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Top-k dominating queries on incomplete large dataset.

Jimmy Ming-Tai Wu1, Min Wei1, Mu-En Wu2

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This study introduces an Efficient Hadoop BitMap Index Guided Algorithm (EHBIG) for top-k dominating (TKD) queries on large, incomplete datasets. The EHBIG algorithm, along with its improved version (IEHBIG), significantly enhances performance and reduces resource usage.

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Area of Science:

  • Data Mining
  • Database Systems
  • Big Data Analytics

Background:

  • Top-k dominating (TKD) queries identify objects that dominate others in a dataset.
  • Incomplete datasets with missing values pose challenges for traditional data mining.
  • Existing methods struggle with TKD queries on large-scale incomplete data.

Purpose of the Study:

  • To develop an efficient algorithm for TKD queries on large incomplete datasets.
  • To leverage MapReduce architecture for scalability and performance.
  • To reduce runtime and memory usage through a pruning strategy.

Main Methods:

  • Proposed the Efficient Hadoop BitMap Index Guided Algorithm (EHBIG) using MapReduce and BitMap Index.
  • Implemented a pruning strategy to optimize query processing.
  • Developed an improved version, IEHBIG, for enhanced algorithm flow.

Main Results:

  • EHBIG successfully performs TKD queries on incomplete large datasets.
  • The pruning strategy significantly reduces runtime and memory consumption.
  • Experimental results demonstrate strong performance in a Hadoop cluster.

Conclusions:

  • EHBIG and IEHBIG provide an effective solution for TKD queries on big incomplete data.
  • The MapReduce architecture ensures scalability for large datasets.
  • The proposed algorithms offer significant performance improvements over existing methods.