Bibliografias temáticas / Storage and indexing

Literatura científica selecionada sobre o tema "Storage and indexing"

Autor: Grafiati
Publicado: 17 de julho de 2024

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Artigos de revistas sobre o assunto "Storage and indexing"

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Vimal, Vrince. "An Efficient and Secure Query Processing and Indexing model for Secure Dynamic Cloud Storage". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, n.º 2 (10 de setembro de 2019): 1043–48. http://dx.doi.org/10.17762/turcomat.v10i2.13623.

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To ensure the security and privacy of stored data, as well as the efficacy and efficiency of cloud storage, it is necessary to overcome significant challenges, such as efficient and secure query processing and indexing in dynamic cloud storage. There are a number of limitations with the present methodologies and tactics for query processing and indexing in cloud storage, including high processing overhead, scalability problems, and security concerns. In this paper, we provide a method for efficiently and securely executing queries and indexing data in dynamic cloud storage. The suggested system incorporates scalable indexing techniques, secure query processing, and dynamic data management to overcome these issues. The proposed system has several potential uses in many different areas, including healthcare, finance, e-commerce, government, and research. As new problems arise with cloud storage services, the proposed approach will need to be adjusted and enhanced via ongoing research and development. The proposed method has the potential to enhance data administration and analysis in dynamically managed cloud storage service environments while also protecting data privacy and security.
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Iliopoulos, Costas. "Storage and indexing of massive data". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, n.º 2016 (28 de maio de 2014): 20130213. http://dx.doi.org/10.1098/rsta.2013.0213.

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Adeleke, Imran A., Adegbuyi D. Gbadebo e Abayomi O. Dawodu. "A B+-Tree-Based Indexing and Storage of Numerical Records in School Databases". Asian Journal of Research in Computer Science 16, n.º 4 (26 de dezembro de 2023): 418–27. http://dx.doi.org/10.9734/ajrcos/2023/v16i4401.

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The need for effective indexing and retrieval of data is paramount in any contemporary organization. However, the use of tree data structure had been effective in this regard as evident in literature. This article gives an overview of B+-tree data structure, its indexing technique and application in indexing and retrieving students’ academic records in the school system in order to make such records flexible. The study demonstrates the indexing and arrangement patterns of some numerical data. In essence, it discusses how to adopt the use of B+-tree data structure to manage some numerical data in order to enhance indexing, retrieval and modifications of such record. It concludes that good record management results in more convenient indexing and retrieval of students’ academic records within the school system.
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Niu, De Jiao, Yong Zhao Zhan e Tao Cai. "The Multi-Level Metadata Indexing in Mass Storage System". Advanced Materials Research 532-533 (junho de 2012): 818–22. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.818.

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Metadata query plays an important role in mass storage system. Efficient indexing algorithm can reduce the time and space which greatly determine the efficiency of mass storage system. Typically, temporal and spatial consuming is immense and volatile in the existing metadata management algorithms. In this paper, a novel metadata indexing algorithm is presented. Metadata query algorithm is based on two-level indexing strategy. The metadata is classified into two categories, that are active metadata and non-active metadata. The Bloom Filter is used to generate binary string for active metadata, and the B-tree is used to establish index of each active partition. While, the suitable hash function is selected for each non-active metadata partition. The results show that the multi-level metadata indexing algorithm can reduce the temporal and spatial costs of metadata query.
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Jiang, Chao, Jinlin Wang e Yang Li. "An Efficient Indexing Scheme for Network Traffic Collection and Retrieval System". Electronics 10, n.º 2 (15 de janeiro de 2021): 191. http://dx.doi.org/10.3390/electronics10020191.

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Historical network traffic retrieval, both at the packet and flow level, has been applied in many fields of network security, such as network traffic analysis and network forensics. To retrieve specific packets from a vast number of packet traces, it is an effective solution to build indexes for the query attributes. However, it brings challenges of storage consumption and construction time overhead for packet indexing. To address these challenges, we propose an efficient indexing scheme called IndexWM based on the wavelet matrix data structure for packet indexing. Moreover, we design a packet storage format based on the PcapNG format for our network traffic collection and retrieval system, which can speed up the extraction of index data from packet traces. Offline experiments on randomly generated network traffic and actual network traffic are performed to evaluate the performance of the proposed indexing scheme. We choose an open-source and widely used bitmap indexing scheme, FastBit, for comparison. Apart from the native bitmap compression method Word-Aligned Hybrid (WAH), we implement an efficient bitmap compression method Scope-Extended COMPAX (SECOMPAX) in FastBit for performance evaluation. The comparison results show that our scheme outperforms the selected bitmap indexing schemes in terms of time consumption, storage consumption and retrieval efficiency.
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Du, M., J. Wang, C. Jing, J. Jiang e Q. Chen. "HIERARCHICAL DATA MODEL FOR STORAGE AND INDEXING OF MASSIVE STREET VIEW". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (5 de junho de 2019): 1295–99. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1295-2019.

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<p><strong>Abstract.</strong> Maintaining an up-to-date inventory of urban infrastructure such as fire hydrant is critical to urban management. Street view database such as Google Street View and Baidu Street View contain street-level images, their potential for urban management has not been fully explored. For the massive image, data model for storage and indexing is an important research issue. Considering multiple cameras and GPS device in the image capturing platform, a hierarchical data model named 3D-Grid is proposed. Massive street view images were stored according to grid ID, GPS time and camera ID. An efficient time indexing algorithm is brought forth to replace the spatial indexing. Real test experiments are conducted in a project, and the validation and feasibility of 3D-Grid including time indexing algorithm were validated.</p>
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Geetha, K., e A. Vijaya. "Cross-Layer Fragment Indexing based File Deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for Optimized Cloud Storage". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 8s (18 de agosto de 2023): 565–75. http://dx.doi.org/10.17762/ijritcc.v11i8s.7239.

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Cloud computing and storage processing is a big service for maintaining a large number of data in a centralized server to store and retrieve data depending on the use to pay as a service model. Due to increasing storage depending on duplicate copy presence during different sceneries, the increased size leads to increased cost. To resolve this problem, we propose a Cross-Layer Fragment Indexing (CLFI) based file deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for optimized cloud storage. Initially, the file storage indexing easy carried out with Lexical Syntactic Parser (LSP) to split the files into blocks. Then comparativesector was created based on Chunk staking. Based on the file frequency weight, the relative Indexing was verified through Cross-Layer Fragment Indexing (CLFI). Then the fragmented index gets grouped by maximum relative threshold margin usingIntra Subset Near-Duplicate Clusters (ISNDC). The hashing is applied to get comparative index points based on hyper correlation comparer using Hyper Spectral Hash Duplicate Filter (HSHDF). This filter the near duplicate contentdepending on file content difference to identify the duplicates. This proposed system produces high performance compared to the other system. This optimizes cloudstorage and has a higher precision rate than other methods.
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Moses, Timothy, Abubakar Usman Othman, Umar Yahaya Aisha, Abdulsalam Ya’u Gital, Boukari Souley e Badmos Tajudeen Adeleke. "Big data indexing: Taxonomy, performance evaluation, challenges and research opportunities". Journal of Computer Science and Engineering (JCSE) 3, n.º 2 (6 de setembro de 2022): 71–94. http://dx.doi.org/10.36596/jcse.v3i2.548.

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In order to efficiently retrieve information from highly huge and complicated datasets with dispersed storage in cloud computing, indexing methods are continually used on big data. Big data has grown quickly due to the accessibility of internet connection, mobile devices like smartphones and tablets, body-sensor devices, and cloud applications. Big data indexing has a variety of problems as a result of the expansion of big data, which is seen in the healthcare industry, manufacturing, sciences, commerce, social networks, and agriculture. Due to their high storage and processing requirements, current indexing approaches fall short of meeting the needs of large data in cloud computing. To fulfil the indexing requirements for large data, an effective index strategy is necessary. This paper presents the state-of-the-art indexing techniques for big data currently being proposed, identifies the problems these techniques and big data are currently facing, and outlines some future directions for research on big data indexing in cloud computing. It also compares the performance taxonomy of these techniques based on mean average precision and precision-recall rate.
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Turner, James M. "Indexing “Ordinary” Pictures for Storage and Retrieval". Visual Resources 10, n.º 3 (janeiro de 1994): 265–73. http://dx.doi.org/10.1080/01973762.1994.9658292.

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Kundu, Anirban, Siddhartha Sett, Subhajit Kumar, Shruti Sengupta e Srayan Chaudhury. "Search engine indexing storage optimisation using Hamming distance". International Journal of Intelligent Information and Database Systems 6, n.º 2 (2012): 113. http://dx.doi.org/10.1504/ijiids.2012.045845.

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Teses / dissertações sobre o assunto "Storage and indexing"

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Munishwar, Vikram P. "Storage and indexing issues in sensor networks". Diss., Online access via UMI:, 2006.

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Schmidt, Karsten [Verfasser]. "Self-Tuning Storage and Indexing for Native XML DBMSs / Karsten Schmidt". München : Verlag Dr. Hut, 2011. http://d-nb.info/1018981071/34.

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Mick, Alan A. "Knowledge based text indexing and retrieval utilizing case based reasoning /". Online version of thesis, 1994. http://hdl.handle.net/1850/11715.

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Habtu, Simon. "Indexing file metadata using a distributed search engine for searching files on a public cloud storage". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232064.

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Visma Labs AB or Visma wanted to conduct experiments to see if file metadata could be indexed for searching files on a public cloud storage. Given that storing files in a public cloud storage is cheaper than the current storage solution, the implementation could save Visma money otherwise spent on expensive storage costs. The thesis is therefore to find and evaluate an approach chosen for indexing file metadata and searching files on a public cloud storage with the chosen distributed search engine Elasticsearch. The architecture of the proposed solution is similar to a file service and was implemented using several containerized services for it to function. The results show that the file service solution is indeed feasible but would need further tuning and more resources to function according to the demands of Visma.
Visma Labs AB eller Visma ville genomföra experiment för att se om filmetadata skulle kunna indexeras för att söka efter filer på ett publikt moln. Med tanke på att lagring av filer på ett publikt moln är billigare än den nuvarande lagringslösningen, kan implementeringen spara Visma pengar som spenderas på dyra lagringskostnader. Denna studie är därför till för att hitta och utvärdera ett tillvägagångssätt valt för att indexera filmetadata och söka filer på ett offentligt molnlagring med den utvalda distribuerade sökmotorn Elasticsearch. Arkitekturen för den föreslagna lösningen har likenelser av en filtjänst och implementerades med flera containeriserade tjänster för att den ska fungera. Resultaten visar att filservicelösningen verkligen är möjlig men skulle behöva ytterligare modifikationer och fler resurser att fungera enligt Vismas krav.
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Teng, Shyh Wei 1973. "Image indexing and retrieval based on vector quantization". Monash University, Gippsland School of Computing and Information Technology, 2003. http://arrow.monash.edu.au/hdl/1959.1/5764.

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Yapp, Lawrence. "Content-based indexing of MPEG video through the analysis of the accompanying audio /". Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/5835.

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Tekli, Joe, Richard Chbeir, Agma J. M. Traina, Caetano Traina, Kokou Yetongnon, Carlos Raymundo Ibanez, Assad Marc Al e Christian Kallas. "Full-fledged semantic indexing and querying model designed for seamless integration in legacy RDBMS". Elsevier B.V, 2018. http://hdl.handle.net/10757/624626.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
In the past decade, there has been an increasing need for semantic-aware data search and indexing in textual (structured and NoSQL) databases, as full-text search systems became available to non-experts where users have no knowledge about the data being searched and often formulate query keywords which are different from those used by the authors in indexing relevant documents, thus producing noisy and sometimes irrelevant results. In this paper, we address the problem of semantic-aware querying and provide a general framework for modeling and processing semantic-based keyword queries in textual databases, i.e., considering the lexical and semantic similarities/disparities when matching user query and data index terms. To do so, we design and construct a semantic-aware inverted index structure called SemIndex, extending the standard inverted index by constructing a tightly coupled inverted index graph that combines two main resources: a semantic network and a standard inverted index on a collection of textual data. We then provide a general keyword query model with specially tailored query processing algorithms built on top of SemIndex, in order to produce semantic-aware results, allowing the user to choose the results' semantic coverage and expressiveness based on her needs. To investigate the practicality and effectiveness of SemIndex, we discuss its physical design within a standard commercial RDBMS allowing to create, store, and query its graph structure, thus enabling the system to easily scale up and handle large volumes of data. We have conducted a battery of experiments to test the performance of SemIndex, evaluating its construction time, storage size, query processing time, and result quality, in comparison with legacy inverted index. Results highlight both the effectiveness and scalability of our approach.
This study is partly funded by the National Council for Scientific Research - Lebanon (CNRS-L), by the Lebanese American University (LAU), and the Research Support Foundation of the State of Sao Paulo ( FAPESP ). Appendix SemIndex Weighting Scheme We propose a set of weighting functions to assign weight scores to SemIndex entries, including: index nodes , index edges, data nodes , and data edges . The weighting functions are used to select and rank semantically relevant results w.r.t. the user's query (cf. SemIndex query processing in Section 5). Other weight functions could be later added to cater to the index designer's needs.
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Liu, Hain-Ching. "Automatic scene detection in MPEG digital video for random access indexing and MPEG compression optimization /". Thesis, Connect to this title online; UW restricted, 1995. http://hdl.handle.net/1773/6001.

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Vasaitis, Vasileios. "Novel storage architectures and pointer-free search trees for database systems". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6240.

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Database systems research is an old and well-established field in computer science. Many of the key concepts appeared as early as the 60s, while the core of relational databases, which have dominated the database world for a while now, was solidified during the 80s. However, the underlying hardware has not displayed such stability in the same period, which means that a lot of assumptions that were made about the hardware by early database systems are not necessarily true for modern computer architectures. In particular, over the last few decades there have been two notable consistent trends in the evolution of computer hardware. The first is that the memory hierarchy of mainstream computer systems has been getting deeper, with its different levels moving away from each other, and new levels being added in between as a result, in particular cache memories. The second is that, when it comes to data transfers between any two adjacent levels of the memory hierarchy, access latencies have not been keeping up with transfer rates. The challenge is therefore to adapt database index structures so that they become immune to these two trends. The latter is addressed by gradually increasing the size of the data transfer unit; the former, by organizing the data so that it exhibits good locality for memory transfers across multiple memory boundaries. We have developed novel structures that facilitate both of these strategies. We started our investigation with the venerable B+-tree, which is the cornerstone order-preserving index of any database system, and we have developed a novel pointer-free tree structure for its pages that optimizes its cache performance and makes it immune to the page size. We then adapted our approach to the R-tree and the GiST, making it applicable to multi-dimensional data indexes as well as generalized indexes for any abstract data type. Finally, we have investigated our structure in the context of main memory alone, and have demonstrated its superiority over the established approaches in that setting too. While our research has its roots in data structures and algorithms theory, we have conducted it with a strong experimental focus, as the complex interactions within the memory hierarchy of a modern computer system can be quite challenging to model and theorize about effectively. Our findings are therefore backed by solid experimental results that verify our hypotheses and prove the superiority of our structures over competing approaches.
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Paul, Arnab Kumar. "An Application-Attuned Framework for Optimizing HPC Storage Systems". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99793.

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High performance computing (HPC) is routinely employed in diverse domains such as life sciences, and Geology, to simulate and understand the behavior of complex phenomena. Big data driven scientific simulations are resource intensive and require both computing and I/O capabilities at scale. There is a crucial need for revisiting the HPC I/O subsystem to better optimize for and manage the increased pressure on the underlying storage systems from big data processing. Extant HPC storage systems are designed and tuned for a specific set of applications targeting a range of workload characteristics, but they lack the flexibility in adapting to the ever-changing application behaviors. The complex nature of modern HPC storage systems along with the ever-changing application behaviors present unique opportunities and engineering challenges. In this dissertation, we design and develop a framework for optimizing HPC storage systems by making them application-attuned. We select three different kinds of HPC storage systems - in-memory data analytics frameworks, parallel file systems and object storage. We first analyze the HPC application I/O behavior by studying real-world I/O traces. Next we optimize parallelism for applications running in-memory, then we design data management techniques for HPC storage systems, and finally focus on low-level I/O load balance for improving the efficiency of modern HPC storage systems.
Doctor of Philosophy
Clusters of multiple computers connected through internet are often deployed in industry and laboratories for large scale data processing or computation that cannot be handled by standalone computers. In such a cluster, resources such as CPU, memory, disks are integrated to work together. With the increase in popularity of applications that read and write a tremendous amount of data, we need a large number of disks that can interact effectively in such clusters. This forms the part of high performance computing (HPC) storage systems. Such HPC storage systems are used by a diverse set of applications coming from organizations from a vast range of domains from earth sciences, financial services, telecommunication to life sciences. Therefore, the HPC storage system should be efficient to perform well for the different read and write (I/O) requirements from all the different sets of applications. But current HPC storage systems do not cater to the varied I/O requirements. To this end, this dissertation designs and develops a framework for HPC storage systems that is application-attuned and thus provides much improved performance than other state-of-the-art HPC storage systems without such optimizations.
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Livros sobre o assunto "Storage and indexing"

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Manolopoulos, Yannis. Advanced database indexing. Boston: Kluwer Academic, 2000.

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Lancaster, F. Wilfrid. Indexing and abstracting in theory and practice. London: Library Association, 1991.

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Library, Washington State, ed. Agency guide to indexing websites. Olympia: Washington State Library, 1999.

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System, Unesco Computerized Documentation. CDS/ISIS cataloguing and indexing guide. 6a ed. [Paris]: UNESCO Integrated Documentation Network, 1994.

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Benois-Pineau, Jenny. Visual Indexing and Retrieval. New York, NY: Springer New York, 2012.

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Cisco, Susan Lynn. Indexing business records: The value proposition. Silver Spring, MD: Association for Information and Image Management International, 1998.

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O'Connor, Brian Clark. Doing things with information: Beyond indexing and abstracting. Westport, Conn: Libraries Unlimited, 2008.

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Bertino, Elisa. Indexing Techniques for Advanced Database Systems. Boston, MA: Springer US, 1997.

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Choi, Hansol. Purifying and Indexing Technology for Nucleic Acids-Based Next Generation Storage Medium. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-4274-7.

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Greig, Peter E. Newspaper indexes & indexing: Newspaper information storage and retrieval : a checklist, 1980-1987. [S.l: s.n., 1987.

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Capítulos de livros sobre o assunto "Storage and indexing"

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Manolopoulos, Yannis, Yannis Theodoridis e Vassilis J. Tsotras. "Storage Systems". In Advanced Database Indexing, 1–16. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4419-8590-3_1.

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Palladino, Santiago. "Indexing and Storage". In Ethereum for Web Developers, 181–214. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5278-9_6.

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Feng, Dan. "Indexing Schemes". In Data Deduplication for High Performance Storage System, 53–68. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0112-6_4.

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Strate, Jason, e Grant Fritchey. "Index Storage Fundamentals". In Expert Performance Indexing in SQL Server, 15–54. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1118-2_2.

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Strate, Jason. "Index Storage Fundamentals". In Expert Performance Indexing in SQL Server 2019, 29–90. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5464-6_2.

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Strate, Jason, e Ted Krueger. "Index Storage Fundamentals". In Expert Performance Indexing for SQL Server 2012, 15–49. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-3742-6_2.

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Korotkevitch, Dmitri. "Special Indexing and Storage Features". In Pro SQL Server Internals, 81–110. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1964-5_4.

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Korotkevitch, Dmitri. "Special Indexing and Storage Features". In Pro SQL Server Internals, 81–112. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-5963-3_4.

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Pollack, Edward, e Jason Strate. "Index Storage Fundamentals". In Expert Performance Indexing in Azure SQL and SQL Server 2022, 29–51. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9215-0_2.

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Gibbons, Philip B. "Data Storage and Indexing in Sensor Networks". In Encyclopedia of Database Systems, 635–38. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_112.

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Trabalhos de conferências sobre o assunto "Storage and indexing"

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Alsubaiee, Sattam, Michael J. Carey e Chen Li. "LSM-Based Storage and Indexing". In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2786006.2786007.

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Wei, Hai, e Lansun Shen. "Fractal-based image storage and indexing". In Electronic Imaging, editado por Minerva M. Yeung, Boon-Lock Yeo e Charles A. Bouman. SPIE, 1999. http://dx.doi.org/10.1117/12.373574.

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Panwar, Ajeet Pal Singh, Devendra Kumar e Jaydesh Chandra. "Hierarchal data storage using NAI indexing". In 2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall). IEEE, 2017. http://dx.doi.org/10.1109/icaccaf.2017.8344687.

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Paul, Arnab K., Brian Wang, Nathan Rutman, Cory Spitz e Ali R. Butt. "Efficient Metadata Indexing for HPC Storage Systems". In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). IEEE, 2020. http://dx.doi.org/10.1109/ccgrid49817.2020.00-77.

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"Session details: Industry 2: Storage & Indexing". In the 2019 International Conference, Chair Alexander Shraer. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3299869.3341283.

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Sima, Jin, Netanel Raviv e Jehoshua Bruck. "Robust Indexing - Optimal Codes for DNA Storage". In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174447.

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Barreto, João, e Paulo Ferreira. "Efficient file storage using content-based indexing". In the twentieth ACM symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1095810.1118597.

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Shraer, Alexander. "Session details: Industry 2: Storage & Indexing". In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341283.

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Weintraub, Grisha, Ehud Gudes e Shlomi Dolev. "Indexing cloud data lakes within the lakes". In SYSTOR '21: The 14th ACM International Systems and Storage Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3456727.3463828.

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Smith, Mark, e Alireza Khotanzad. "Efficient Indexing and Storage Algorithm for Visual Databases". In 2008 Fifth International Conference on Information Technology: New Generations (ITNG). IEEE, 2008. http://dx.doi.org/10.1109/itng.2008.162.

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Relatórios de organizações sobre o assunto "Storage and indexing"

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Bethel, E. Wes, Luke Gosink, John Shalf, Kurt Stockinger e Kesheng Wu. HDF5-FastQuery: An API for Simplifying Access to Data Storage,Retrieval, Indexing and Querying. Office of Scientific and Technical Information (OSTI), junho de 2006. http://dx.doi.org/10.2172/888964.

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