4.5 Article

Making big data small

出版社

ROYAL SOC
DOI: 10.1098/rspa.2019.0034

关键词

big data; database systems; structured query language; bounded evaluation; approximate query answering

资金

  1. ERC [652976]
  2. Royal Society Wolfson Research Merit Award [WRM/R1/180014]
  3. EPSRC [EP/M025268/1]
  4. Foundation for Innovative Research Groups of NSFC
  5. NSFC [61421003]
  6. Beijing Advanced Innovation Centre for Big Data and Brain Computing
  7. Shenzhen Institute of Computing Sciences
  8. Joint Lab between Edinburgh and Huawei
  9. EPSRC [EP/M025268/1] Funding Source: UKRI

向作者/读者索取更多资源

Big data analytics is often prohibitively costly and is typically conducted by parallel processing with a cluster of machines. Is big data analytics beyond the reach of small companies that can only afford limited resources? This paper tackles this question by presenting Boundedly EvAlable SQL (BEAS), a system for querying big relations with constrained resources. The idea is to make big data small. To answer a query posed on a dataset, it often suffices to access a small fraction of the data no matter how big the dataset is. In the light of this, BEAS answers queries on big data by identifying and fetching a small set of the data needed. Under available resources, it computes exact answers whenever possible and otherwise approximate answers with accuracy guarantees. Underlying BEAS are principled approaches of bounded evaluation and data-driven approximation, the focus of this paper.

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