• Title/Summary/Keyword: Cloud collection

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A Study on the Cloud Collection (클라우드 컬렉션에 관한 연구)

  • Cho, Jane
    • Journal of Korean Library and Information Science Society
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    • v.43 no.1
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    • pp.201-219
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    • 2012
  • Cloud collection, which is derived from cloud computing, is a framework that creates new value through library cooperative platform to preserve and manage libraries' duplicated collection. Due to E-book market vitalization and mass digitalization of retrospective print material, new library collection management paradigm has been expected that licensed based E-book service and management of shared print repository. This study discuss cloud collection concept which digital repository and Shared print repository are in complementary relations. And suggest cloud collection model can be applicable to Korean university libraries.

The Automatic Collection and Analysis System of Cloud Artifact (클라우드 아티팩트 자동 수집 및 분석 시스템)

  • Kim, Mingyu;Jeong, Doowon;Lee, Sangjin
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.25 no.6
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    • pp.1377-1383
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    • 2015
  • As the cloud services users' increase, there are important files created by individual in cloud storage. Thus, investigation of cloud artifact should be conducted. There are two methods of analyzing cloud service, one is that investigates cloud server provider (CSP), and another is that investigates client. In this paper, we presents an automated framework to detect the altered artifact and developes a tool that detects the cloud artifact. We also developed Cloud Artifact Tool that can investigate client computer. Cloud Artifact Tool provides feature of collection and analysis for the services such as Google Drive, Dropbox, Evernote, NDrive, DaumCloud, Ucloud, LG Cloud, T Cloud and iCloud.

Global Patterns of Pigment Concentration, Cloud Cover, and Sun Glint: Application to the OSMI Data Collection Planning

  • Kim, Yong-Seung;Kang, Chi-Ho;Lim, Hyo-Suk
    • Proceedings of the KSRS Conference
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    • 1998.09a
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    • pp.387-392
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    • 1998
  • To establish a monthly data collection planning for the Ocean Scanning Multispectral Imager (OSMI), we have examined the global patterns of three impacting factors: pigment concentration, cloud cover, and sun glint. Other than satellite mission constraints (e.g., duty cycle), these three factors are considered critical for the OSMI data collection. The Nimbus-7 Coastal Zone Color Scanner (CZCS) monthly mean products and the International Satellite Cloud Climatology Project (ISCCP) monthly mean products (C2) were used for the analysis of pigment concentration and cloud cover distributions, respectively. And the monthly simulated patterns of sun glint were produced by performing the OSMI orbit prediction and the calculation of sun glint radiances at the top-of-atmosphere (TOA). Using monthly statistics (mean and/or standard deviation) of each factor in the above for a given 10$^{\circ}$ latitude by 10$^{\circ}$ longitude grid, we generated the priority map for each month. The priority maps of three factors for each month were subsequently superimposed to visualize the impact of three factors in all. The initial results illustrated that a large part of oceans in the summer hemisphere was classified into the low priority regions because of seasonal changes of clouds and sun illumination. Sensitivity tests were performed to see how cloud cover and sun glint affect the priority determined by pigment concentration distributions, and consequently to minimize their seasonal effects upon the data collection planning.

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A Sensing Data Collection Strategy in Software-Defined Mobile-Edge Vehicular Networks (SDMEVN) (소프트웨어 정의 모바일 에지 차량 네트워크(SDMEVN)의 센싱 데이터 수집 전략)

  • Nkenyereye, Lionel;Jang, Jong-Wook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2018.10a
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    • pp.62-65
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    • 2018
  • This paper comes out with the study on sensing data collection strategy in a Software-Defined Mobile Edge vehicular networking. The two cooperative data dissemination are Direct Vehicular cloud mode and edge cell trajectory prediction decision mode. In direct vehicular cloud, the vehicle observe its neighboring vehicles and sets up vehicular cloud for cooperative sensing data collection, the data collection output can be transmitted from vehicles participating in the cooperative sensing data collection computation to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation out-put and send to the closest RSU. The SDMEVN (Software Defined Mobile Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution.

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Cloud Services for the forensic aspects of the investigative methods (클라우드 서비스에 대한 포렌식 측면의 수사 방법)

  • Park, Gi-Hong;No, Si-Young
    • Journal of Korea Society of Industrial Information Systems
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    • v.17 no.1
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    • pp.39-46
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    • 2012
  • In this paper, for the cloud system by explaining how the forensic aspects of the investigation. Smartphone Growth Entering a variety of applications were developed which cloud systems of personal information and information assets sharing applications as during incidents on the case evidence collection, an important factor, whereas such systematic investigative methods, born in the course of my investigation of the can be confusing. This paper on the forensic aspects of the cloud system by proposing a crime scene investigation procedures, investigative support, and aiding in the systematic collection of data to support evidence.

Garbage Collection Synchronization Technique for Improving Tail Latency of Cloud Databases (클라우드 데이터베이스에서의 꼬리응답시간 감소를 위한 가비지 컬렉션 동기화 기법)

  • Han, Seungwook;Hahn, Sangwook Shane;Kim, Jihong
    • Journal of KIISE
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    • v.44 no.8
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    • pp.767-773
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    • 2017
  • In a distributed system environment, such as a cloud database, the tail latency needs to be kept short to ensure uniform quality of service. In this paper, through experiments on a Cassandra database, we show that long tail latency is caused by a lack of memory space because the database cannot receive any request until free space is reclaimed by writing the buffered data to the storage device. We observed that, since the performance of the storage device determines the amount of time required for writing the buffered data, the performance degradation of Solid State Drive (SSD) due to garbage collection results in a longer tail latency. We propose a garbage collection synchronization technique, called SyncGC, that simultaneously performs garbage collection in the java virtual machine and in the garbage collection in SSD concurrently, thus hiding garbage collection overheads in the SSD. Our evaluations on real SSDs show that SyncGC reduces the tail latency of $99.9^{th}$ and, $99.9^{th}-percentile$ by 31% and 36%, respectively.

Digital Forensics Framework for Cloud Computing (클라우드 환경을 고려한 디지털 포렌식 프레임워크)

  • Lee, Chang-Hoon
    • Journal of Advanced Navigation Technology
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    • v.17 no.1
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    • pp.63-68
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    • 2013
  • Recently, companies seek a way to overcome their financial crisis by reducing costs in the field of IT. In such a circumstance, cloud computing is rapidly emerging as an optimal solution to the crisis. Even in a digital forensic investigation, whether users of an investigated system have used a cloud service is a very important factor in selecting additional investigated subjects. When a user has used cloud services, such as Daum Cloud and Google Docs, it is possible to connect to the could service from a remote place by acquiring the user's log-in information. In such a case, evidence data should be collected from the remote place for an efficient digital forensic investigation, and it is needed to conduct research on the collection and analysis of data from various kinds of cloud services. Thus, this study suggested a digital forensic framework considering cloud environments by investigating collection and analysis techniques for each cloud service.

Privacy-Preserving IoT Data Collection in Fog-Cloud Computing Environment

  • Lim, Jong-Hyun;Kim, Jong Wook
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.9
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    • pp.43-49
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    • 2019
  • Today, with the development of the internet of things, wearable devices related to personal health care have become widespread. Various global information and communication technology companies are developing various wearable health devices, which can collect personal health information such as heart rate, steps, and calories, using sensors built into the device. However, since individual health data includes sensitive information, the collection of irrelevant health data can lead to personal privacy issue. Therefore, there is a growing need to develop technology for collecting sensitive health data from wearable health devices, while preserving privacy. In recent years, local differential privacy (LDP), which enables sensitive data collection while preserving privacy, has attracted much attention. In this paper, we develop a technology for collecting vast amount of health data from a smartwatch device, which is one of popular wearable health devices, using local difference privacy. Experiment results with real data show that the proposed method is able to effectively collect sensitive health data from smartwatch users, while preserving privacy.

Development of Cloud based Data Collection and Analysis for Manufacturing (클라우드 기반의 생산설비 데이터 수집 및 분석 시스템 개발)

  • Young-Dong Lee
    • Journal of the Institute of Convergence Signal Processing
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    • v.23 no.4
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    • pp.216-221
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    • 2022
  • The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

SDN-Based Collection-path Steering for IoT-Cloud Service Monitoring Data over SmartX-mini Playground (SmartX-mini Playground 상의 IoT-Cloud 서비스에 대한 SDN 기반 모니터링 데이터 수집 경로 설정)

  • Yoon, Heebum;Kim, Seungryong;Kim, JongWon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.11
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    • pp.1598-1607
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    • 2016
  • Safe transmitting monitoring data is essential for supporting IoT-Cloud services efficiently. In this paper, we find ways to configure data path flexibly in SDN based for IoT-Cloud services utilizing SmartX-mini Playground. To do this, we use ONOS(Open Network Operating System) SDN Controller, ONOS NBI Applications made from us to check flexible and safe data path configuration for IoT-Cloud monitoring data transmitting in real IoT-SDN-Cloud environments.