NIST Big Data Interoperability Framework: Volume 7, Standards Roadmap

While opportunities exist with Big Data, the data can overwhelm traditional technical approaches. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework (BDIF) series of volumes. This volume, Volume 7, contains summaries of the work presented in the other six volumes, an investigation of standards related to Big Data, and an inspection of gaps in those standards.

[1]  A. H. Ball,et al.  How to Cite Datasets and Link to Publications:A Report of the Digital Curation Centre , 2012 .

[2]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 4, Security and Privacy , 2019 .

[3]  Charles Anderson,et al.  The end of theory: The data deluge makes the scientific method obsolete , 2008 .

[4]  Fatos Xhafa,et al.  P2P data replication and trustworthiness for a JXTA-Overlay P2P system using fuzzy logic , 2013, Appl. Soft Comput..

[5]  Shang Gao,et al.  Modeling a Dynamic Data Replication Strategy to Increase System Availability in Cloud Computing Environments , 2012, Journal of Computer Science and Technology.

[6]  Qing He,et al.  Parallel K-Means Clustering Based on MapReduce , 2009, CloudCom.

[7]  Peter Baumann,et al.  Extending the SQL array concept to support scientific analytics , 2014, SSDBM '14.

[8]  Nigel Shadbolt,et al.  Resource Description Framework (RDF) , 2009 .

[9]  Jens Klump,et al.  How do you assign persistent identifiers to extracts from large, complex, dynamic data sets that underpin scholarly publications? , 2016 .

[10]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: volume 1, definitions, version 2 , 2018 .

[11]  nbspAbdullah Al-Shomrani,et al.  Big Data Security and Privacy Challenges , 2018 .

[12]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 5, Architectures White Paper Survey , 2015 .

[13]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: , 2019 .

[14]  Knut Blind,et al.  Research and standardisation in nanotechnology: evidence from Germany , 2009 .

[15]  Geoffrey C. Fox,et al.  NIST Big Data Interoperability Framework: Volume 3, Use Cases and General Requirements , 2019 .

[16]  Nicolaus Henke,et al.  The age of analytics: competing in a data-driven world , 2016 .

[17]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 2, Big Data Taxonomies , 2015 .

[18]  Georgy Kopanitsa,et al.  Development, implementation and evaluation of an information model for archetype based user responsive medical data visualization , 2015, J. Biomed. Informatics.

[19]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 9, Adoption and Modernization , 2018 .

[20]  Jingdong Wang,et al.  Composite Quantization for Approximate Nearest Neighbor Search , 2014, ICML.

[21]  Krisztian Buza,et al.  SOHAC: Efficient Storage of Tick Data That Supports Search and Analysis , 2012, ICDM.

[22]  Peter Baumann,et al.  The OGC web coverage processing service (WCPS) standard , 2010, GeoInformatica.

[23]  C. Johnman,et al.  Big data! Big deal? , 2015, Public health.