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Verifying Properties of MapReduce-Based Big Data Processing
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DOI：10.1109/TR.2020.2999441.png)
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Abstract
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Big data techniques are widely used in various fields. To deal with large data sets efficiently, a new programming framework MapReduce has emerged. Thus, new verification challenges arise to improve the reliability of big data processing. In this article, MapReduce processes are implemented by modeling simulation and verification language programs. Then, several data properties such as data soundness, nonconflict, nonduplication, cooperation, and completeness are taken into account. Moreover, these properties are specified by propositional projection temporal logic formulas. To verify these properties, a runtime verification approach at code level based on unified model checking is employed. In addition, two case studies are conducted to demonstrate our approach: sparse matrix multiplication and tracking down suspected patients of an infectious disease.
Keywords:
Big Data
Reliability
Sparse matrices
Infectious diseases
Storms
Urban areas
Big data
formal verification
MapReduce
model checking
reliability
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