期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2012
卷号：35
期号：3
出版社：IEEE Computer Society
摘要：Biological image databases have quickly replaced the personal media collections of individual scien-tists. Such databases permit objective comparisons, benchmarking, and data-driven science. As thesecollections have grown using advanced (and automated) imaging tools and microscopes, scientists needhigh-throughput large-scale statistical analysis of the data.Traditional databases and standalone analysis tools are not suited for image-based scientific en-deavors due to subjectivity, non-uniformity and uncertainty of the primary data and their analyses. Thispaper describes our image-database platform Bisque, which combines .exible data structuring, uncer-tain data management and high-throughput analysis. In particular, we examine: (i) Management ofscientific images and metadata for experimental science where the data model may change from experi-ment to experiment; (ii) Providing easy provisioning for high-throughput and large-scale image analysisusing cluster/cloud resources; (iii) Strategies for managing uncertainty in measurement and analysis sothat important aspects of the data are not prematurely filtered
