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.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Big Data |
| Place of Publication | Santa Clara, CA |
| Publisher | IEEE Computer Society |
| Pages | 569-573 |
| ISBN (Print) | 978-1-4799-1292-6 |
| DOIs | |
| Publication status | Published - Oct 2013 |
| Event | IEEE International Conference on Big Data - Santa Clara, United States Duration: 6 Oct 2013 → 9 Oct 2013 |
Conference
| Conference | IEEE International Conference on Big Data |
|---|---|
| Country/Territory | United States |
| City | Santa Clara |
| Period | 6/10/13 → 9/10/13 |
Keywords
- heterogeneous data
- data integration
- data mining
- NoSQL
- cloud computing
- web services
- e-Science
- e-Research
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HPC (High Performance Computing) and HTC (High Throughput Computing) Facilities
Alam, S. R. (Manager), Williams, D. A. G. (Manager), Eccleston, P. E. (Manager) & Greene, D. (Manager)
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