problem of inaccurate judgment if frequency is not considered in the update request. This paper proposes a lightweight hot data determination technique that considers both frequency and persistence in data update requests.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.44-49
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• 2016

Recently, data has been growing dramatically along with the growth of social media and digital devices. A distributed memory processing system has been used to efficiently process large amounts of data. However, if a load is concentrated in a certain node in distributed environments, a node performance significantly degrades. In this paper, we propose a load balancing scheme to distribute load in a distributed memory environment. The proposed scheme replicates hot data to multiple nodes for managing a node's load and migrates the data by considering the load of the nodes when nodes are added or removed. The client reduces the number of accesses to the central server by directly accessing the data node through the metadata information of the hot data. In order to show the superiority of the proposed scheme, we compare it with the existing load balancing scheme through performance evaluation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.6
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• pp.797-803
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• 2010

A vehicle door impact beam made of a thin sheet of steel has been constructed using hot stamping technology with the aim of ensuring occupant safety in the event of a side collision. This technology has been used to increase the strength of the vehicle body parts and to reduce the weight of the door impact beam as well as the number of work processes. Mechanical tests were performed to determine the material properties of the hot-stamped specimen and the results of the tests were used as input data in stamping and structural simulation in order to obtain the optimal design of door impact beam. The strength of the hot-stamped door impact beam increased to a value that was 102% higher than that of conventional pipe-shaped door impact beam. A weight reduction of 34% was also achieved.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.83-92
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• 2009

Complicated business logic makes program complexity more complicated. It's inevitable that the program must undergo reengineering processes all the way of in its lifetime. Hot spot analysis that has diverse purposes is getting an important question more and more. As a rule, reengineering process is done by UML model-based approach to analyze the legacy system. The smallest fragment of targets to be analysed is unit, that is function or class. Today's software development is to deal with huge change of software product and huge class including heavy quantity of LOC(Lines Of Code). However, analysis of unit is not precise approach process for reliable reengineering consequence. In this paper, we propose very precise hot spot analysis approach using Aspect-Oriented Programming languages, such as AspectJ. Typically the consistency between UML and source is needed code to redefine the modified library or framework boundaries. But reengineering approach using AOP doesn't need to analyze UML and source code. This approach makes dynamic event log data that contains detailed program interaction information. This dynamic event log data makes it possible to analyze hot spot.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1167-1175
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• 2016

This paper proposes an efficient page-level mapping algorithm that reduces the erase count in the FTL for flash memory systems. By maintaining the weight for each write request in the request buffer, the proposed algorithm estimates the degree of temporal locality for each incoming write request. To exploit temporal locality deliberately for determination of hot request, the degree of temporal locality should be much higher than the reference point determined experimentally. While previous LRU algorithm treats a new write request to have high temporal locality, the proposed algorithm allows write requests that are estimated to have high temporal locality to access hot blocks to store hot data intensively. The pages are more frequently updated in hot blocks than warm blocks. A hot block that has most of invalid pages is always selected as victim block at Garbage Collection, which results in delayed erase operation and in reduced erase count. Experimental results show that erase count is reduced by 9.3% for real I/O workloads, when compared to the previous LRU algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.367-369
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• 2004

웹 서비스는 차세대 기술임에도 불구하고, 비즈니스 측면의 중요한 요소인 보안, 핫 스왑핑, 과금 등을 지원하지 않는 문제점을 가지고 있다. 이러한 문제를 해결하기 위해서 본 논문에서는 웹 서비스를 보다 쉽게 배포하고, 사용할 수 있으면서 비즈니스에서 중요한 특성을 지원할 수 있는 프레임워크를 소개한다. 이 프레임워크는 웹 서비스 컨테이너위에서 메타 데이터를 이용해서 자동으로 보안, 탓 스와핑, 과금 등의 기능을 제공한다. 따라서 이 프레임워크를 사용하는 경우에 보다 쉽게 웹 서비스를 개발 및 사용할 수 있기 때문에 시스템 개발에서 많은 비용과 노력을 줄일 수 있을 것이다.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.24-36
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• 2015

Recently, interests in detecting hot topics have been significantly growing as it becomes important to find out and analyze meaningful information from the large amount of data which flows in from social network services. Since it deals with a number of random writings that are not confirmed in advance due to the characteristics of SNS, there is a problem that the reliability of the results declines when hot topics are predicted from the writings. To solve such a problem, this paper proposes a high reliable hot topic prediction scheme considering user influences in social networks. The proposed scheme extracts a set of keywords with hot issues instantly through the modified TF-IDF algorithm based on Twitter. It improves the reliability of the results of hot topic prediction by giving weights of user influences to the tweets. To show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation. Our experimental results show that our proposed method has improved precision and recall compared to the existing method.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.618-621
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• 2009

데스크탑 및 이동형 컴퓨터의 저장 장치를 지원하는 플래시 메모리는 비휘발성, 낮은 전력소모, 빠른 데이터 접근 속도 등의 장점이 있다. 하지만, 일반 RAM 메모리에 비하여 상대적으로 느린 연산 특성을 고려하여 기존의 전통적인 인덱스 관리 기법을 개선할 필요가 있다. 이를 위하여, 본 논문은 CHC-Tree 라고 하는 압축된 핫-콜드 클러스터링에 기반하는 새로운 인덱스 관리 기법을 제안한다. CHC-Tree는 인덱스 노드를 핫-콜드 세그먼트로 분류하며, 인덱스 노드의 키와 포인터를 압축한다. 또한, 실험 결과를 통하여 기존의 B-Tree 기반의 인덱스 관리 기법보다 인덱스 검색 및 인덱스 수정 연산에서 더 우수함을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.531-534
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• 2010

웹 2.0의 적극적인 도입에 따라 소셜 네트워크 기반 커뮤니티 사이트에서는 관련된 콘텐츠를 적절하게 추천하는 것은 중요한 문제로 부각되고 있으며 이로 인해 사용자들의 동향 및 이슈 추출 기법이 중요하게 작용하고 있다. 이러기 위해서 지금까지의 연구에서는 콘텐츠에 포함된 키워드 매칭 방법을 이용하고 있으나 사용자들 간의 연결 관계와 키워드의 중요도를 고려하지 못하고 있다. 본 논문에서는 FOAF 기반의 소셜 네트워크와 del.icio.us에서 제공하는 소셜 북마크 데이터를 기초로 소셜네트워크 분석을 보이며 이를 통한 사용자들 사이에서 중요하게 부각되는 핫 이슈를 추출하는 방법을 제안한다. 본 논문에서 제안하는 핫 이슈 추출 방법을 활용하면 사용자들의 관심 분야 동향파악을 효율적으로 수행할 수 있으며 이를 통해 맞춤형 마케팅 및 콘텐츠 추천이 가능해 진다.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.