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Retrieving missing data based on key levels


J. Catherine Princy, V. S. Priyanga, P. Sailaja
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Data imputation method is to fill the missing values from a group of data sets. Existing imputation approaches to non-quantities string knowledge may be roughly placed into two categories: 1.Inferring based approaches and 2.Retrieving primarily based approaches. Specifically, the inferring-based approaches notice substitutes or estimations for the missing ones from the entire partook the information set.However,they usually come short in filling in distinctive missing attribute values that don’t exist in the complete part of the information set. During this project we tend to investigate the interaction between the inferring based methods and also the retrieving based approaches. we tend to show that retrieving a tiny low variety of selected missing values will highly improve the imputation recall of the inferring based ways. With this institution ,we tend to propose associate interactive Retrieving-Inferring knowledge imputation approach ,that performs retrieving and inferring alternately in filling missing attribute in an exceedingly data sets to confirm the high recall at the minimum values. This approach faces a challenge of choosing the smallest amount variety of missing values for retrieving to maximize the amount of inferable values.

J. Catherine Princy, V. S. Priyanga, P. Sailaja

Data Imputation, Data Repairing, Interactive Retrieving-Inferring.

  1. S. Abiteboul, R.Hull, and V.Vianu Foundations of databases. Addison-Wesley Reading, 1995.
  2. E.Agichtein and L.Gravano.Snowball: Extracting relations from large plaintextcollections. In ACM DL, pages 85–94, 2000.
  3. J. Barnard and D. Rubin. Small-sample degrees of freedom with multiple imputation. Biometrika, 86(4):948–955, 1999.
  4. G. Batista and M. Monard. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5-6):519–533, 2003.
  5. P. Bohannon, W. Fan, M. Flaster, and R. Rastogi.A cost-based model and effective heuristic for repairing constraints by value modification. In SIGMOD, pages 143–154, 2005.
  6. P. Bohannon, W. Fan, F. Geerts, X. Jia, and A. Kementsietsidis.Conditional functional dependencies for data cleaning. In ICDE, pages 746–755, 2007.
  7. S. Brin. Extracting patterns and relations from the world wide web. The World Wide Web and Databases, pages 172–183, 1999.
  8. M. Cafarella, A. Halevy, and N. Khoussainova.Data integration for the relational web. PVLDB, 2(1):1090–1101, 2009.
  9. M. Cafarella, A. Halevy, D. Wang, E. Wu, and Y. Zhang.Webtables: exploring the power of tables on the web. PVLDB, 1(1):538–549, 2008.
  10. H. Elmeleegy, J. Madhavan, and A. Halevy.Harvesting relational tables from lists on the web. PVLDB, 2(1):1078–1089, 2009.

Publication Details

Published in : Volume 2 | Issue 2 | March-April - 2016
Date of Publication Print ISSN Online ISSN
2016-04-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
293-295 IJSRSET162268   Technoscience Academy

Cite This Article

J. Catherine Princy, V. S. Priyanga, P. Sailaja, "Retrieving missing data based on key levels", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.293-295, March-April-2016.
URL : http://ijsrset.com/IJSRSET162268.php