Optimizing DICOM data management with NSGA-G

Abstract : Cloud-based systems enable to manage ever-increasing medical data. The Digital Imaging and Communication in Medicine (DI-COM) standard has been widely accepted to store and transfer the medical data, which uses single (row/column) or hybrid data storage technique (row-column). In particular, hybrid systems leverage the advantages of both techniques and allow to take into account various kinds of queries from full records retrieval (online transaction processing) to analytics (online analytical processing) queries. Additionally, the pay-as-you-go model and elasticity of cloud computing raise an important issue regarding to Multiple Objective Optimization (MOO) to find a data configuration according to users preferences such as storage space, processing response time, monetary cost, quality, etc. In such a context, the considerable space of solutions in MOO leads to generation of Pareto-optimal front with high complexity. Pareto-dominated based Multiple Objective Evolutionary Algorithms are often used as an alternative solution, e.g., Non-dominated Sorting Genetic Algorithms (NSGA) which provide less computational complexity. This paper presents NSGA-G, an NSGA based on Grid Partitioning to improve the complexity and quality of current NSGAs and to obtain efficient storage and querying of DICOM hybrid data. Experimental results on DTLZ test problems [10] and DICOM hybrid data prove the relevance of the proposed algorithm.
Document type :
Conference papers
Complete list of metadatas

Cited literature [41 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02285736
Contributor : Laurent d'Orazio <>
Submitted on : Friday, September 13, 2019 - 9:17:03 AM
Last modification on : Sunday, September 15, 2019 - 1:17:50 AM

File

Paper21-TLe.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02285736, version 1

Citation

Trung-Dung Le, Verena Kantere, Laurent Orazio. Optimizing DICOM data management with NSGA-G. International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, Mar 2019, Lisbon, Portugal. ⟨hal-02285736⟩

Share

Metrics

Record views

30

Files downloads

21