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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Automated Debugging in Data-Intensive Scalable Computing.

Muhammad Ali Gulzar1, Matteo Interlandi2, Xueyuan Han3

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Summary
This summary is machine-generated.

BigSift efficiently finds minimal faulty data subsets for Big Data Analytics debugging. This approach significantly enhances fault localization accuracy and performance, reducing debugging time.

Keywords:
Automated debuggingand data cleaningbig datadata provenancedata-intensive scalable computing (DISC)fault localization

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Area of Science:

  • Computer Science
  • Data Engineering

Background:

  • Big Data Analytics workloads often encounter errors due to data quality issues or incorrect assumptions.
  • Debugging these errors typically involves identifying specific input data subsets that trigger the problem.

Purpose of the Study:

  • To introduce BigSift, a novel approach for localizing faulty data in Big Data Analytics.
  • To enhance the efficiency and accuracy of debugging complex data workloads.

Main Methods:

  • BigSift combines automated fault isolation techniques from software engineering with data provenance from database systems.
  • It redefines data provenance for debugging using a test oracle function.
  • Unique optimizations are implemented for iterative debugging workloads.

Main Results:

  • BigSift achieves significant improvements in fault localizability accuracy, orders-of-magnitude higher than existing methods like Titian.
  • It demonstrates superior performance, up to 66x faster than Delta Debugging.
  • Fault-inducing data is localized within 62% of the original job running time.

Conclusions:

  • BigSift offers a highly accurate and performant solution for identifying failure-inducing inputs in Big Data Analytics.
  • The approach streamlines the debugging process for complex data workloads.