The schema last approach to data fusion

Neil Brittliff, Dharmendra Sharma

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

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Big Data presents new challenges that require new and novel approaches in order to resolve the problems associated with the variability and variety of data obtained from multiple sources. This paper focuses on how to manage variety and the eclectic nature of big data using a technique known as 'Schema Last'. The 'Schema Last' approach is a frame work which defers the application of a descriptive model until it is required. This paper also provides a formal definition of the 'Schema Last' methodology and demonstrates the effectiveness over the more traditional Extract- Transform-Load methodologies employed in many organizations. The 'Schema Last' approach can be used as input to Map Reduction, Index creation and various data mining techniques. Ultimately, the Schema Last approach provides the frame-work to 'fuse' semistructured data into a single coherent view.

Original languageEnglish
Title of host publicationTwelfth Australasian Data Mining Conference (AusDM14)
Subtitle of host publicationProceedings of the 12th Australasian Data Mining Conference, AusDM 2014
EditorsXue Li, Lin Liu, Kok-Leong Ong, Yanchang Zhao
Place of PublicationBrisbane, Australia
PublisherAustralian Computer Society
Number of pages8
ISBN (Print)9781921770173
Publication statusPublished - 2014

Publication series

NameConferences in Research and Practice in Information Technology Series
ISSN (Print)1445-1336


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