Encyclopedia of Big Data

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

Data Architecture and Design

  • Erik W. Kuiler
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32001-4_297-1

Introduction

The availability of Big Data sets has led many organizations to shift their emphases from supporting transaction-oriented data processing to supporting data-centric analytics and applications. The increasing rapidity of dynamic data flows, such as those generated by IoT applications and devices, the increasing sophistication of interoperability mechanisms, and the concomitant decreasing costs of data storage have transformed not only data acquisition and management paradigms but have also overloaded available ICT resources, thereby diminishing their capabilities to support organizational data and information requirements. Due to the difficulties of managing Big Data sets and increasingly more complex analytical models, transaction processing-focused ICT architectures that were sufficient to manage small data sets may require enhancements and re-purposing to support Big Data analytics.

A number of properties inform properly designed Big Data system architectures. Such...

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Further Readings

  1. NIST Big Data Public Working Group Reference Architecture Subgroup. (2015). NIST big data interoperability framework: volume 6: Reference architecture. Washington DC: US Department of Commerce National Institute of Standards and Technology. Downloaded from https://bigdatawg.nist.gov.
  2. Santos, M. Y., Sá, J., Costa, C., Galvão, J., Andrade, C., Martinho, B., Lima, F. V., & Costa, E. (2017). A big data analytics architecture for industry 4.0. In A. Rocha, A. Correia, H. Adeli, L. Reis, & S. Costanzo (Eds.), Recent advances in information systems and technologies. WorldCIST 2017 (Advances in intelligent systems and computing) (Vol. 570, pp. 175–184). Cham: Springer. (Porto Santo Island, Madeira, Portugal).Google Scholar
  3. Viana, P., & Sato, L. (2015). A proposal for a reference architecture for long-term archiving, preservation, and retrieval of big data. In 13th international conference on Trust, Security and Privacy in Computing and Communications, (TrustCom) (pp. 622–629). Beijing: IEEE Computer Society.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.George Mason UniversityArlingtonUSA