Encyclopedia of Big Data

Living Edition
| Editors: Laurie A. Schintler, Connie L. McNeely

Anomaly Detection

  • Feras A. BatarsehEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32001-4_223-1



Anomaly Detection is the process of uncovering anomalies, errors, bugs, and defects in software to eradicate them and increase the overall quality of a system. Finding anomalies in big data analytics is especially important. Big data is “unstructured” by definition, hence, the process of structuring it is continually presented with anomaly detection activities.


Data engineering is a challenging process. Different stages of the process affect the outcome in a variety of ways. Manpower, system design, data formatting, variety of data sources, size of the software, and project budget are among the variables that could alter the outcome of an engineering project. Nevertheless, software and data anomalies pose one of the most challenging obstacles in the success of any project. Anomalies have postponed space shuttle launches, caused problems for airplanes, and disrupted credit card and...

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Further Readings

  1. Batarseh, F. (2012). Incremental lifecycle validation of knowledge-based systems through CommonKADS. Ph.D. Dissertation Registered at the University of Central Florida and the Library of Congress.Google Scholar
  2. Batarseh, F., & Gonzalez, A. (2015). Predicting failures in contextual software development through data analytics. Proceedings of Springer’s Software Quality Journal.Google Scholar
  3. Planning Report for NIST. (2002). The economic impacts of inadequate infrastructure for software testing. A report published by the US Department of Commerce.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.College of ScienceGeorge Mason UniversityFarifaxUSA