Scientific discoveries are increasingly driven by analyzing large volumes of image data. Many new libraries and specialized database management systems (DBMSs) have emerged to support such tasks. It is unclear, however, how well these systems support real-world image analysis use cases, and how performant are the image analytics tasks implemented on top of such systems. In this paper, we present the first comprehensive evaluation of large-scale image analysis systems using two real-world scientific image data processing use cases. We evaluate five representative systems (SciDB, Myria, Spark, Dask, and TensorFlow) and find that each of them has shortcomings that complicate implementation or hurt performance. Such shortcomings lead to new research opportunities in making large-scale image analysis both efficient and easy to use.
@article{arxiv.1612.02485,
title = {Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads},
author = {Parmita Mehta and Sven Dorkenwald and Dongfang Zhao and Tomer Kaftan and Alvin Cheung and Magdalena Balazinska and Ariel Rokem and Andrew Connolly and Jacob Vanderplas and Yusra AlSayyad},
journal= {arXiv preprint arXiv:1612.02485},
year = {2016}
}