In:
Proceedings of the VLDB Endowment, Association for Computing Machinery (ACM), Vol. 10, No. 11 ( 2017-08), p. 1226-1237
Abstract:
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 how well these systems support real-world image analysis use cases, and how performant the image analytics tasks implemented on top of such systems are. 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.
Type of Medium:
Online Resource
ISSN:
2150-8097
DOI:
10.14778/3137628.3137634
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2017
detail.hit.zdb_id:
2478691-3