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Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

Ablimit Aji1, Fusheng Wang2, Joel H Saltz3

  • 1Department of Mathematics & Computer Science, Emory University.

Proceedings of the ... ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems : ACM GIS. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
|February 7, 2014
PubMed
Summary

This study introduces a high-performance spatial query system for massive scientific data on MapReduce. It enables efficient querying of large-scale pathology images for computer-aided diagnosis, addressing big data challenges.

Keywords:
Data SkewDesignExperimentationManagementMapReducePathology ImagingPerformanceSpatial Query Processing

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Area of Science:

  • Geoinformatics
  • Digital Pathology
  • Computational Biology

Background:

  • High-performance spatial queries are crucial for large scientific datasets, driven by fields like digital pathology.
  • Digital pathology generates vast amounts of spatial data from high-resolution tissue images, requiring efficient analysis.
  • Analyzing this data presents "big data" and high computational complexity challenges for effective querying.

Purpose of the Study:

  • To develop a high-performance spatial query system for massive spatial data using the MapReduce framework.
  • To enable effective querying of large-scale pathology images for computer-aided diagnosis and scientific analysis.
  • To address the challenges of big data and computational complexity in scientific spatial data analysis.

Main Methods:

  • An on-demand index building approach for processing spatial queries.
  • A partition-merge approach for parallel spatial query pipelines within the MapReduce model.
  • Cost-based query optimization to mitigate data skew and reduce response times.

Main Results:

  • Demonstrated support for multi-way spatial joins for algorithm evaluation.
  • Enabled efficient nearest neighbor queries for micro-anatomic objects in pathology images.
  • Showcased the framework's ability to efficiently handle complex analytical spatial queries on MapReduce.

Conclusions:

  • The developed framework efficiently supports complex analytical spatial queries on massive datasets using MapReduce.
  • The system effectively addresses the "big data" and computational complexity challenges in scientific spatial data analysis.
  • This work advances the potential of image-based computer-aided diagnosis through high-performance spatial querying.