NDLI: Towards building a high performance spatial query system for large scale medical imaging data

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Towards building a high performance spatial query system for large scale medical imaging data

Content Provider	ACM Digital Library
Author	Aji, Ablimit Saltz, Joel H. Wang, Fusheng
Abstract	Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.
Starting Page	309
Ending Page	318
Page Count	10
File Format	PDF
ISBN	9781450316910
DOI	10.1145/2424321.2424361
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-11-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Data skew Spatial query processing Mapreduce Pathology imaging
Content Type	Text
Resource Type	Article

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