Relevant bibliographies by topics / Record matching

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  2. Dissertations / Theses
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Academic literature on the topic 'Record matching'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Record matching"

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Somasekhar, G., SeshaSravani K, Keerthi P, and Sai Sandeep G. "Record linkage and deduplication using traditional blocking." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 294. http://dx.doi.org/10.14419/ijet.v7i1.1.9705.

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Record Linkage and Deduplication are the two process that are used in matching records. Matching of records is done to remove the duplicate records. These duplicate records highly influence the outputs of data mining and data processing. If the matching of records is done on the single database, it is called Deduplication. In Deduplication we check for the duplicate records in the single database. Unlike deduplication if the matching of the records is done on the several databases it is called as record linkage. In this paper we also discuss about the indexing technique called as traditional blocking which is used to remove non matching pairs that leads to the less number of record pair to be compared.
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Copas, J. B., and F. J. Hilton. "Record Linkage: Statistical Models for Matching Computer Records." Journal of the Royal Statistical Society. Series A (Statistics in Society) 153, no. 3 (1990): 287. http://dx.doi.org/10.2307/2982975.

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Winkler, William E. "Matching and record linkage." Wiley Interdisciplinary Reviews: Computational Statistics 6, no. 5 (July 2, 2014): 313–25. http://dx.doi.org/10.1002/wics.1317.

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Fan, Wenfei, Hong Gao, Xibei Jia, Jianzhong Li, and Shuai Ma. "Dynamic constraints for record matching." VLDB Journal 20, no. 4 (November 16, 2010): 495–520. http://dx.doi.org/10.1007/s00778-010-0206-6.

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Fan, Wenfei, Xibei Jia, Jianzhong Li, and Shuai Ma. "Reasoning about record matching rules." Proceedings of the VLDB Endowment 2, no. 1 (August 2009): 407–18. http://dx.doi.org/10.14778/1687627.1687674.

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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.

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Verykios, 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.

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Wiseley, W. Charles. "Collaborative Administrative Record Matching in California." New Directions for Community Colleges 1998, no. 104 (1998): 41–51. http://dx.doi.org/10.1002/cc.10404.

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Kan, Min-Yen, and Yee Fan Tan. "Record matching in digital library metadata." Communications of the ACM 51, no. 2 (February 2008): 91–94. http://dx.doi.org/10.1145/1314215.1314231.

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Bianchi 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.

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Cost considerations are critical in the analysis and prevention of traffic crashes. Integration of cost data into crash datasets facilitates the crash-cost analyses with all their related attributes. It is, however, a challenging task because of the lack of availability of unique identifiers across the databases and because of privacy and confidentiality regulations. This study performed a record linkage comparison between the deterministic and probabilistic approaches using attributes matching techniques with numerical distance and weight patterns under the Fellegi–Sunter approach. As a result, the deterministic algorithm developed using the exact match of the 14-digit police accident record number had an overall matching performance of 52.38% of real matched records, while the probabilistic algorithm had an overall matching performance of 70.41% with a quality measurement of the sensitivity of 99.99%. The deterministic approach was thus outperformed by the probabilistic approach by approximately 20% of records matched. The probabilistic matching with numerical variables seems to be a good matching strategy supported by quality variables. On record matching, a multivariable regression model was developed to model medical costs and identify factors that increase the costs of treating injured claimants in Puerto Rico.
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Dissertations / Theses on the topic "Record matching"

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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.

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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.

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Computer and Information Science
Ph.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
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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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Grzebala, Pawel B. "Private Record Linkage: A Comparison of Selected Techniques for Name Matching." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1461096562.

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Sze, Wui-fung. "Robust feature-point based image matching." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37153262.

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Denk, Michaela, Peter Hackl, and Norbert Rainer. "String Matching Techniques: An Empirical Assessment Based on Statistics Austria's Business Register." Austrian Statistical Society, c/o Bundesanstalt Statistik Austria, 2005. http://epub.wu.ac.at/5630/1/415%2D1277%2D1%2DSM.pdf.

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The maintenance and updating of Statistics Austria's business register requires a regularly matching of the register against other data sources; one of them is the register of tax units of the Austrian Federal Ministry of Finance. The matching process is based on string comparison via bigrams of enterprise names and addresses, and a quality class approach assigning pairs of register units into classes of different compliance (i.e., matching quality) based on bigram similarity values and the comparison of other matching variables, like the NACE code or the year of foundation. Based on methodological research concerning matching techniques carried out in the DIECOFIS project, an empirical comparison of the bigram method and other string matching techniques was conducted: the edit distance, the Jaro algorithm and the Jaro-Winkler algorithm, the longest common subsequence and the maximal match were selected as appropriate alternatives and evaluated in the study. This paper briefly introduces Statistics Austria's business register and the corresponding maintenance process and reports on the results of the empirical study.
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Lai, Ka-ying. "Solving multiparty private matching problems using Bloom-filters." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37854847.

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Hackl, Peter, and Michaela Denk. "Data Integration: Techniques and Evaluation." Austrian Statistical Society, 2004. http://epub.wu.ac.at/5631/1/435%2D1317%2D1%2DSM.pdf.

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Within the DIECOFIS framework, ec3, the Division of Business Statistics from the Vienna University of Economics and Business Administration and ISTAT worked together to find methods to create a comprehensive database of enterprise data required for taxation microsimulations via integration of existing disparate enterprise data sources. This paper provides an overview of the broad spectrum of investigated methodology (including exact and statistical matching as well as imputation) and related statistical quality indicators, and emphasises the relevance of data integration, especially for official statistics, as a means of using available information more efficiently and improving the quality of a statistical agency's products. Finally, an outlook on an empirical study comparing different exact matching procedures in the maintenance of Statistics Austria's Business Register is presented.
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Denk, Michaela, and Peter Hackl. "Data Integration and Record Matching: An Austrian Contribution to Research in Official Statistics." Austrian Statistical Society, 2003. http://epub.wu.ac.at/5632/1/464%2D1378%2D1%2DSM.pdf.

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Data integration techniques are one of the core elements of DIECOFIS, an EU-funded international research project that aims at developing a methodology for the construction of a system of indicators on competitiveness and fiscal impact on enterprise performance. Data integration is also of major interest for official statistics agencies as a means of using available information more efficiently and improving the quality of the agency's products. The Austrian member of the project consortium comprises university departments, representatives from the Bundesanstalt Statistik Austria, from the Statistical Department of the Austrian Economic Chamber, and from ec3, a non-profit research corporation. This paper gives a short report on DIECOFIS in general and on the Austrian contribution to the project, mainly dealing with data integration methodology. Various papers that have been read at the DIECOFIS workshop last November in Vienna, will be published as a Special Issue of the Austrian Journal of Statistics.
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Wong, Iok Lan. "Face detection in skin color modeling and template matching." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1795653.

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Books on the topic "Record matching"

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Scanu, Mauro. Metodi statistici per il record linkage. Roma: ISTAT, 2003.

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service), SpringerLink (Online, ed. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Gill, Leicester. Methods for automatic record matching and linkage and their use in national statistics. London: ONS, 2001.

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Workshop on Exact Matching Methodologies (1985 Arlington, Va.). Record linkage techniques, 1985: Proceedings of the Workshop on Exact Matching Methodologies, Arlington, Virginia, May 9-10, 1985 : co-sponsored with the Washington Statistical Society and the Federal Committee on Statistical Methodology. [Washington, D.C.]: Dept. of the Treasury, Internal Revenue Service, Statistics of Income Division, 1986.

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Management, United States Congress Senate Committee on Governmental Affairs Subcommittee on Oversight of Government. Computer Matching and Privacy Protection Act of 1986: Hearing before the Subcommittee on Oversight of Government Management of the Committee on Governmental Affairs, United States Senate, Ninety-ninth Congress, second session, on S. 2756 ... September 16, 1986. Washington: U.S. G.P.O., 1986.

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United, States Congress House Committee on Government Operations Government Information Justice and Agriculture Subcommittee. Computer Matching and Privacy Protection Amendments of 1990: Hearing before the Government Information, Justice, and Agriculture Subcommittee of the Committee on Government Operations, House of Representatives, One Hundred First Congress, second session, on H.R. 5450 ... September 11, 1990. Washington: U.S. G.P.O., 1991.

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Office, General Accounting. Tax administration: Changes to IRS's Schedule K-1 document matching program burdened compliant taxpayers : report to the Chair, Committee on Small Business and Entrepreneurship, U.S. Senate. [Washington, D.C.]: GAO, 2003.

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Rudin, Robert, Richard Hillestad, M. Ridgely, Nabeel Qureshi, John Davis, and Shira Fischer. Defining and Evaluating Patient-Empowered Approaches to Improving Record Matching. RAND Corporation, 2018. http://dx.doi.org/10.7249/rr2275.

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Data Matching Concepts And Techniques For Record Linkage Entity Resolution And Duplicate Detection. Springer, 2012.

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Christen, Peter. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, 2012.

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Book chapters on the topic "Record matching"

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Arasu, Arvind, and Josep Domingo-Ferrer. "Record Matching." In Encyclopedia of Database Systems, 3129–35. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_594.

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Arasu, Arvind, and Josep Domingo-Ferrer. "Record Matching." In Encyclopedia of Database Systems, 2354–58. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_594.

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Arasu, Arvind, and Josep Domingo-Ferrer. "Record Matching." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_594-2.

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Christen, Peter. "Field and Record Comparison." In Data Matching, 101–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31164-2_5.

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Winkler, William E. "Matching and Record Linkage." In Business Survey Methods, 353–84. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118150504.ch20.

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Scannapieco, Monica. "Object Matching: New Challenges for Record Linkage." In The Philosophy of Information Quality, 95–106. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07121-3_6.

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Yang, Qiang, Zhixu Li, Jun Jiang, Pengpeng Zhao, Guanfeng Liu, An Liu, and Jia Zhu. "NokeaRM: Employing Non-key Attributes in Record Matching." In Web-Age Information Management, 438–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21042-1_36.

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Dou, Chenxiao, Daniel Sun, and Raymond K. Wong. "Unsupervised Blocking of Imbalanced Datasets for Record Matching." In Web Information Systems Engineering – WISE 2016, 172–86. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48743-4_14.

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Wang, Jau-Hwang, Bill T. Lin, Ching-Chin Shieh, and Peter S. Deng. "Criminal Record Matching Based on the Vector Space Model." In Intelligence and Security Informatics, 386. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44853-5_36.

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Takasu, Atsuhiro, Daiji Fukagawa, and Tatsuya Akutsu. "Latent Topic Extraction from Relational Table for Record Matching." In Discovery Science, 449–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04747-3_38.

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Conference papers on the topic "Record matching"

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Arasu, Arvind, Surajit Chaudhuri, Kris Ganjam, and Raghav Kaushik. "Incorporating string transformations in record matching." In the 2008 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1376616.1376742.

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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.

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Inan, Ali, Murat Kantarcioglu, Gabriel Ghinita, and Elisa Bertino. "Private record matching using differential privacy." In the 13th International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1739041.1739059.

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Arasu, Arvind, Surajit Chaudhuri, and Raghav Kaushik. "Transformation-based Framework for Record Matching." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497412.

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Tai, Xiao Hui. "Record Linkage and Matching Problems in Forensics." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00081.

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Dong, Boxiang, and Hui Wang. "EARRING: Efficient Authentication of Outsourced Record Matching." In 2017 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2017. http://dx.doi.org/10.1109/iri.2017.16.

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Chiang, Yueh-Hsuan, AnHai Doan, and Jeffrey F. Naughton. "Modeling entity evolution for temporal record matching." In SIGMOD/PODS'14: International Conference on Management of Data. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2588555.2588560.

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James, Scott D., and John C. Hansen. "Least-criteria record matching in database systems." In 2007 IEEE International Conference on Electro/Information Technology. IEEE, 2007. http://dx.doi.org/10.1109/eit.2007.4374498.

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Arasu, Arvind, Michaela Götz, and Raghav Kaushik. "On active learning of record matching packages." In the 2010 international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807167.1807252.

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Fan, Wenfei, Jianzhong Li, Shuai Ma, Nan Tang, and Wenyuan Yu. "Interaction between record matching and data repairing." In the 2011 international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1989323.1989373.

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Reports on the topic "Record matching"

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Beuermann, Diether, Sabine Rieble-Aubourg, and Tatiana Zarate-Barrera. Matching Educational and Criminal Records at the Individual Level in Trinidad and Tobago: Methodology and Implementation. Inter-American Development Bank, December 2016. http://dx.doi.org/10.18235/0000599.

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