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Analysis of SEC-SAXS data via EFA deconvolution and Scatter
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Making big data small.

Wenfei Fan1,2,3

  • 1University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK.

Proceedings. Mathematical, Physical, and Engineering Sciences
|June 26, 2019
PubMed
Summary
This summary is machine-generated.

Small companies can now perform big data analytics with limited resources using Boundedly EvAlable SQL (BEAS). BEAS makes big data manageable by fetching only necessary data subsets for exact or approximate query answers.

Keywords:
approximate query answeringbig databounded evaluationdatabase systemsstructured query language

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Area of Science:

  • Computer Science
  • Data Science
  • Database Systems

Background:

  • Big data analytics is resource-intensive, often requiring expensive parallel processing on clusters.
  • Small companies with limited resources face challenges in accessing and analyzing large datasets.
  • Existing solutions are often inaccessible due to high costs and infrastructure requirements.

Purpose of the Study:

  • To introduce Boundedly EvAlable SQL (BEAS), a novel system designed for querying big data under constrained resources.
  • To demonstrate that big data analytics can be made feasible for resource-limited organizations.
  • To present a system that makes big data manageable by reducing the data volume required for analysis.

Main Methods:

  • BEAS identifies and retrieves only a small, relevant fraction of the data needed to answer a query.
  • The system employs bounded evaluation principles to manage resource constraints effectively.
  • Data-driven approximation techniques are utilized to provide accurate answers when exact computation is not feasible.

Main Results:

  • BEAS enables querying of large datasets even with limited computational resources.
  • The system can provide exact query answers when possible, ensuring data integrity.
  • When exact answers are not feasible, BEAS delivers approximate answers with guaranteed accuracy.

Conclusions:

  • BEAS democratizes big data analytics, making it accessible to small companies.
  • The system's approach of 'making big data small' is effective for resource-constrained environments.
  • Bounded evaluation and data-driven approximation are key to enabling efficient big data querying.