A Higher-Order Data Flow Model for Heterogeneous Big Data

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)


We introduce a data flow model that supports highly parallelisable design patterns, but which also has useful properties for analysing data serially over extended time periods without requiring traditional Big Data computing facilities. The model ranges over a class of higher-order relations which are sufficiently expressive to represent a wide variety of unstructured, semi-structured and structured data. Using JSONMatch, our web service implementation of the model, we show that the combination of this model and higher-order representation provides a powerful and extensible framework that is particularly well suited to analysing Big Variety data in a web application context.
Original languageEnglish
Title of host publicationIEEE International Conference on Big Data
Place of PublicationSanta Clara, CA
PublisherIEEE Computer Society
ISBN (Print)978-1-4799-1292-6
Publication statusPublished - Oct 2013
EventIEEE International Conference on Big Data - Santa Clara, United States
Duration: 6 Oct 20139 Oct 2013


ConferenceIEEE International Conference on Big Data
Country/TerritoryUnited States
CitySanta Clara


  • heterogeneous data
  • data integration
  • data mining
  • NoSQL
  • cloud computing
  • web services
  • e-Science
  • e-Research


Dive into the research topics of 'A Higher-Order Data Flow Model for Heterogeneous Big Data'. Together they form a unique fingerprint.

Cite this