Challenges and approaches for distributed workflow-driven analysis of large-scale biological data: vision paper
dc.contributor.author | Altintas, Ilkay | |
dc.contributor.author | Wang, Jianwu | |
dc.contributor.author | Crawl, Daniel | |
dc.contributor.author | Li, Weizhong | |
dc.date.accessioned | 2024-02-19T15:02:50Z | |
dc.date.available | 2024-02-19T15:02:50Z | |
dc.date.issued | 2012-03-30 | |
dc.description | ICDT '12: 15th International Conference on Database Theory Berlin Germany 30 March 2012 | |
dc.description.abstract | Next-generation DNA sequencing machines are generating a very large amount of sequence data with applications in many scientific challenges and placing unprecedented demands on traditional single-processor bioinformatics algorithms. Middleware and technologies for scientific workflows and data-intensive computing promise new capabilities to enable rapid analysis of next-generation sequence data. Based on this motivation and our previous experiences in bioinformatics and distributed scientific workflows, we are creating a Kepler Scientific Workflow System module, called "bioKepler", that facilitates the development of Kepler workflows for integrated execution of bioinformatics applications in distributed environments. This vision paper discusses the challenges related to next-generation sequencing data, explains the approaches taken in bioKepler to help with analysis of such data, and presents preliminary results demonstrating these approaches. | |
dc.description.sponsorship | The authors would like to thank the rest of Kepler and CAMERA teams for their collaboration. This work was supported by NSF SDCI Award OCI-0722079 for Kepler/CORE, NSF ABI Award DBI-1062565 for bioKepler, the Gordon and Betty Moore Foundation award to Calit2 at UCSD for CAMERA, and an SDSC Triton Research Opportunities grant. | |
dc.description.uri | https://dl.acm.org/doi/10.1145/2320765.2320791 | |
dc.format.extent | 6 pages | |
dc.genre | conference papers and proceedings | |
dc.identifier | doi:10.13016/m2x94i-x9d6 | |
dc.identifier.citation | Altintas, Ilkay, Jianwu Wang, Daniel Crawl, and Weizhong Li. “Challenges and Approaches for Distributed Workflow-Driven Analysis of Large-Scale Biological Data: Vision Paper.” In Proceedings of the 2012 Joint EDBT/ICDT Workshops, 73–78. EDBT-ICDT ’12. New York, NY, USA: Association for Computing Machinery, 2012. https://doi.org/10.1145/2320765.2320791. | |
dc.identifier.uri | https://doi.org/10.1145/2320765.2320791 | |
dc.identifier.uri | http://hdl.handle.net/11603/31654 | |
dc.language.iso | en_US | |
dc.publisher | ACM | |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Information Systems Department Collection | |
dc.relation.ispartof | UMBC Center for Accelerated Real Time Analysis | |
dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department | |
dc.relation.ispartof | UMBC Data Science | |
dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology (JCET) | |
dc.relation.ispartof | UMBC Center for Real-time Distributed Sensing and Autonomy | |
dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
dc.subject | scientific workflows | |
dc.subject | data-parallel patterns | |
dc.subject | bioinformatics | |
dc.subject | next- generation sequence analysis | |
dc.subject | UMBC Big Data Analytics Lab | |
dc.title | Challenges and approaches for distributed workflow-driven analysis of large-scale biological data: vision paper | |
dc.type | Text | |
dcterms.creator | https://orcid.org/0000-0002-9933-1170 |
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