Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Data Fusion

  • Jesús Garcia
  • José Manuel Molina
  • Antonio Berlanga
  • Miguel Angel Patricio
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_5-1

Introduction and Key Concepts of Information Fusion: Data, Models, and Context

Information fusion (IF) is a multi-domain-growing field aiming to provide data processes for situation understanding (Liggins et al. 2008). Globally, fusion systems aim to integrate sensor data and information/knowledge databases, contextual information, mission goals, etc., to describe dynamically changing situations. In a sense, the goal of information fusion is to obtain continuous refinements of estimates and assessments of a subset of the world based on partial observations and the evaluation of the need for additional sources or modification of the process itself, to achieve improved results.

The capability to fuse digital data and generate useful information is conditioned by the quality of inputs, whether device-derived or text-based. Data are generated in different formats, some of them unstructured and may be inaccurate, incomplete, ambiguous, or contradictory. The key aspect in modern DF...
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References

  1. Amazon (2017) https://aws.amazon.com/. Accessed Oct 15th 2017
  2. Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D (2010) A survey of context modelling and reasoning techniques. Pervasive Mob Comput 6(2):161–180CrossRefGoogle Scholar
  3. Biermann J, Garcia J, Krenc K, Nimier V, Rein K, Snidaro L (2014) Multi-level fusion of hard and soft information. 17th International Conference on Information Fusion, Salamanca. July 2014Google Scholar
  4. Biermann J, Garcia J, Krenc K, Nimier V, Rein K, Snidaro L (2016) Standardized representation via BML to support multi-level fusion of hard and soft information. Symposium IST/SET-216 Information Fusion (Hard and Soft) for Intelligence, Surveillance & Reconnaissance (ISR). Norfolk Virginia, USA 4, 5 May 2015Google Scholar
  5. Bishop C (2004) Pattern recognition and machine learning. Springer, New YorkMATHGoogle Scholar
  6. Caragea C, McNeese N, Jaiswal A, Traylor G, Kim H-W, Mitra P, Wu D, Tapia A-H, Giles L, Jansen B-L, Yen J (2011) Classifying text messages for the Haiti earthquake. Proceedings of the 8th International ISCRAM Conference – Lisbon, Portugal, May 2011Google Scholar
  7. Gómez J, García J, Patricio MA, Molina JM, Llinas J (2011) High-level information fusion in visual sensor networks. In: Ang K-L, Seng K-P (eds) Information processing in wireless sensor networks: technology, trends and applications, IGI Global, Hershey, PennsylvaniaGoogle Scholar
  8. Liggins M, Hall D, Llinas J (2008) Handbook of multisensor data fusion: theory and practice, 2nd edn. CRC Press, Boca Raton, FloridaGoogle Scholar
  9. Microsoft (2017) https://cloud.microsoft.com/en-us/. Accessed Oct 15th 2017
  10. Snidaro L, Garcia J, Corchado JM (2014) Guest editorial: context-based information fusion. Inf. Fusion, Special Issue on Context-Based Information Fusion, 2014Google Scholar
  11. Snidaro L, García J, Llinas J, Blasch E (2016) Context-enhanced information fusion. Boosting real-world performance with domain knowledge. Springer, Basel, SwitzerlandGoogle Scholar
  12. Steinberg A-N, Bowman C (2009) Revisions to the JDL data fusion model, Chap. 3. In: Liggins M, Hall D, Llinas J (eds) Handbook of multisensor data fusion. CRC Press, London, pp 45–68Google Scholar
  13. Szuster P, Molina J-M, Garcia J, Kolodziej J (2017) Data fusion in cloud computing: big data approach. European Conference on Modelling and Simulation, ECMS 2017, Budapest, Hungary, May 23–26, pp 569–575Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jesús Garcia
    • 1
    • 2
  • José Manuel Molina
    • 1
    • 2
  • Antonio Berlanga
    • 1
    • 2
  • Miguel Angel Patricio
    • 1
    • 2
  1. 1.Applied Artificial Intelligence GroupUniversidad Carlos III de MadridColmenarejoSpain
  2. 2.Computer Science and EngineeringUniversidad Carlos III de MadridColmenarejoSpain

Section editors and affiliations

  • Maik Thiele
    • 1
  1. 1.Database Systems GroupTechnische Universität DresdenDresdenDeutschland