Academic literature on the topic 'Storage and indexing'
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Journal articles on the topic "Storage and indexing"
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, no. 2 (September 10, 2019): 1043–48. http://dx.doi.org/10.17762/turcomat.v10i2.13623.
Full textIliopoulos, Costas. "Storage and indexing of massive data." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2016 (May 28, 2014): 20130213. http://dx.doi.org/10.1098/rsta.2013.0213.
Full textAdeleke, Imran A., Adegbuyi D. Gbadebo, and Abayomi O. Dawodu. "A B+-Tree-Based Indexing and Storage of Numerical Records in School Databases." Asian Journal of Research in Computer Science 16, no. 4 (December 26, 2023): 418–27. http://dx.doi.org/10.9734/ajrcos/2023/v16i4401.
Full textNiu, De Jiao, Yong Zhao Zhan, and Tao Cai. "The Multi-Level Metadata Indexing in Mass Storage System." Advanced Materials Research 532-533 (June 2012): 818–22. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.818.
Full textJiang, Chao, Jinlin Wang, and Yang Li. "An Efficient Indexing Scheme for Network Traffic Collection and Retrieval System." Electronics 10, no. 2 (January 15, 2021): 191. http://dx.doi.org/10.3390/electronics10020191.
Full textDu, M., J. Wang, C. Jing, J. Jiang, and 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 (June 5, 2019): 1295–99. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1295-2019.
Full textGeetha, K., and 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, no. 8s (August 18, 2023): 565–75. http://dx.doi.org/10.17762/ijritcc.v11i8s.7239.
Full textMoses, Timothy, Abubakar Usman Othman, Umar Yahaya Aisha, Abdulsalam Ya’u Gital, Boukari Souley, and Badmos Tajudeen Adeleke. "Big data indexing: Taxonomy, performance evaluation, challenges and research opportunities." Journal of Computer Science and Engineering (JCSE) 3, no. 2 (September 6, 2022): 71–94. http://dx.doi.org/10.36596/jcse.v3i2.548.
Full textTurner, James M. "Indexing “Ordinary” Pictures for Storage and Retrieval." Visual Resources 10, no. 3 (January 1994): 265–73. http://dx.doi.org/10.1080/01973762.1994.9658292.
Full textKundu, Anirban, Siddhartha Sett, Subhajit Kumar, Shruti Sengupta, and Srayan Chaudhury. "Search engine indexing storage optimisation using Hamming distance." International Journal of Intelligent Information and Database Systems 6, no. 2 (2012): 113. http://dx.doi.org/10.1504/ijiids.2012.045845.
Full textDissertations / Theses on the topic "Storage and indexing"
Munishwar, Vikram P. "Storage and indexing issues in sensor networks." Diss., Online access via UMI:, 2006.
Find full textSchmidt, Karsten [Verfasser]. "Self-Tuning Storage and Indexing for Native XML DBMSs / Karsten Schmidt." München : Verlag Dr. Hut, 2011. http://d-nb.info/1018981071/34.
Full textMick, Alan A. "Knowledge based text indexing and retrieval utilizing case based reasoning /." Online version of thesis, 1994. http://hdl.handle.net/1850/11715.
Full textHabtu, 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.
Full textVisma 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.
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.
Full textYapp, 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.
Full textTekli, Joe, Richard Chbeir, Agma J. M. Traina, Caetano Traina, Kokou Yetongnon, Carlos Raymundo Ibanez, Assad Marc Al, and 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.
Full textIn 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.
Revisión por pares
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.
Full textVasaitis, Vasileios. "Novel storage architectures and pointer-free search trees for database systems." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6240.
Full textPaul, Arnab Kumar. "An Application-Attuned Framework for Optimizing HPC Storage Systems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99793.
Full textDoctor 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.
Books on the topic "Storage and indexing"
Manolopoulos, Yannis. Advanced database indexing. Boston: Kluwer Academic, 2000.
Find full textLancaster, F. Wilfrid. Indexing and abstracting in theory and practice. London: Library Association, 1991.
Find full textLibrary, Washington State, ed. Agency guide to indexing websites. Olympia: Washington State Library, 1999.
Find full textSystem, Unesco Computerized Documentation. CDS/ISIS cataloguing and indexing guide. 6th ed. [Paris]: UNESCO Integrated Documentation Network, 1994.
Find full textBenois-Pineau, Jenny. Visual Indexing and Retrieval. New York, NY: Springer New York, 2012.
Find full textCisco, Susan Lynn. Indexing business records: The value proposition. Silver Spring, MD: Association for Information and Image Management International, 1998.
Find full textO'Connor, Brian Clark. Doing things with information: Beyond indexing and abstracting. Westport, Conn: Libraries Unlimited, 2008.
Find full textBertino, Elisa. Indexing Techniques for Advanced Database Systems. Boston, MA: Springer US, 1997.
Find full textChoi, 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.
Full textGreig, Peter E. Newspaper indexes & indexing: Newspaper information storage and retrieval : a checklist, 1980-1987. [S.l: s.n., 1987.
Find full textBook chapters on the topic "Storage and indexing"
Manolopoulos, Yannis, Yannis Theodoridis, and 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.
Full textPalladino, 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.
Full textFeng, 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.
Full textStrate, Jason, and 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.
Full textStrate, 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.
Full textStrate, Jason, and 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.
Full textKorotkevitch, 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.
Full textKorotkevitch, 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.
Full textPollack, Edward, and 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.
Full textGibbons, 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.
Full textConference papers on the topic "Storage and indexing"
Alsubaiee, Sattam, Michael J. Carey, and 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.
Full textWei, Hai, and Lansun Shen. "Fractal-based image storage and indexing." In Electronic Imaging, edited by Minerva M. Yeung, Boon-Lock Yeo, and Charles A. Bouman. SPIE, 1999. http://dx.doi.org/10.1117/12.373574.
Full textPanwar, Ajeet Pal Singh, Devendra Kumar, and 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.
Full textPaul, Arnab K., Brian Wang, Nathan Rutman, Cory Spitz, and 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.
Full text"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.
Full textSima, Jin, Netanel Raviv, and 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.
Full textBarreto, João, and 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.
Full textShraer, 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.
Full textWeintraub, Grisha, Ehud Gudes, and 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.
Full textSmith, Mark, and 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.
Full textReports on the topic "Storage and indexing"
Bethel, E. Wes, Luke Gosink, John Shalf, Kurt Stockinger, and Kesheng Wu. HDF5-FastQuery: An API for Simplifying Access to Data Storage,Retrieval, Indexing and Querying. Office of Scientific and Technical Information (OSTI), June 2006. http://dx.doi.org/10.2172/888964.
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