Information integration

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Information integration (II) is the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations. It is used in data mining and consolidation of data from unstructured or semi-structured resources. Typically, information integration refers to textual representations of knowledge but is sometimes applied to rich-media content. Information fusion, which is a related term, involves the combination of information into a new set of information towards reducing redundancy and uncertainty.[1]

Examples of technologies available to integrate information include deduplication, and string metrics which allow the detection of similar text in different data sources by fuzzy matching. A host of methods for these research areas are available such as those presented in the International Society of Information Fusion. Other methods rely on causal estimates of the outcomes based on a model of the sources.[2]

See also


  • M. E. Liggins, D. L. Hall, and J. Llinas. Multisensor Data Fusion, (2008). Theory and Practice (Multisensor Data Fusion) (Second ed.). CRC. ISBN 978-1-4200-5308-1. 
  • D. L. Hall, S. A. H. McMullen (2004). Mathematical Techniques in Multisensor Data Fusion. ISBN 1-58053-335-3. 
  • Springer, Information Fusion in Data Mining (2003), ISBN 3-540-00676-1
  • H. B. Mitchell, Multi-sensor Data Fusion – An Introduction (2007) Springer-Verlag, Berlin, ISBN 978-3-540-71463-7
  • S. Das, High-Level Data Fusion (2008), Artech House Publishers, Norwood, MA, ISBN 978-1-59693-281-4 and 1596932813
  • E. P. Blasch, E. Bosse, and D. A. Lambert, High-Level Information Fusion Management and System Design (2012), Artech House Publishers, Norwood, MA. ISBN 1608071510 | ISBN 978-1608071517
  • L. Snidaro, J. Garcia-Herrero, J. Llinas et al. (2016). Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge. Springer-Verlag. ISBN 978-3319289694. 


  1. ^ M. Haghighat, M. Abdel-Mottaleb, & W. Alhalabi (2016). Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition. IEEE Transactions on Information Forensics and Security, 11(9), 1984-1996.
  2. ^ P.K. Davis, D. Manheim, W.L. Perry, J. Hollywood (2015). In Proceedings of the 2015 Winter Simulation Conference (WSC '15). IEEE Press, Piscataway, NJ, USA, 2586-2597.

External links

  • Discriminant Correlation Analysis (DCA)
  • Information Integration Using Logical View LNCS 1997.
  • International Society of Information Fusion
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