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Статті в журналах з теми "Approximate record matching"
Seleznjev, Oleg, and Bernhard Thalheim. "Random Databases with Approximate Record Matching." Methodology and Computing in Applied Probability 12, no. 1 (July 31, 2008): 63–89. http://dx.doi.org/10.1007/s11009-008-9092-4.
Повний текст джерелаVerykios, Vassilios S., Ahmed K. Elmagarmid, and Elias N. Houstis. "Automating the approximate record-matching process." Information Sciences 126, no. 1-4 (July 2000): 83–98. http://dx.doi.org/10.1016/s0020-0255(00)00013-x.
Повний текст джерелаEssex, Aleksander. "Secure Approximate String Matching for Privacy-Preserving Record Linkage." IEEE Transactions on Information Forensics and Security 14, no. 10 (October 2019): 2623–32. http://dx.doi.org/10.1109/tifs.2019.2903651.
Повний текст джерелаHanrath, Scott, and Erik Radio. "User search terms and controlled subject vocabularies in an institutional repository." Library Hi Tech 35, no. 3 (September 18, 2017): 360–67. http://dx.doi.org/10.1108/lht-11-2016-0133.
Повний текст джерелаBianchi Santiago, Josie D., Héctor Colón Jordán, and Didier Valdés. "Record Linkage of Crashes with Injuries and Medical Cost in Puerto Rico." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (July 31, 2020): 739–48. http://dx.doi.org/10.1177/0361198120935439.
Повний текст джерелаDouglas, M. M., D. Gardner, D. Hucker, and S. W. Kendrick. "Best-Link Matching of Scottish Health Data Sets." Methods of Information in Medicine 37, no. 01 (1998): 64–68. http://dx.doi.org/10.1055/s-0038-1634494.
Повний текст джерелаWang, Shan, Huiling Shan, Chi Zhang, Yuexing Wang, and Chunxiang Shi. "Bias Correction in Monthly Records of Satellite Soil Moisture Using Nonuniform CDFs." Advances in Meteorology 2018 (July 16, 2018): 1–11. http://dx.doi.org/10.1155/2018/1908570.
Повний текст джерелаGrannis, Shaun J., Huiping Xu, Joshua R. Vest, Suranga Kasthurirathne, Na Bo, Ben Moscovitch, Rita Torkzadeh, and Josh Rising. "Evaluating the effect of data standardization and validation on patient matching accuracy." Journal of the American Medical Informatics Association 26, no. 5 (March 8, 2019): 447–56. http://dx.doi.org/10.1093/jamia/ocy191.
Повний текст джерелаZhang, Yifan, Erin E. Holsinger, Lea Prince, Jonathan A. Rodden, Sonja A. Swanson, Matthew M. Miller, Garen J. Wintemute, and David M. Studdert. "Assembly of the LongSHOT cohort: public record linkage on a grand scale." Injury Prevention 26, no. 2 (October 29, 2019): 153–58. http://dx.doi.org/10.1136/injuryprev-2019-043385.
Повний текст джерелаGreer, Melody Lynn. "4294 Patient Matching Errors and Associated Safety Events." Journal of Clinical and Translational Science 4, s1 (June 2020): 42. http://dx.doi.org/10.1017/cts.2020.160.
Повний текст джерелаДисертації з теми "Approximate record matching"
Jupin, Joseph. "Temporal Graph Record Linkage and k-Safe Approximate Match." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/412419.
Повний текст джерелаPh.D.
Since the advent of electronic data processing, organizations have accrued vast amounts of data contained in multiple databases with no reliable global unique identifier. These databases were developed by different departments for different purposes at different times. Organizing and analyzing these data for human services requires linking records from all sources. RL (Record Linkage) is a process that connects records that are related to the identical or a sufficiently similar entity from multiple heterogeneous databases. RL is a data and compute intensive, mission critical process. The process must be efficient enough to process big data and effective enough to provide accurate matches. We have evaluated an RL system that is currently in use by a local health and human services department. We found that they were using the typical approach that was offered by Fellegi and Sunter with tuple-by-tuple processing, using the Soundex as the primary approximate string matching method. The Soundex has been found to be unreliable both as a phonetic and as an approximate string matching method. We found that their data, in many cases, has more than one value per field, suggesting that the data were queried from a 5NF data base. Consider that if a woman has been married 3 times, she may have up to 4 last names on record. This query process produced more than one tuple per database/entity apparently generating a Cartesian product of this data. In many cases, more than a dozen tuples were observed for a single database/entity. This approach is both ineffective and inefficient. An effective RL method should handle this multi-data without redundancy and use edit-distance for approximate string matching. However, due to high computational complexity, edit-distance will not scale well with big data problems. We developed two methodologies for resolving the aforementioned issues: PSH and ALIM. PSH – The Probabilistic Signature Hash is a composite method that increases the speed of Damerau-Levenshtein edit-distance. It combines signature filtering, probabilistic hashing, length filtering and prefix pruning to increase the speed of edit-distance. It is also lossless because it does not lose any true positive matches. ALIM – Aggregate Link and Iterative Match is a graph-based record linkage methodology that uses a multi-graph to store demographic data about people. ALIM performs string matching as records are inserted into the graph. ALIM eliminates data redundancy and stores the relationships between data. We tested PSH for string comparison and found it to be approximately 6,000 times faster than DL. We tested it against the trie-join methods and found that they are up to 6.26 times faster but lose between 10 and 20 percent of true positives. We tested ALIM against a method currently in use by a local health and human services department and found ALIM to produce significantly more matches (even with more restrictive match criteria) and that ALIM ran more than twice as fast. ALIM handles the multi-data problem and PSH allows the use of edit-distance comparison in this RL model. ALIM is more efficient and effective than a currently implemented RL system. This model can also be expanded to perform social network analysis and temporal data modeling. For human services, temporal modeling can reveal how policy changes and treatments affect clients over time and social network analysis can determine the effects of these on whole families by facilitating family linkage.
Temple University--Theses
Tam, Siu-lung. "Linear-size indexes for approximate pattern matching and dictionary matching." Click to view the E-thesis via HKUTO, 2010. http://sunzi.lib.hku.hk/hkuto/record/B44205326.
Повний текст джерелаТодоріко, Ольга Олексіївна. "Моделі та методи очищення та інтеграції текстових даних в інформаційних системах". Thesis, Запорізький національний університет, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/21856.
Повний текст джерелаThe thesis for the candidate degree in technical sciences, speciality 05.13.06 – Information Technologies. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2016. In the thesis the actual scientific and practical problem of increasing the efficiency and quality of cleaning and integration of data in information reference system and information retrieval system is solved. The improvement of information technology of cleaning and integration of data is achieved by reduction of quantity of mistakes in text information by means of use of model of an inflectional paradigm, methods of creation of a lexeme index, advanced methods of tolerant retrieval. The developed model of an inflectional paradigm includes a representation of words as an ordered collection of signatures and an approximate measure of similarity between two representations. The model differs in method of dealing with forms of words and character positions. It provides the basis for the implementation of improved methods of tolerant retrieval, cleaning and integration of datasets. The method of creation of the lexeme index which is based on the offered model of an inflectional paradigm is developed, and it allows mapping a word and all its forms to a record of the index. The method of tolerant retrieval is improved at preliminary filtration stage thanks to the developed model of an inflectional paradigm and the lexeme index. The experimental efficiency evaluation indicates high precision and 99 0,5 % recall. The information technology of cleaning and integration of data is improved using the developed models and methods. The software which on the basis of the developed models and methods carries out tolerant retrieval, cleaning and integration of data sets was developed. Theoretical and practical results of the thesis are introduced in production of document flow of an entrance committee and educational process of mathematical faculty of the State institution of higher education "Zaporizhzhya National University".
Тодоріко, Ольга Олексіївна. "Моделі та методи очищення та інтеграції текстових даних в інформаційних системах". Thesis, НТУ "ХПІ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/21853.
Повний текст джерелаThe thesis for the candidate degree in technical sciences, speciality 05.13.06 – Information Technologies. – National Technical University «Kharkiv Polytechnic Institute», Kharkiv, 2016. In the thesis the actual scientific and practical problem of increasing the efficiency and quality of cleaning and integration of data in information reference system and information retrieval system is solved. The improvement of information technology of cleaning and integration of data is achieved by reduction of quantity of mistakes in text information by means of use of model of an inflectional paradigm, methods of creation of a lexeme index, advanced methods of tolerant retrieval. The developed model of an inflectional paradigm includes a representation of words as an ordered collection of signatures and an approximate measure of similarity between two representations. The model differs in method of dealing with forms of words and character positions. It provides the basis for the implementation of improved methods of tolerant retrieval, cleaning and integration of datasets. The method of creation of the lexeme index which is based on the offered model of an inflectional paradigm is developed, and it allows mapping a word and all its forms to a record of the index. The method of tolerant retrieval is improved at preliminary filtration stage thanks to the developed model of an inflectional paradigm and the lexeme index. The experimental efficiency evaluation indicates high precision and 99 0,5 % recall. The information technology of cleaning and integration of data is improved using the developed models and methods. The software which on the basis of the developed models and methods carries out tolerant retrieval, cleaning and integration of data sets was developed. Theoretical and practical results of the thesis are introduced in production of document flow of an entrance committee and educational process of mathematical faculty of the State institution of higher education «Zaporizhzhya National University».
Dobiášovský, Jan. "Přibližná shoda znakových řetězců a její aplikace na ztotožňování metadat vědeckých publikací." Master's thesis, 2020. http://www.nusl.cz/ntk/nusl-415121.
Повний текст джерелаЧастини книг з теми "Approximate record matching"
Dong, Boxiang, and Hui Wendy Wang. "Efficient Authentication of Approximate Record Matching for Outsourced Databases." In Advances in Intelligent Systems and Computing, 119–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98056-0_6.
Повний текст джерелаMargaritis, Dimitris, Christos Faloutsos, and Sebastian Thrun. "NetCube." In Database Technologies, 2011–36. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-058-5.ch120.
Повний текст джерелаMargaritis, Dimitris, Christos Faloutsos, and Sebastian Thrun. "NetCube." In Bayesian Network Technologies, 54–83. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-141-4.ch004.
Повний текст джерелаТези доповідей конференцій з теми "Approximate record matching"
Gollapalli, Mohammed, Xue Li, Ian Wood, and Guido Governatori. "Approximate Record Matching Using Hash Grams." In 2011 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2011. http://dx.doi.org/10.1109/icdmw.2011.33.
Повний текст джерелаDong, Boxiang, and Wendy Wang. "ARM: Authenticated Approximate Record Matching for Outsourced Databases." In 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI). IEEE, 2016. http://dx.doi.org/10.1109/iri.2016.86.
Повний текст джерелаSchraagen, Marijn. "Complete Coverage for Approximate String Matching in Record Linkage Using Bit Vectors." In 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2011. http://dx.doi.org/10.1109/ictai.2011.116.
Повний текст джерелаJia, Dan, Yong-Yi Wang, and Steve Rapp. "Material Properties and Flaw Characteristics of Vintage Girth Welds." In 2020 13th International Pipeline Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/ipc2020-9658.
Повний текст джерелаRamakrishnan, Kishore Ranganath, Shoaib Ahmed, Benjamin Wahls, Prashant Singh, Maria A. Aleman, Kenneth Granlund, Srinath Ekkad, Federico Liberatore, and Yin-Hsiang Ho. "Gas Turbine Combustor Liner Wall Heat Load Characterization for Different Gaseous Fuels." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11283.
Повний текст джерелаCummings, Scott M. "Prediction of Rolling Contact Fatigue Using Instrumented Wheelsets." In ASME 2008 Rail Transportation Division Fall Technical Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/rtdf2008-74013.
Повний текст джерелаЗвіти організацій з теми "Approximate record matching"
Day, Christopher M., Howell Li, Sarah M. L. Hubbard, and Darcy M. Bullock. Observations of Trip Generation, Route Choice, and Trip Chaining with Private-Sector Probe Vehicle GPS Data. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317368.
Повний текст джерела