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Relevant bibliographies by topics / Large databases

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Academic literature on the topic 'Large databases'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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Journal articles on the topic "Large databases"

1

Bruelheide, Helge, and Ute Jandt. "Demarcation of communities in large databases." Phytocoenologia 27, no. 2 (1997): 141–59. http://dx.doi.org/10.1127/phyto/27/1997/141.

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Qadah and Irani. "A Database Machine for Very Large Relational Databases." IEEE Transactions on Computers C-34, no. 11 (1985): 1015–25. http://dx.doi.org/10.1109/tc.1985.1676534.

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Jick, H., S. Jick, and L. E. Derby. "Large computer databases." BMJ 302, no. 6781 (1991): 912. http://dx.doi.org/10.1136/bmj.302.6781.912-b.

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Ganti, V., J. Gehrke, and R. Ramakrishnan. "Mining very large databases." Computer 32, no. 8 (1999): 38–45. http://dx.doi.org/10.1109/2.781633.

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Dutton, Richard P. "Large databases in anaesthesiology." Current Opinion in Anaesthesiology 28, no. 6 (2015): 697–702. http://dx.doi.org/10.1097/aco.0000000000000243.

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Sitzmann, Markus, Wolf-Dietrich Ihlenfeldt, and Marc C. Nicklaus. "Tautomerism in large databases." Journal of Computer-Aided Molecular Design 24, no. 6-7 (2010): 521–51. http://dx.doi.org/10.1007/s10822-010-9346-4.

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Salter, A. Hugh. "Large-scale databases in toxicogenomics." Pharmacogenomics 6, no. 7 (2005): 749–54. http://dx.doi.org/10.2217/14622416.6.7.749.

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Zhang, Shichao, and Chengqi Zhang. "Discovering causality in large databases." Applied Artificial Intelligence 16, no. 5 (2002): 333–58. http://dx.doi.org/10.1080/08839510290030264.

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Hinneburg, Alexander, and Daniel A. Keim. "Clustering methods for large databases." ACM SIGMOD Record 28, no. 2 (1999): 509. http://dx.doi.org/10.1145/304181.304232.

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Furano, Fabrizio. "Large databases on the GRID." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 623, no. 2 (2010): 855–58. http://dx.doi.org/10.1016/j.nima.2010.03.094.

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Dissertations / Theses on the topic "Large databases"

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U, Leong-Hou. "Matching problems in large databases." Click to view the E-thesis via HKUTO, 2010. http://sunzi.lib.hku.hk/hkuto/record/B43910488.

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U, Leong-Hou, and 余亮豪. "Matching problems in large databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B43910488.

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Pinheiro, Diogo Ricardo Marques. "Large databases management of space missions." Master's thesis, Universidade de Aveiro, 2012. http://hdl.handle.net/10773/8617.

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Mestrado em Engenharia de Computadores e Telemática<br>A cosmologia é uma área em expansão, sendo realizadas cada vez mais missões espaciais com o objectivo de conhecer melhor o universo, nomeadamente descobrir a sua origem e determinar a sua expansão. Com o avanço da tecnologia, os satélites são equipados com detectores cada vez mais sofisticados, permitindo obter mapas do c eu de elevada resolução. Para facilitar a análise desses dados é necessário recorrer a algumas ferramentas, como por exemplo software que permita representar esses dados graficamente sob a forma de mapas. Devido ao grande aumento da resolução destes mapas, actualmente na ordem de 1 bilião de pixéis, torna-se necessário criar um visualizador que suporte o seu processamento e que os permita manipular com um desempenho e fluidez aceitável. O objectivo desta dissertação consiste na criação de uma aplicação para visualizar mapas espaciais Hierarchical Equal Area isoLatitude Pixelization (HEALPix), armazenados em formato Flexible Image Transport System (FITS), de elevada resolução, e que permita a sincronização de múltiplos mapas em simultâneo, usando diferentes tipos de projecções.<br>Cosmology is a growing area, which translates into an increase of space missions deployed, with the aim of know the universe better, in particular discover its origin and its expansion rate. With the advance of technology, satellites are equipped with detectors even more sophisticated, allowing gathering sky maps of higher resolution. To ease the task of analyze these data, it is necessary to use some tools, for example software that allows to represent the data graphically in the form of sky maps. Due to the increase of the sky maps resolution, actually around 1 billion pixels, it is necessary to create a viewer that supports their processing, and allows to manipulate them with acceptable performance and fluidity. The goal of this thesis is to develop a application to visualize high resolution Hierarchical Equal Area isoLatitude Pixelization (HEALPix) maps, stored in Flexible Image Transport System (FITS) format, and that allows the synchronization between multiple maps simultaneous, using diferent types of projections.

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Ottesen, Erik Bagge. "Similarity Search in Large Databases using Metric Indexing and Standard Database Access Methods." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9038.

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<p>Several methods exists for performing similarity searches quickly using metric indexing. However, most of these methods are based on main memory indexing or require specialized disk access methods. We have described and implemented a method combining standard database access methods with the LAESA Linear Approximating Eliminating Search Algorithm to perform both range and K nearest neighbour (KNN) queries using standard database access methods and relational operators. We have studied and tested various existing implementations of R-trees, and implemented the R*-tree. We also found that some of the optimizations in R*-trees was damaging to the response time at very high dimensionality. This is mostly due to the increased CPU time removing any benefit from reducing the number of disk accesses. Further we have performed comprehensive experiments using different access methods, join operators, pivot counts and range limits for both range and nearest neighbour queries. We will also implement and experiment using a multi-threaded execution environment running on several processors.</p>

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Lee, Sau Dan. "Constrained mining of patterns in large databases." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=978668944.

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Cary, Ariel. "Scaling Geospatial Searches in Large Spatial Databases." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/548.

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Modern geographical databases store a rich set of aspatial attributes in addition to geographic data. Retrieving spatial records constrained on spatial and aspatial attributes provides users the ability to perform more interesting spatial analyses via composite spatial searches; e.g., in a real estate database, "Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000". Efficient processing of such composite searches requires combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by non-spatial filter, or viceversa), which can incur large performance overheads. On the other hand, the amount of geolocation data in databases is rapidly increasing due in part to advances in geolocation technologies (e.g., GPS- enabled mobile devices) that allow to associate location data to nearly every object or event. Hence, practical spatial databases may face data ingestion challenges of large data volumes. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre- processing task) can be scaled in MapReduce – a well-adopted parallel programming model, developed by Google, for data intensive problems. Close to linear scalability was observed in index construction tasks over large spatial datasets. Subsequently, we develop novel techniques for simultaneously indexing spatial with textual and numeric data to process k-nearest neighbor searches with aspatial Boolean selection constraints. In particular, numeric ranges are compactly encoded and explicitly indexed. Experimental evaluations with real spatial databases showed query response times within acceptable ranges for interactive search systems.

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李守敦 and Sau-dan Lee. "Maintenance of association rules in large databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31215531.

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Tahir, Shahzaib. "Privacy preserving search in large encrypted databases." Thesis, City, University of London, 2018. http://openaccess.city.ac.uk/21592/.

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The Cloud is an environment designed for the provision of on-demand resource sharing and data access to remotely located clients and devices. Once data is outsourced to the Cloud, clients tend to lose control of their data thus becoming susceptible to data theft. To mitigate/ reduce the chances of data theft, Cloud service providers employ methods like encrypting data prior to outsourcing it to the Cloud. Although this increases security, it also gives rise to the challenge of searching and sifting through the large amounts of encrypted documents present in the Cloud. This thesis proposes a comprehensive framework that provides Searchable Encryption-as-a-Service (SEaaS) by enabling clients to search for keyword(s) over the encrypted data stored in the Cloud. Searchable Encryption (SE) is a methodology based on recognized cryptographic primitives to enable a client to search over the encrypted Cloud data. This research makes five major contributions to the field of Searchable Encryption: The first contribution is that the thesis proposes novel index-based SE schemes that increase the query effectiveness while being lightweight. To increase query effectiveness this thesis presents schemes that facilitate single-keyword, parallelized disjunctive-keyword (multi-keyword) and fuzzy-keyword searches. The second contribution of this research is the incorporation of probabilistic trapdoors in all the proposed schemes. Probabilistic trapdoors enable the client to hide the search pattern even when the same keyword is searched repeatedly. Hence, this quality allows the client to resist distinguishability attacks and prevents attackers from inferring the search pattern. The third contribution is the enumeration of a "Privacy-preserving" SE scheme by presenting new definitions for SE; i.e., keyword-trapdoor indistinguishability and trapdoor index indistinguishability. The existing security definitions proposed for SE did not take into account the incorporation of probabilistic trapdoors hence they were not readily applicable to our proposed schemes; hence new definitions have been studied. The fourth contribution is the validation that the proposed index-based SE schemes are efficient and can be deployed on to the real-world Cloud offering. The proposed schemes have been implemented and proof-of-concept prototypes have been deployed onto the British Telecommunication's Cloud Server (BTCS). Once deployed onto the BTCS the proof-of-concept prototypes have been tested over a large real-world speech corpus. The fifth contribution of the thesis is the study of a novel homomorphic SE scheme based on probabilistic trapdoors for the provision of higher level of security and privacy. The proposed scheme is constructed on a Partially Homomorphic Encryption Scheme that is lightweight when compared to existing Fully Homomorphic-based SE schemes. The scheme also provides non-repudiation of the transmitted trapdoor while eliminating the need for a centralized data structure, thereby facilitating scalability across Cross-Cloud platforms.

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Yuen, Jenny Ph D. Massachusetts Institute of Technology. "Labeling and modeling large databases of videos." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/71492.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 91-98).<br>As humans, we can say many things about the scenes surrounding us. For instance, we can tell what type of scene and location an image depicts, describe what objects live in it, their material properties, or their spatial arrangement. These comprise descriptions of a scene and are majorly studied areas in computer vision. This thesis, however, hypotheses that observers have an inherent prior knowledge that can be applied to the scene at hand. This prior knowledge can be translated into the cognisance of which objects move, or in the trajectories and velocities to expect. Conversely, when faced with unusual events such as car accidents, humans are very well tuned to identify them regardless of having observed the scene a priori. This is, in part, due to prior observations that we have for scenes with similar configurations to the current one. This thesis emulates the prior knowledge base of humans by creating a large and heterogeneous database and annotation tool for videos depicting real world scenes. The first application of this thesis is in the area of unusual event detection. Given a short clip, the task is to identify the moving portions of the scene that depict abnormal events. We adopt a data-driven framework powered by scene matching techniques to retrieve the videos nearest to the query clip and integrate the motion information in the nearest videos. The result is a final clip with localized annotations for unusual activity. The second application lies in the area of event prediction. Given a static image, we adapt our framework to compile a prediction of motions to expect in the image. This result is crafted by integrating the knowledge of videos depicting scenes similar to the query image. With the help of scene matching, only scenes relevant to the queries are considered, resulting in reliable predictions. Our dataset, experimentation, and proposed model introduce and explore a new facet of scene understanding in images and videos.<br>by Jenny Yuen.<br>Ph.D.

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Lee, Sau-dan. "Maintenance of association rules in large databases /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19003250.

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Books on the topic "Large databases"

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International Conference on Very Large Data Bases (16th 1990 Brisbane, Qld.). Very large data bases. Edited by McLeod Dennis, Sacks-Davis Ron, and Schek H. -J. Morgan Kaufmann, 1990.

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Endowment, VLDB, Association for Computing Machinery. Special Interest Group on Management of Data., and IEEE Computer Society, eds. Proceedings of the Fourteenth International Conference on Very Large Data Bases. Morgan Kaufmann, 1988.

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International, Conference on Very Large Data Bases (19th 1993 Dublin Ireland). 19th International Conference on Very Large Data Bases, August 24th-27th 1993, Dublin, Ireland: Proceedings. Morgan Kaufmann Publishers, 1993.

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Symposium SSD (1st 1989 Santa Barbara, Calif.). Design and implementation of large spatial databases. Springer-Verlag, 1990.

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Freitas, Alex A., and Simon H. Lavington. Mining Very Large Databases with Parallel Processing. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5521-6.

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Moyal, Ami, Vered Aharonson, Ella Tetariy, and Michal Gishri. Phonetic Search Methods for Large Speech Databases. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6489-1.

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Buchmann, Alejandro P., Oliver Günther, Terence R. Smith, and Yuan-Fang Wang, eds. Design and Implementation of Large Spatial Databases. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52208-5.

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Moyal, Ami. Phonetic Search Methods for Large Speech Databases. Springer New York, 2013.

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Freitas, Alex A. Mining very large databases with parallel processing. Springer, 2000.

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Freitas, Alex A. Mining very large databases with parallel processing. Kluwer Academic Publishers, 1998.

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Book chapters on the topic "Large databases"

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Ke, Yiping, James Cheng, and Jeffrey Xu Yu. "Querying Large Graph Databases." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12098-5_57.

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Adhikari, Animesh, Pralhad Ramachandrarao, and Witold Pedrycz. "Mining Multiple Large Databases." In Developing Multi-Database Mining Applications. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-044-1_3.

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Goodchild, Michael F. "Tiling large geographical databases." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52208-5_25.

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Pilania, Ghanshyam, Prasanna V. Balachandran, James E. Gubernatis, and Turab Lookman. "Learning with Large Databases." In Data-Based Methods for Materials Design and Discovery. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-02383-5_3.

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Johnson, Theodore, and Damianos Chatziantoniou. "Joining Very Large Data Sets." In Databases in Telecommunications. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10721056_9.

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Schlehuber, Cameron. "Structuring Databases for Data Sharing." In Computerizing Large Integrated Health Networks. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0655-2_6.

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De Capitani di Vimercati, Sabrina, Sara Foresti, Giovanni Livraga, Stefano Paraboschi, and Pierangela Samarati. "Confidentiality Protection in Large Databases." In Studies in Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61893-7_27.

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Ramón-Balmaseda, Enrique, Javier Lorenzo-Navarro, and Modesto Castrillón-Santana. "Gender Classification in Large Databases." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33275-3_9.

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Strom, Brian L. "Pharmacoepidemiology Studies Using Large Databases." In Drug Epidemiology and Post-Marketing Surveillance. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-2587-9_9.

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Keim, Daniel A., and Hans-Peter Kriegel. "Issues in Visualizing Large Databases." In Visual Database Systems 3. Springer US, 1995. http://dx.doi.org/10.1007/978-0-387-34905-3_13.

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Conference papers on the topic "Large databases"

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Adhikari, Animesh, P. R. Rao, and Jhimli Adhikari. "Mining Multiple Large Databases." In 10th International Conference on Information Technology (ICIT 2007). IEEE, 2007. http://dx.doi.org/10.1109/icit.2007.42.

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Adhikari, Animesh, P. R. Rao, and Jhimli Adhikari. "Mining Multiple Large Databases." In 10th International Conference on Information Technology (ICIT 2007). IEEE, 2007. http://dx.doi.org/10.1109/icoit.2007.4418272.

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Schaefer, Gerald. "Navigating large image databases." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6003055.

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KAHVECI, TAMER, and AMBUJ SINGH. "MAP: SEARCHING LARGE GENOME DATABASES." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776303_0029.

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Hinneburg, Alexander, and Daniel A. Keim. "Clustering methods for large databases." In the 1999 ACM SIGMOD international conference. ACM Press, 1999. http://dx.doi.org/10.1145/304182.304232.

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Berra, P. B., and Pericles A. Mitkas. "Parallel optical memories for very large databases." In Very Large Optical Memories: Materials and System Architectures. SPIE, 1993. http://dx.doi.org/10.1117/12.983227.

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Bird, C. L. "Content-driven navigation of large databases." In IEE Colloquium on Intelligent Image Databases. IEE, 1996. http://dx.doi.org/10.1049/ic:19960751.

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Schaefer, Gerald. "Interactive browsing of large image databases." In 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC). IEEE, 2016. http://dx.doi.org/10.1109/icdipc.2016.7470812.

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Pakhira, Malay K. "Clustering Large Databases in Distributed Environment." In 2009 IEEE International Advance Computing Conference (IACC 2009). IEEE, 2009. http://dx.doi.org/10.1109/iadcc.2009.4809035.

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Tanin, Egemen, František Brabec, and Hanan Samet. "Remote access to large spatial databases." In the tenth ACM international symposium. ACM Press, 2002. http://dx.doi.org/10.1145/585147.585150.

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Reports on the topic "Large databases"

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Rock, N. Some experiences with large microcomputer databases. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193885.

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Munro, J. K., R. L. Fellows, D. Carrick, M. R. Phifer, and N. Tarlton. Optimal access to large databases via networks. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/539855.

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Crain, I. K. Implementation of large spatial databases in large organizations - issues and experiences. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193892.

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Bowen, Kenneth A. Testbeds for Logic Programming and Very Large Databases. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada209632.

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Clapp, N., P. Green, and D. Bell. Automated tools for cross-referencing large databases. Final report. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/539841.

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Twitchell, Kevin E. Independent verification and validation of large software requirement specification databases. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10163875.

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Twitchell, K. Independent verification and validation of large software requirement specification databases. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7233636.

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Bogdanovich, P., and H. Samet. The ATree: A data structure to support very large scientific databases. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/638241.

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Croft, W. Bruce. Browsing, Discovery, and Search in Large Distributed Databases of Complex and Scanned Documents. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada372353.

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Husar, Rudolf B. Organization, Access, and Exploration Facilities for Large Geophysical Databases: DATSAV Surface Meteorological Data. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada225132.

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