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PhD Thesis: Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies

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Slides from my PhD thesis defense presentation, about reproducibility of computational experiments

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PhD Thesis: Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies

  1. 1. Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Date: 22/01/16 Idafen Santana-Pérez Supervisors: María S. Pérez-Hernández, Oscar Corcho
  2. 2. Introduction 2Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies HYPOTHESIS CONVINCE AUDIENCE REPEATABLE SCIENTIFIC EXPERIMENTS
  3. 3. Introduction 3Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies SCIENTIFIC EXPERIMENTS IN VIVO/VITRO IN SILICO
  4. 4. Introduction 4Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies SCIENTIFIC EXPERIMENTS IN VIVO/VITRO IN SILICO REPEATABILITY
  5. 5. Terminology PRESERVATION CONSERVATION 5Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  6. 6. Terminology PRESERVATION CONSERVATION REPLICABILITY REPRODUCIBILITY 6Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  7. 7. Experiment components 7Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies DATA SCIENTIFIC PROCEDURE EQUIPMENT INVIVO/VITROINSILICO
  8. 8. Experiment components 8Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies DATA SCIENTIFIC PROCEDURE EQUIPMENT INVIVO/VITROINSILICO
  9. 9. Experiment components 9Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies DATA SCIENTIFIC PROCEDURE EQUIPMENT INSILICO
  10. 10. Experiment components 10Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies DATA SCIENTIFIC PROCEDURE EQUIPMENT INSILICO
  11. 11. Experiment components 11Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies DATA SCIENTIFIC PROCEDURE EQUIPMENT INSILICO
  12. 12. Experiment components 12Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies DATA SCIENTIFIC PROCEDURE EQUIPMENT INSILICO
  13. 13. Research Methodology 13Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies State of the Art Open Research Problems Hypothesis & Goals Evaluation
  14. 14. Open Research Problems 14Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  15. 15. Open Research Problems 15Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Computational Infrastructures are usually a predefined element of a Computational Scientific Workflow.
  16. 16. Open Research Problems 16Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Computational Infrastructures are usually a predefined element of a Computational Scientific Workflow. • Execution Environments are poorly described.
  17. 17. Open Research Problems 17Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Computational Infrastructures are usually a predefined element of a Computational Scientific Workflow. • Execution Environments are poorly described. • Current reproducibility approaches for computational experiments consider only data and procedure.
  18. 18. Outline 18Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies 1. Introduction and motivation 2. Hypothesis and goals 3. Execution environment representation 4. Experiment reproduction 5. Evaluation 6. Conclusions and future work
  19. 19. Hypothesis 19Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies It is possible to describe the main properties of the Execution Environment of a Computational Scientific Experiment and, based on this description, derive a reproduction process for generating an equivalent environment using virtualization techniques.
  20. 20. Hypothesis 20Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies It is possible to describe the main properties of the Execution Environment of a Computational Scientific Experiment and, based on this description, derive a reproduction process for generating an equivalent environment using virtualization techniques. • Hypothesis 1: Semantic technologies are expressive enough to describe the Execution Environment of a Computational Scientific Experiment.
  21. 21. Hypothesis 21Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies It is possible to describe the main properties of the Execution Environment of a Computational Scientific Experiment and, based on this description, derive a reproduction process for generating an equivalent environment using virtualization techniques. • Hypothesis 2: An algorithmic process can be developed that, based on the description of the main capabilities of an Execution Environment, is able to define an equivalent infrastructure for executing the original Computational Scientific Experiment obtaining equivalent results.
  22. 22. Hypothesis 22Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies It is possible to describe the main properties of the Execution Environment of a Computational Scientific Experiment and, based on this description, derive a reproduction process for generating an equivalent environment using virtualization techniques. • Hypothesis 3: Virtualization techniques are capable of supporting the reproduction of an Execution Environment by creating and customizing computational resources, such as Virtual Machines, that fulfil the requirements of the former experiment.
  23. 23. Goals 23Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Goal 1: Create a model able to conceptualize the set of relevant capabilities that describe a Computational Infrastructure. • Goal 2: Design a framework to provide means for populating these models, collecting information from the materials of a Computational Scientific Experiment and generating structured information. H1 H1
  24. 24. Goals 24Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Goal 3: Propose an algorithm that, based on the description of a former Computational Infrastructure, is able to define an equivalent infrastructure specification. • Goal 4: Integrate a system able to deploy virtual machines on several Virtualized Infrastructure providers, meeting a certain hardware specification and install and configure the proper software stack, based on the deployment plan specified by the aforementioned algorithms. H2 H3
  25. 25. Restrictions and assumptions 25Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Restrictions • Performance • Common software components • Web services • Data-related aspects • Assumptions • Reproducibility is more important than performance • Sc. Workflows are a widely accepted approach • Virtualization solutions are a mature technology • Equivalent environment and results
  26. 26. Outline 26Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies 1. Introduction and motivation 2. Hypothesis and goals 3. Execution environment representation 4. Experiment reproduction 5. Evaluation 6. Conclusions and future work
  27. 27. Representation 27Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies CLOUD • Describing execution environments FORMER EQUIPMENT ANNOTATE REPRODUCE SEMANTIC ANNOTATIONS EQUIVALENT EXECUTION ENVIRONMENT
  28. 28. Representation • Semantic models for describing the main aspects related to the execution of a workflow. • Workflow • Software • Hardware • Computational resources • Increasing the understanding of the underlying components • Making this knowledge explicit • Easy to extend and integrate • NeOn methodology • Scenario-based methodology for building ontologies • Standard technology: RDF & OWL 28Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  29. 29. Representation • WICUS ontology network • Workflow Infrastructure Conservation Using Semantics • http://purl.org/net/wicus • 5 ontologies • WICUS Workflow Execution Requirements ontology • WICUS Software Stack ontology • WICUS Hardware Specs ontology • WICUS Scientific Virtual Appliance ontology • WICUS Ontology: links the previous ontologies 29Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  30. 30. WICUS ontology network • WICUS Workflow Execution Requirements ontology • http://purl.org/net/wicus-reqs 30Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  31. 31. WICUS ontology network • WICUS Software Stack ontology • http://purl.org/net/wicus-stack 31Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  32. 32. WICUS ontology network • WICUS Scientific Virtual Appliance ontology • http://purl.org/net/wicus-sva 32Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  33. 33. WICUS ontology network • WICUS Hardware Specs ontology • http://purl.org/net/wicus-hwspecs 33Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  34. 34. WICUS ontology network • WICUS ontology network • http://purl.org/net/wicus 34Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  35. 35. WICUS ontology network • WICUS ontology network • http://purl.org/net/wicus 35Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  36. 36. Outline 36Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies 1. Introduction and motivation 2. Hypothesis and goals 3. Execution environment representation 4. Experiment reproduction A. Parsing tools and semantic annotations B. Specification process C. Enactment and execution 5. Evaluation 6. Conclusions and future work
  37. 37. WICUS system • Overview, inputs and outputs 37Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  38. 38. Outline 38Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies 1. Introduction and motivation 2. Hypothesis and goals 3. Execution environment representation 4. Experiment reproduction A. Parsing tools and semantic annotations B. Specification process C. Enactment and execution 5. Evaluation 6. Conclusions and future work
  39. 39. Parsing tools and semantic annotations 39Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  40. 40. Parsing tools and semantic annotations 40Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  41. 41. Parsing tools and semantic annotations • Workflow Specification File 41Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  42. 42. Parsing tools and semantic annotations • Workflow Parser and Annotator • Workflow Annotations 42Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  43. 43. Parsing tools and semantic annotations • WMS Annotations 43Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  44. 44. Parsing tools and semantic annotations • Software Components Registry 44Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  45. 45. Parsing tools and semantic annotations • Software Components Annotator • Software Components Catalog 45Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  46. 46. Parsing tools and semantic annotations • Software Components Annotator • Software Components Catalog • Workflow & Configuration Annotations 46Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  47. 47. Parsing tools and semantic annotations • Scientific Virtual Appliance Catalog 47Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  48. 48. Outline 48Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies 1. Introduction and motivation 2. Hypothesis and goals 3. Execution environment representation 4. Experiment reproduction A. Parsing tools and semantic annotations B. Specification process C. Enactment and execution 5. Evaluation 6. Conclusions and future work
  49. 49. Specification process 49Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  50. 50. Specification process • Infrastructure Specification Algorithm (ISA) 50Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies GET WF REQUIREMENTS GET <REQ,STACKS> GET <REQ,D-GRAPH> GET AVAILABLE SVA GET <SVA,STACKS> CALCULATE REQ-SVA COMPATIBILITY GET MAX COMPATIBLE REQ-SVA CLEAN REQ D-GRAPH
  51. 51. Infrastructure Specification Algorithm 51Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  52. 52. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 52Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  53. 53. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S15 53Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  54. 54. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S15 54Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  55. 55. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S13 S15 S15 S14 S14 55Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  56. 56. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S13 S15 S15 S14 S14 56Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  57. 57. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S13 S15 S15 S14 S14 57Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  58. 58. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S13 S15 S15 S14 S14 58Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  59. 59. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S13 S15S15 S14 S15 59Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  60. 60. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S13 S15S15 S14 S15 60Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  61. 61. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S15 S13 S14 S13 S15S15 S14 S15 61Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  62. 62. Infrastructure Specification Algorithm S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S15 S14 S15 62Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  63. 63. Specification process • Abstract Deployment Plan • Provider-independent representation format • Based on the WICUS stack ontology 63Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  64. 64. Outline 64Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies 1. Introduction and motivation 2. Hypothesis and goals 3. Execution environment representation 4. Experiment reproduction A. Parsing tools and semantic annotations B. Specification process C. Enactment and execution 5. Evaluation 6. Conclusions and future work
  65. 65. Enactment and Execution 65Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  66. 66. Enactment and Execution • PRECIP • Pegasus Repeatable Experiments for the Cloud in Python (PRECIP) • API for running experiments in Clouds • OpenStack and AWS EC2 API • Running remote commands and file transfer • No pre-installed components in the VM images 66Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  67. 67. Enactment and Execution 67Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Vagrant • Local virtualization • Virtualization tools • VirtualBox • VMWare • Vagrantfiles • Shared folder
  68. 68. Summary 68Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  69. 69. Outline 69Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies 1. Introduction and motivation 2. Hypothesis and goals 3. Execution environment representation 4. Experiment reproduction 5. Evaluation 6. Conclusions and future work
  70. 70. Evaluation • Workflows reproduced 70Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  71. 71. Evaluation • Workflows reproduced • 3 scientific domains 71Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST
  72. 72. Evaluation • Workflows reproduced • 3 scientific domains • 3 workflow management systems 72Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST
  73. 73. Evaluation • Workflows reproduced • 3 scientific domains • 3 workflow management systems • 6 different workflows 73Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST (2003) (2014)(2014) (2015) (2011)(2011)
  74. 74. Evaluation • Experimental setup 74Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Public Cloud provider • De facto standard • Academic Cloud facility • OpenStack Havana • India server • 1024 cores • 3072 GB RM • Local virtualization solution • VirtualBox • Ubuntu 12.04.5 • 4 cores, at 2 GHz • 8 Gb RAM PRECIP VAGRANT
  75. 75. Evaluation • Experimental setup 75Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  76. 76. Evaluation 76Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST Results FORMER EQUIPMENT ANNOTATE REPRODUCE CLOUD EQUIVALENT EXECUTION ENVIRONMENT SEMANTIC ANNOTATIONS COMPARE
  77. 77. Evaluation 77Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST Results CLOUD FORMER EQUIPMENT ANNOTATE REPRODUCE SEMANTIC ANNOTATIONS EQUIVALENT EXECUTION ENVIRONMENT COMPARE
  78. 78. Evaluation 78Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST Results CLOUD FORMER EQUIPMENT ANNOTATE REPRODUCE SEMANTIC ANNOTATIONS EQUIVALENT EXECUTION ENVIRONMENT COMPARE • Non-deterministic • Standard and error output • Generated files equivalent
  79. 79. Evaluation 79Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST Results CLOUD FORMER EQUIPMENT ANNOTATE REPRODUCE SEMANTIC ANNOTATIONS EQUIVALENT EXECUTION ENVIRONMENT COMPARE • Same results • Results from Int. Extinction may vary
  80. 80. Evaluation 80Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST Results CLOUD FORMER EQUIPMENT ANNOTATE REPRODUCE SEMANTIC ANNOTATIONS EQUIVALENT EXECUTION ENVIRONMENT COMPARE • Genomic data • Exact match
  81. 81. Evaluation 81Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Domain Seismic Astronomy Bio WMS dispel4py Pegasus Makeflow Name xcorr Internal Extinction Montage Epigenomics SoyKB BLAST Results CLOUD FORMER EQUIPMENT ANNOTATE REPRODUCE SEMANTIC ANNOTATIONS EQUIVALENT EXECUTION ENVIRONMENT COMPARE
  82. 82. Outline 82Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies 1. Introduction and motivation 2. Hypothesis and goals 3. Execution environment representation 4. Experiment reproduction 5. Evaluation 6. Conclusions and future work
  83. 83. Conclusions 83Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Hypothesis 1: Semantic technologies are expressive enough to describe the Execution Environment of a Computational Scientific Experiment. • Goal 1 • WICUS ontology network • Goal 2 • Parsing and annotations modules
  84. 84. Conclusions 84Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Hypothesis 2: An algorithmic process can be developed that, based on the description of the main capabilities of an Execution Environment, is able to define an equivalent infrastructure for executing the original Computational Scientific Experiment obtaining equivalent results • Goal 3 • Infrastructure Specification Algorithm • Abstract Deployment Plan
  85. 85. Conclusions 85Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Hypothesis 3: Virtualization techniques are capable of supporting the reproduction of an Execution Environment by creating and customizing computational resources, such as Virtual Machines, that fulfil the requirements of the former experiment. • Goal 4 • Script Generator for PRECIP and Vagrant scripts • AWS EC2, FutureGrid, and Vagrant
  86. 86. Conclusions • Other approaches • Sharing VM • Exhaustive trace of the execution components • Semantic description for business processes 86Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  87. 87. Dissemination 87Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Journals • Idafen Santana-Perez, Rafael Ferreira da Silva, Mats Rynge, Ewa Deelman, María S. Pérez-Hernández, Oscar Corcho, “Reproducibility of execution environments in computational science using Semantics and Clouds”, Future Generation Computer Systems, Available online 8 January 2016, ISSN 0167-739X, http://dx.doi.org/10.1016/j.future.2015.12.017 (impact factor: 2.786) • Santana-Perez, Idafen and Pérez-Hernández, María , “Towards Reproducibility in Scientific Workflows: An Infrastructure-Based Approach” Scientific Programming, vol. 2015, Article ID 243180, 11 pages, 2015. doi:10.1155/2015/243180 (impact factor: 0.559)
  88. 88. Dissemination 88Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Conferences & workshops • Doug James, et. al. (including Santana-Perez, Idafen),“Standing Together for Reproducibility in Large-Scale Computing: Report on reproducibility@XSEDE” reproducibility@XSEDE workshop, 2014. • Santana-Perez, Idafen, Ferreira da Silva, Rafael, Rynge, Mats, Deelman, Ewa, Pérez-Henández, María, Corcho, Oscar , “A Semantic- Based Approach to Attain Reproducibility of Computational Environments in Scientific Workflows: A Case Study” 1st International Workshop on Reproducibility in Parallel Computing (REPPAR14) in conjunction with Euro-Par 2014 (August 25-29), Porto, Portugal. • Santana-Perez, Idafen and Pérez-Hernández, María.; , “A Semantic Scheduler Architecture for Federated Hybrid Clouds” Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on , vol., no., pp.384- 391, 24-29 June 2012.
  89. 89. Future work 89Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
  90. 90. Future work 90Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Incentives for scientists to produce reproducible results • Define roles and responsibilities • Infrastructure management plan
  91. 91. Future work 91Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Incentives for scientists to produce reproducible results • Define roles and responsibilities • Infrastructure management plan • Publish descriptions as Linked Data • Linking it with other resources describing scientific workflows
  92. 92. Future work 92Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Incentives for scientists to produce reproducible results • Define roles and responsibilities • Infrastructure management plan • Publish descriptions as Linked Data • Linking it with other resources describing scientific workflows • Multi-node infrastructures
  93. 93. Future work 93Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies • Incentives for scientists to produce reproducible results • Define roles and responsibilities • Infrastructure management plan • Publish descriptions as Linked Data • Linking it with other resources describing scientific workflows • Multi-node infrastructures • Completeness of annotations
  94. 94. Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies Idafen Santana-Pérez Supervisors: María S. Pérez-Hernández, Oscar Corcho Date: 22/01/16 Experimental materials available online: http://w3id.org/idafensp/ro/wicuspegasusmontage http://w3id.org/idafensp/ro/wicuspegasusepigenomics http://w3id.org/idafensp/ro/wicuspegasussoykb http://w3id.org/idafensp/ro/wicusdispel4pyastro http://w3id.org/idafensp/ro/wicusdispel4pyxcorr http://w3id.org/idafensp/ro/wicusmakeflowblast

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