IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v12y2010i1d10.1007_s11009-008-9092-4.html
   My bibliography  Save this article

Random Databases with Approximate Record Matching

Author

Listed:
  • Oleg Seleznjev

    (Umeå University
    Moscow State University)

  • Bernhard Thalheim

    (Christian-Albrechts University)

Abstract

In many database applications in telecommunication, environmental and health sciences, bioinformatics, physics, and econometrics, real-world data are uncertain and subjected to errors. These data are processed, transmitted and stored in large databases. We consider stochastic modelling for databases with uncertain data and for some basic database operations (for example, join, selection) with exact and approximate matching. Approximate join is used for merging or data deduplication in large databases. Distribution and mean of the join sizes are studied for random databases. A random database is treated as a table with independent random records with a common distribution (or a set of random tables). These results can be used for integration of information from different databases, multiple join optimization, and various probabilistic algorithms for structured random data.

Suggested Citation

  • Oleg Seleznjev & Bernhard Thalheim, 2010. "Random Databases with Approximate Record Matching," Methodology and Computing in Applied Probability, Springer, vol. 12(1), pages 63-89, March.
    • Handle: RePEc:spr:metcap:v:12:y:2010:i:1:d:10.1007_s11009-008-9092-4
    DOI: 10.1007/s11009-008-9092-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11009-008-9092-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11009-008-9092-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J. B. Copas & F. J. Hilton, 1990. "Record Linkage: Statistical Models for Matching Computer Records," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(3), pages 287-312, May.
    2. Oleg Seleznjev & Bernhard Thalheim, 2003. "Average Case Analysis in Database Problems," Methodology and Computing in Applied Probability, Springer, vol. 5(4), pages 395-418, December.
    3. Shykula, Mykola & Seleznjev, Oleg, 2006. "Stochastic structure of asymptotic quantization errors," Statistics & Probability Letters, Elsevier, vol. 76(5), pages 453-464, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Gyumin & Lee, Sungjun & Lee, Changyong, 2023. "Inventor–licensee matchmaking for university technology licensing: A fastText approach," Technovation, Elsevier, vol. 125(C).
    2. Vo, Thanh Huan & Chauvet, Guillaume & Happe, André & Oger, Emmanuel & Paquelet, Stéphane & Garès, Valérie, 2023. "Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    3. Debabrata Dey, 2003. "Record Matching in Data Warehouses: A Decision Model for Data Consolidation," Operations Research, INFORMS, vol. 51(2), pages 240-254, April.
    4. Bartolini, Fabio & Brunori, Gianluca & Coli, Alessandra & Landi, Chiara & Pacini, Barbara, 2015. "Assessing the Causal Effect of Decoupled Payments on farm labour in Tuscany Using Propensity Score Methods," 2015 Conference, August 9-14, 2015, Milan, Italy 211200, International Association of Agricultural Economists.
    5. Francesco D. d’Ovidio & Paola Perchinunno & Laura Antonucci, 2021. "Data Integration Techniques for the Identification of Poverty Profiles," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 515-531, August.
    6. Jixian Wang & Peter Donnan, 2002. "Adjusting for missing record linkage in outcome studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(6), pages 873-884.
    7. Skinner, Chris J., 2007. "The probability of identification: applying ideas from forensic statistics to disclosure risk assessment," LSE Research Online Documents on Economics 39105, London School of Economics and Political Science, LSE Library.
    8. Leonenko, Nikolaj & Seleznjev, Oleg, 2010. "Statistical inference for the [epsilon]-entropy and the quadratic Rényi entropy," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 1981-1994, October.
    9. C. J. Skinner, 2007. "The probability of identification: applying ideas from forensic statistics to disclosure risk assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 195-212, January.
    10. Xinyi Lu & Mevin B. Hooten & Andee Kaplan & Jamie N. Womble & Michael R. Bower, 2022. "Improving Wildlife Population Inference Using Aerial Imagery and Entity Resolution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 364-381, June.
    11. Perchinunno, Paola & Mongelli, Lucia & d’Ovidio, Francesco D., 2020. "Statistical matching techniques in order to plan interventions on socioeconomic weakness: An Italian case," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    12. Bruce D. Meyer & Nikolas Mittag, 2019. "Misreporting of Government Transfers: How Important Are Survey Design and Geography?," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 230-253, July.
    13. Li‐Chun Zhang & Tiziana Tuoto, 2021. "Linkage‐data linear regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 522-547, April.
    14. Meyer, Bruce D. & Mittag, Nikolas, 2018. "Misreporting of Government Transfers: How Important Are Survey Design and Geography?," IZA Discussion Papers 12038, Institute of Labor Economics (IZA).
    15. Debabrata Dey & Sumit Sarkar & Prabuddha De, 1998. "A Probabilistic Decision Model for Entity Matching in Heterogeneous Databases," Management Science, INFORMS, vol. 44(10), pages 1379-1395, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:metcap:v:12:y:2010:i:1:d:10.1007_s11009-008-9092-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.