Relevant bibliographies by topics / Data Storage Optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Storage Optimization'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Data Storage Optimization"

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Koka, Naveen. "Data Storage Codification for Enhanced Optimization." International Journal of Science and Research (IJSR) 10, no. 6 (2021): 1813–16. http://dx.doi.org/10.21275/sr24608151607.

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Ekta, Mrinal. "Secured Cloud Data Sharing: Privacy-Preserving Storage Optimization with Data Confidentiality." International Journal of Research Publication and Reviews 4, no. 8 (2023): 2957–66. http://dx.doi.org/10.55248/gengpi.4.823.51935.

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Yolchuyev, Agil, and Janos Levendovszky. "Data Chunks Placement Optimization for Hybrid Storage Systems." Future Internet 13, no. 7 (2021): 181. http://dx.doi.org/10.3390/fi13070181.

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“Hybrid Cloud Storage” (HCS) is a widely adopted framework that combines the functionality of public and private cloud storage models to provide storage services. This kind of storage is especially ideal for organizations that seek to reduce the cost of their storage infrastructure with the use of “Public Cloud Storage” as a backend to on-premises primary storage. Despite the higher performance, the hybrid cloud has latency issues, related to the distance and bandwidth of the public storage, which may cause a significant drop in the performance of the storage systems during data transfer. This
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Naveen, Koka. "Data Storage Codification for Enhanced Optimization." Journal of Scientific and Engineering Research 8, no. 6 (2021): 142–46. https://doi.org/10.5281/zenodo.12805341.

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In today's digital landscape, the integration of mobile applications with server systems presents both opportunities and challenges in data management. This symbiotic relationship necessitates seamless synchronization between mobile devices and servers, particularly in scenarios where internet connectivity fluctuates. Strategies such as Data Storage Codification for Enhanced Optimization offer a solution, enabling robust data storage and retrieval processes that remain functional even in offline settings. By leveraging mechanisms such as cloud document servers and unique identifiers, organizat
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Ur Rahaman, Shafeeq. "Data - Driven Warehouse Automation and Route Optimization in Cold Storage Logistics." International Journal of Science and Research (IJSR) 9, no. 3 (2020): 1718–25. http://dx.doi.org/10.21275/sr200310112008.

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Talakh, M. V., V. V. Dvorzhak, and Yu O. Ushenko. "Denormalization techniques for IOT data warehouses: balancing query performance and data redundancy." Optoelectronic Information-Power Technologies 49, no. 1 (2025): 72–81. https://doi.org/10.31649/1681-7893-2025-49-1-72-81.

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This article explores the impact of denormalization techniques on query performance in IoT data warehouses while maintaining acceptable data redundancy. It analyzes normalized and denormalized approaches in a smart home IoT environment using Azure Synapse. Empirical testing (10,000–5 million records) shows that strategic denormalization combined with columnar storage optimization improves performance by up to 94%. Evaluating four key optimization techniques (Join Reduction, Columnar Storage, Query Complexity Optimization, Temporal Scaling Optimization), we find that denormalization initially i
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Feser, John, Sam Madden, Nan Tang, and Armando Solar-Lezama. "Deductive optimization of relational data storage." Proceedings of the ACM on Programming Languages 4, OOPSLA (2020): 1–30. http://dx.doi.org/10.1145/3428238.

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Butil, John Carlo M., Ma Lei Frances Magsisi, John Hart Pua, Prince Kevin Se, and Ria Sagum. "The Application of Genetic Algorithm in Motion Detection for Data Storage Optimization." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 199–202. http://dx.doi.org/10.7763/ijcce.2014.v3.319.

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Alekseev, A., A. Kiryanov, A. Klimentov, et al. "Data Handling Optimization in Russian Data Lake Prototype." Journal of Physics: Conference Series 2438, no. 1 (2023): 012021. http://dx.doi.org/10.1088/1742-6596/2438/1/012021.

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Abstract CERN experiments are preparing for the HL-LHC era, which will bring an unprecedented volume of scientific data. These data will need to be stored and processed by thousands of physicists, but expected resource growth is nowhere near the extrapolated requirements of existing models, in terms of both storage volume and compute power. Opportunistic CPU resources such as HPCs and university clusters can provide extra CPU cycles, but there is no opportunistic storage. In this article, we will present the main architectural ideas, deployment details, and test results, with emphasis on our r
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Yuan, Zhu, Xueqiang Lv, Yunchao Gong, Boshan Liu, Haixiang Yang, and Xindong You. "Text Semantics-Driven Data Classification Storage Optimization." Applied Sciences 14, no. 3 (2024): 1159. http://dx.doi.org/10.3390/app14031159.

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Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices to orchestrate a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. The traditional strategy of solely relying on access frequency for data classification is no longer suitable for today’s complex storage environment. Diverging from conventional methods, we explore from the perspective of text semantics to address this issue and propose an effective data classification storage method using text semantic similarity to ext
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Dissertations / Theses on the topic "Data Storage Optimization"

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Edwards, Laura E. (Laura Elaine). "Optimization of data storage for the adaptive transformer monitoring system." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/12770.

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Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.<br>Includes bibliographical references (leaves 150-151).<br>by Laura E. Edwards.<br>B.S.
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DiMatteo, Joseph Howard 1959. "Focus servo performance optimization for an optical disk data storage device." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276866.

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This thesis concerns a study of the application, and performance optimization, of standard lead-lag compensation techniques to improve the closed loop performance of a focus servo system for an optical data storage device. Only with proper application of these compensation techniques will it be possible to meet the sub-micrometer focus error tolerances while maintaining the stability of the closed loop system. The performance indices used in this optimization study are the Integral of the Squared Error (ISE), the Integral of the Absolute Error (IAE), and the Integral of the Time multiplied by
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Wadhwa, Bharti. "Scalable Data Management for Object-based Storage Systems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99791.

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Parallel I/O performance is crucial to sustain scientific applications on large-scale High-Performance Computing (HPC) systems. Large scale distributed storage systems, in particular the object-based storage systems, face severe challenges for managing the data efficiently. Inefficient data management leads to poor I/O and storage performance in HPC applications and scientific workflows. Some of the main challenges for efficient data management arise from poor resource allocation, load imbalance in object storage targets, and inflexible data sharing between applications
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Munir, Rana Faisal. "Storage format selection and optimization for materialized intermediate results in data-intensive flows." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/668476.

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Modern organizations produce and collect large volumes of data, that need to be processed repeatedly and quickly for gaining business insights. For such processing, typically, Data-intensive Flows (DIFs) are deployed on distributed processing frameworks. The DIFs of different users have many computation overlaps (i.e., parts of the processing are duplicated), thus wasting computational resources and increasing the overall cost. The output of these computation overlaps (known as intermediate results) can be materialized for reuse, which helps in reducing the cost and saves computational resourc
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Massaro, Evan K. "Modeling exascale data generation and storage for the large hadron collider computing network." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/126990.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 85-86).<br>The Large Hadron Collider (LHC) is the world's largest and highest energy particle accelerator. With the particle collisions produced at the LHC and measured with the Compact Muon Solenoid (CMS) detector, the CMS experimental group performs precision measurements and general searches for new physics. Year-round CMS operations produce 100 Petabytes of physics data per year, which is
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Tandon, Ashish. "Analysis and optimization of data storage using enhanced object models in the .NET framework." Thesis, Edinburgh Napier University, 2007. http://researchrepository.napier.ac.uk/Output/4047.

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The purpose of thesis is to benchmark the database to examine and analyze the performance using the Microsoft COM+ the most commonly used component framework heavily used for developing component based applications. The prototype application based on Microsoft Visual C#.NET language used to benchmark the database performance on Microsoft .NET Framework environment 2.0 and 3.0 using the different sizes of data range from low (100 Rows) to high volume (10000 Rows) of data with five or ten number of users connections. There are different type of application used like COM+, Non-COM+ and .NET based
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Jafarizadeh, Saber. "Distributed coding and algorithm optimization for large-scale networked systems." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/13238.

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In this thesis design and optimization of several distributed algorithms in large-scale networked systems is studied. The studied algorithms operate on networks of autonomous agents in general including the sensor networks and the ad hoc networks. The main focus here is on distributed algorithms operating on large-scale networks. This is due to their robustness to node failure and ability to extend according to the size and topology of the system. Regarding the optimization of the studied algorithms, it is aimed to increase their convergence rate to their equilibrium state considering the cons
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Komaragiri, Vivek Chakravarthy. "Application of decision diagrams for information storage and retrieval." Master's thesis, Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-04082002-144345.

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Wiren, Jakob. "Data Storage Cost Optimization Based on Electricity Price Forecasting with Machine Learning in a Multi-Geographical Cloud Environment." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-152250.

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As increased demand of cloud computing leads to increased electricity costs for cloud providers, there is an incentive to investigate in new methods to lower electricity costs in data centers. Electricity price markets suffer from sudden price spikes as well as irregularities between different geographical electricity markets. This thesis investigates in whether it is possible to leverage these volatilities and irregularities between different electricity price markets, to offload or move storage in order to reduce electricity price costs for data storage. By forecasting four different electri
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Munsill, Caleb Mosby. "CubeSat Data Transmission and Storage Throughput Optimization Through the Use of a Zynq SoC Based CubeSat Science Instrument Interface Electronics Board." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1736.

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The CubeSat standard sprang from the desire to create a satellite standard that would open the doors for universities and other lower budget research institutions by making it more feasible to get their work into space. Since then, many other institutions and industries have been adopting variations on the standard for their own use. As more people are seeking out to use the CubeSat standard as their main bus, the standards and practices of the community have grown and expanded and with this growth, new challenges have been created. One such challenge is the bandwidth limitation in the RF-down
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Books on the topic "Data Storage Optimization"

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Kim, Daehee, Sejun Song, and Baek-Young Choi. Data Deduplication for Data Optimization for Storage and Network Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42280-0.

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Stützle, Thomas. Learning and Intelligent Optimization: Third International Conference, LION 3, Trento, Italy, January 14-18, 2009. Selected Papers. Springer-Verlag Berlin Heidelberg, 2009.

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T, Feagin, Overland D, University of Houston--Clear Lake. Research Institute for Computing and Information Systems., and Lyndon B. Johnson Space Center., eds. Communications and tracking expert systems study. Research Institute for Computing and Information Systems, University of Houston--Clear Lake, 1987.

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Choi, Baek-Young, Sejun Song, and Daehee Kim. Data Deduplication for Data Optimization for Storage and Network Systems. Springer London, Limited, 2016.

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Choi, Baek-Young, Sejun Song, and Daehee Kim. Data Deduplication for Data Optimization for Storage and Network Systems. Springer, 2018.

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Choi, Baek-Young, Sejun Song, and Daehee Kim. Data Deduplication for Data Optimization for Storage and Network Systems. Springer International Publishing AG, 2016.

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Data Storage for Social Networks: A Socially Aware Approach. Springer London, Limited, 2012.

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Redbooks, IBM. IBM Totalstorage: San Product, Design, And Optimization Guide. Ibm, 2005.

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IBM Totalstorage: San Product, Design, And Optimization Guide. Vervante, 2005.

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Wu, Victor. Storage Optimization with Unity All-Flash Array: Learn to Protect, Replicate or Migrate Your Data Across Dell EMC Unity Storage and UnityVSA. BPB Publications, 2019.

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Book chapters on the topic "Data Storage Optimization"

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Burr, G. W., and M. P. Bernal Artajona. "System Optimization for Holographic Data Storage Systems." In Holographic Data Storage. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-47864-5_21.

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Tran, Duc A. "Social Locality in Data Storage." In SpringerBriefs in Optimization. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4636-1_2.

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Deivamani, M., Rashmi Vikraman, S. Abirami, and R. Baskaran. "Data Storage Optimization in Cloud Environment." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2135-7_45.

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Fan, Weibei, Zhijie Han, and Zhengwei Guo. "Wireless Sensor Network Data Storage Optimization Strategy." In Advanced Technologies in Ad Hoc and Sensor Networks. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54174-2_31.

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Wang, Di, Ji-wu Shu, and Meiming Shen. "Data I/O Optimization in Storage Systems." In Grid and Cooperative Computing - GCC 2004 Workshops. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30207-0_37.

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Abdulrahman, Khalid, Abdulghani Ali Ahmed, and Muamer N. Mohammed. "Investigation Model for Locating Data Remnants on Cloud Storage." In Intelligent Computing & Optimization. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00979-3_25.

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Catthoor, Francky, Koen Danckaert, Chidamber Kulkarni, et al. "Cache Optimization Methodologies and Automation." In Data Access and Storage Management for Embedded Programmable Processors. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-4903-8_7.

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Meikshan, Vladimir I., Natalia B. Teslya, Evgeniya E. Istratova, Irina D. Koldunova, and Albina V. Ikonnikova. "Cost Optimization for Data Storage Using Cloud Technologies." In Frontier Information Technology and Systems Research in Cooperative Economics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57831-2_53.

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Kim, Daehee, Sejun Song, and Baek-Young Choi. "SoftDance: Software-Defined Deduplication as a Network and Storage Service." In Data Deduplication for Data Optimization for Storage and Network Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42280-0_5.

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Kim, Daehee, Sejun Song, and Baek-Young Choi. "Introduction." In Data Deduplication for Data Optimization for Storage and Network Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42280-0_1.

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Conference papers on the topic "Data Storage Optimization"

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Su, Linglei, Hongyang Xu, Cuicui Zhao, and Xiao Cong. "Microgrid Battery Energy Storage Capacity Configuration Optimization Model." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10868502.

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Zhu, Shatong, and Yifan Zhang. "Coordinated Optimization and Configuration Optimization of Wind, Photovoltaics and Energy Storage based on Particle Swarm Optimization Algorithm." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762377.

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Liu, Huizhong, Yujing Wang, Hongcai Chen, Weiping Zhang, and Bing Xie. "Design of highly adaptive data storage system based on optimization algorithm." In Second International Conference on Big Data, Computational Intelligence and Applications (BDCIA 2024), edited by Sos S. Agaian. SPIE, 2025. https://doi.org/10.1117/12.3059267.

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Yang, Yi, Hao Jiao, Mingshen Wang, Shuyi Zhuang, Xianglu Pang, and Yi Pan. "Optimization Configuration of Distributed Photovoltaic and Energy Storage Systems Based on Improved FPA." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10690863.

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Futamata, Akio, Masaharu Moritsugu, Fumitaka Abe, and Koichi Ogawa. "Optimization of magneto-optic head." In Optical Data Storage, edited by Donald B. Carlin and David B. Kay. SPIE, 1990. http://dx.doi.org/10.1117/12.22005.

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Wan, Yuhong, Shiquan Tao, Zhuqing Jiang, and Dayong Wang. "Optimization of data access approach in volume holographic disk memory." In Optical Data Storage. OSA, 2007. http://dx.doi.org/10.1364/ods.2007.tue5.

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He, Ping, William A. McGahan, and John A. Woollam. "Optimization of quadrilayer structures for various magneto-optical recording materials." In Optical Data Storage, edited by James J. Burke, Thomas A. Shull, and Nobutake Imamura. SPIE, 1991. http://dx.doi.org/10.1117/12.45939.

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He, Ping, William A. McGahan, and John A. Woollam. "Optimization of Quadralayer Structures for Various Magneto-Optical Recording Materials." In Optical Data Storage. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/ods.1991.wc5.

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Computer simulation of magneto-optical responses as a function of wavelength (300 - 800 nm), thicknesses, and material were made, based on the scattering matrix formulation for the calculation of optical and magneto-optical response of multilayer systems.1 Results from the following MO materials systems are presented: bismuth substituted garnets, TbFeCo, PtMnSb, and NiMnSb. Silicon nitride and silicon carbide were the dielectrics used in the simulations, and aluminum was assumed for the reflector. The optimum structure for each of these materials systems has been found. The Kerr rotation, elli
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Wan, Yuhong, Shiquan Tao, Zhuqing Jiang, and Dayong Wang. "Optimization of data access approach in volume holographic disk memory." In Optical Data Storage 2007. SPIE, 2007. http://dx.doi.org/10.1117/12.739090.

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Mukaida, Masaru, Yuji Chaki, and Shinichi Katsuda. "MMVF Mastering for Exposure Processes & Simulation." In Optical Data Storage. Optica Publishing Group, 1998. http://dx.doi.org/10.1364/ods.1998.pdp.6.

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Multimedia video file (MMVF), for which the target of recording capacity is 5.2GB/side, requires mastering technologies for fabricating fine preformat patterns having the specifications listed in Table 1. The most important mastering technologies are the following ones for optimization of the exposure processes.
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Reports on the topic "Data Storage Optimization"

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Badia, R., J. Ejarque, S. Böhm, C. Soriano, and R. Rossi. D4.4 API and runtime (complete with documentation and basic unit testing) for IO employing fast local storage. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.9.001.

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This deliverable presents the activities performed on the ExaQUte project task 4.5 Development of interface to fast local storage. The activities have been focused in two aspects: reduction of the storage space used by applications and design and implementation of an interface that optimizes the use of fast local storage by MPI simulations involved in the project applications. In the rst case, for one of the environments involved in the project (PyCOMPSs) the default behavior is to keep all intermediate les until the end of the execution, in case these les are reused later by any additional ta
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Mays, Gary T., Randy Belles, Sacit Cetiner, et al. Application of Spatial Data Modeling Systems, Geographical Information Systems (GIS), and Transportation Routing Optimization Methods for Evaluating Integrated Deployment of Interim Spent Fuel Storage Installations and Advanced Nuclear Plants. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1050369.

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