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How Hadoop and Spark benchmarking algorithms can improve remote health monitoring and data management platforms?

How Hadoop and Spark benchmarking algorithms can improve remote health monitoring and data management platforms?

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This chapter introduces the characteristics of e-care platform and the concept of ontology which helps the reader understand the system that will implement big data tools for its migration while also focusing on focuses on the most popular systems in the Hadoop ecosystem, emphasizing MapReduce and Spark.

Chapter Contents:

  • 12.1 Introduction
  • 12.2 E-care platform
  • 12.2.1 Security and privacy challenges for healthcare applications
  • 12.2.2 Problematic of E-care
  • 12.3 Big data
  • 12.4 Hadoop ecosystem
  • 12.4.1 MapReduce
  • 12.4.2 Spark
  • 12.4.3 Other tools
  • 12.5 Computational techniques
  • 12.5.1 Machine learning techniques in medical field
  • 12.5.2 Spark with machine learning techniques in medical field
  • 12.6 Benchmarking
  • 12.6.1 Benchmarking and big data
  • 12.6.2 Types of benchmarking
  • 12.7 Benchmarks in Hadoop and Spark
  • 12.7.1 Amp Lab Benchmark
  • 12.7.2 BigBench
  • 12.7.3 BigDataBench
  • 12.7.4 BigFrame
  • 12.7.5 GridMix
  • 12.7.6 HiBench
  • 12.7.7 PigMix
  • 12.7.8 SparkBench
  • 12.7.9 Statistical Workload Injector for MapReduce
  • 12.8 Benchmark comparison
  • 12.9 Proposal
  • 12.10 Conclusion
  • References

Inspec keywords: health care; Big Data; medical administrative data processing; parallel processing; ontologies (artificial intelligence); cluster computing

Other keywords: MapReduce; Big Data tools; remote health monitoring; Hadoop; ontology; data management platforms; Spark benchmarking algorithms

Subjects: Parallel software; Medical administration; Data handling techniques; Knowledge engineering techniques

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