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A Higher-Order Data Flow Model for Heterogeneous Big Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationIEEE International Conference on Big Data
Place of PublicationSanta Clara, CA
Publisher or commissioning bodyIEEE Computer Society
Pages569-573
ISBN (Print)978-1-4799-1292-6
DOIs
DatePublished - Oct 2013
EventIEEE International Conference on Big Data - Santa Clara, United States
Duration: 6 Oct 20139 Oct 2013

Conference

ConferenceIEEE International Conference on Big Data
CountryUnited States
CitySanta Clara
Period6/10/139/10/13

Abstract

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.

    Research areas

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

Event

IEEE International Conference on Big Data

Duration6 Oct 20139 Oct 2013
CitySanta Clara
CountryUnited States
Degree of recognitionInternational event

Event: Conference

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