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Computing

, Volume 100, Issue 5, pp 439–472 | Cite as

Mechanisms for provenance collection in scientific workflow systems

  • Mehdi SarikhaniEmail author
  • Andrew Wendelborn
Article

Abstract

Scientific workflow management systems run scientific experiments. They manage sequences of complex process transformations and collect provenance information at various levels of abstraction. Collected provenance information from scientific experiments documents how experimental results are derived from input values along with experimental parameters and workflow configurations. Provenance greatly enhances usability and acceptance of workflow systems among scientists, because provenance allows workflow systems to capture process configuration and behaviour at different levels of detail. On this basis, a sufficient level of collected provenance information enables scientists to validate their hypotheses and make a workflow reproducible. Currently SWfMS’s do not use a standard or portable provenance model for either capturing, storing, querying or representing model. There are a variety of design issues in provenance models and mechanisms in workflow system, owing to the variation of design dimensions in workflow architectures. Given this variety, it seems desirable to classify provenance mechanisms in workflow systems. We aim to survey provenance collection mechanisms, that are either a part of scientific workflow system, or of a software infrastructure that supports collection mechanisms in a scientific workflow system. In this paper, firstly, we identify and define a set of design dimensions and conventions for provenance collection mechanisms in the context of working on scientific workflow systems. After this, we survey a set of scientific workflow projects based on our design dimensions with an emphasis on provenance collection mechanisms. Then, those conventions are used in order to evaluate a number of existing provenance collection mechanisms, presented at the end of this paper. This survey provides an understanding of primary design issues for provenance collection mechanisms along with a set of desirable design dimensions.

Keywords

Provenance Workflow systems Provenance collection mechanism Scientific computing 

Mathematics Subject Classification

68-02 

References

  1. 1.
    Lin C, Lu S, Fei X, Chebotko A, Pai D, Lai Z, Fotouhi F, Hua J (2009) A reference architecture for scientific workflow management systems and the VIEW SOA solution. IEEE Trans Serv Comput 2(1):79–92.  https://doi.org/10.1109/TSC.2009.4 CrossRefGoogle Scholar
  2. 2.
    Ranno F, Shrivastava S (1999) A review of distributed workflow management systems. In: The international joint conference on Work activities coordination and collaboration (WACC99), San Francisco, CaliforniaGoogle Scholar
  3. 3.
    Wu Q, Zhu M, Gu Y, Brown P, Lu X, Lin W, Liu Y (2012) A distributed workflow management system with case study of real-life scientific applications on Grids. J Grid Comput 10(3):367–393.  https://doi.org/10.1007/s10723-012-9222-7 CrossRefGoogle Scholar
  4. 4.
    Miller JA, Sheth AP, Kochut KJ, Wang X (1996) CORBA-based run-time architectures for workflow management systems. J Database Manag (JDM) 7(1):16–27.  https://doi.org/10.4018/jdm.1996010102 CrossRefGoogle Scholar
  5. 5.
    Li H, Yang Y, Shi M (2003) Key issues and experiences in development of distributed workflow management systems. In: Zhou X, Orlowska M, Zhang Y (eds) Web technologies and applications, vol 2642. Lecture notes in computer science. Springer, Berlin, pp 507–512.  https://doi.org/10.1007/3-540-36901-5_51
  6. 6.
    Görlach K, Sonntag M, Karastoyanova D, Leymann F, Reiter M (2011) Conventional workflow technology for scientific simulation. In: Yang X, Wang L, Jie W (eds) Guide to e-science. Computer communications and networks. Springer, London, pp 323–352.  https://doi.org/10.1007/978-0-85729-439-5_12
  7. 7.
    Hahn C, Horn S, Jablonski S, Lay R, Neeb J, Schamburger R, Schlundt M Taxonomy of distribution concepts for workflow management. University Erlangen-NürnbergGoogle Scholar
  8. 8.
    Simmhan YL, Plale B, Gannon D (2005) A survey of data provenance techniques, vol 47405. Indiana University, BloomingtonGoogle Scholar
  9. 9.
    Freire J, Koop D, Santos E, Silva CT (2008) Provenance for computational tasks: a survey. Comput Sci Eng 10(3):11–21.  https://doi.org/10.1109/MCSE.2008.79 CrossRefGoogle Scholar
  10. 10.
    Lee B, Awad A, Awad M (2015) Towards secure provenance in the cloud: a survey. In: 2015 IEEE/ACM 8th international conference on utility and cloud computing (UCC), 7–10 Dec 2015, pp 577-582.  https://doi.org/10.1109/UCC.2015.102
  11. 11.
    Tao L, Ling L, Xiaolong Z, Kai X, Chao Y (2014) ProvenanceLens: service provenance management in the cloud. In: 2014 international conference on collaborative computing: networking, applications and worksharing (CollaborateCom), 22–25 Oct 2014, pp 275-284Google Scholar
  12. 12.
    Chen P, Plale BA (2015) Big data provenance analysis and visualization. In: 15th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid), 4–7 May 2015, pp 797-800.  https://doi.org/10.1109/CCGrid.2015.85
  13. 13.
    Agrawal R, Imran A, Seay C, Walker J (2014) A layer based architecture for provenance in big data. In: IEEE international conference on big data (Big Data), 27–30 Oct. 2014, pp 1-7.  https://doi.org/10.1109/BigData.2014.7004468
  14. 14.
    Tan YS, Ko RKL, Holmes G (2013) Security and data accountability in distributed systems: a provenance survey. In: 2013 IEEE 10th international conference on high performance computing and communications & 2013 IEEE international conference on embedded and ubiquitous computing (HPCC_EUC), 13–15 Nov 2013, pp 1571–1578.  https://doi.org/10.1109/HPCC.and.EUC.2013.221
  15. 15.
    Moreau L, Kwasnikowska N, Van den Bussche J (2009) The foundations of the open provenance model. http://eprints.soton.ac.uk/id/eprint/267282
  16. 16.
    Davidson SB, Boulakia SC, Eyal A, Ludäscher B, McPhillips TM, Bowers S, Anand MK, Freire J (2007) Provenance in scientific workflow systems. IEEE Data Eng Bull 30(4):44–50Google Scholar
  17. 17.
    Amsterdamer Y, Davidson SB, Deutch D, Milo T, Stoyanovich J, Tannen V (2011) Putting lipstick on pig: enabling database-style workflow provenance. Very Large Data Base (VLDB) Endow 5(4):346–357Google Scholar
  18. 18.
    Bowers S (2012) Scientific workflow, provenance, and data modeling challenges and approaches. J Data Sem 1(1):19–30.  https://doi.org/10.1007/s13740-012-0004-y CrossRefGoogle Scholar
  19. 19.
    Stamatogiannakis M, Groth P, Bos H (2015) Looking inside the black-box: capturing data provenance using dynamic instrumentation. In: Ludäscher B, Plale B (eds) Provenance and annotation of data and processes, vol 8628. Lecture notes in computer science. Springer, Switzerland, pp 155–167.  https://doi.org/10.1007/978-3-319-16462-5_12
  20. 20.
    Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report Ver. 2:3Google Scholar
  21. 21.
    Keele S (2007) Guidelines for performing systematic literature reviews in software engineering. In: Technical report, Ver. 2.3 EBSE Technical Report. EBSEGoogle Scholar
  22. 22.
    Daneva M, Damian D, Marchetto A, Pastor O (2014) Empirical research methodologies and studies in requirements engineering: how far did we come? J Syst Softw 95:1–9.  https://doi.org/10.1016/j.jss.2014.06.035 CrossRefGoogle Scholar
  23. 23.
    Kitchenham B, Pretorius R, Budgen D, Pearl Brereton O, Turner M, Niazi M, Linkman S (2010) Systematic literature reviews in software engineering: a tertiary study. Inf Softw Technol 52(8):792–805.  https://doi.org/10.1016/j.infsof.2010.03.006 CrossRefGoogle Scholar
  24. 24.
    Burnham JF (2006) Scopus database: a review. Biomed Digit Libr 3(1):1CrossRefGoogle Scholar
  25. 25.
    Zahedi M, Shahin M, Babar MA (2015) A systematic review of knowledge sharing challenges and practices in global software development. Int J Inf Manag (submiited to)Google Scholar
  26. 26.
    Shahin M, Liang P, Babar MA (2014) A systematic review of software architecture visualization techniques. J Syst Softw 94:161–185.  https://doi.org/10.1016/j.jss.2014.03.071 CrossRefGoogle Scholar
  27. 27.
    Zhang H, Babar MA, Tell P (2011) Identifying relevant studies in software engineering. Inf Softw Technol 53(6):625–637.  https://doi.org/10.1016/j.infsof.2010.12.010 CrossRefGoogle Scholar
  28. 28.
    Chen L, Babar MA, Zhang H (2010) Towards an evidence-based understanding of electronic data sources. Paper presented at the. Proceedings of the 14th international conference on evaluation and assessment in software engineering, UKGoogle Scholar
  29. 29.
    Scheidegger C, Koop D, Santos E, Vo H, Callahan S, Freire J, Silva C (2008) Tackling the provenance challenge one layer at a time. Concurr Comput Pract Exp 20(5):473–483.  https://doi.org/10.1002/cpe.1237 CrossRefGoogle Scholar
  30. 30.
    Moreau L, Clifford B, Freire J, Futrelle J, Gil Y, Groth P, Kwasnikowska N, Miles S, Missier P, Myers J, Plale B, Simmhan Y, Stephan E, den Bussche JV (2011) The open provenance model core specification (v1.1). Future Gener Comput Syst 27(6):743–756.  https://doi.org/10.1016/J.Future.2010.07.005 CrossRefGoogle Scholar
  31. 31.
    Groth P, Miles S, Missier P, Moreau L (2009) A proposal for handling collections in the open provenance model. http://mailman.ecs.soton.ac.uk/pipermail/provenance-challenge-ipaw-info/attachments/20090605/85b3e182/attachment-0001.pdf
  32. 32.
    Groth P, Moreau L (2013) PROV-Overview. W3C. http://www.w3.org/TR/prov-overview/. Accessed 30 April 2013
  33. 33.
    Garijo D, Gil Y (2012) The OPMW ontology. http://www.opmw.org/model/OPMW_20121009/
  34. 34.
    Simmhan Y, Groth P, Moreau L (2011) Special section: the third provenance challenge on using the open provenance model for interoperability. Future Gener Comput Syst 27(6):737–742CrossRefGoogle Scholar
  35. 35.
    Crawl D, Wang J, Altintas I (2011) Provenance for MapReduce-based data-intensive workflows. In: 6th workshop on workflows in support of large-scale science, Seattle, Washington, ACM, pp 21–30Google Scholar
  36. 36.
    Cruz SMS, Paulino CE, Oliveira Dd, Campos MLM, Mattoso M (2011) Capturing distributed provenance metadata from cloud-based scientific workflows. J Inf Data Manag 2(1):43–50Google Scholar
  37. 37.
    Muniswamy-Reddy K-K, Macko P, Seltzer MI (2010) Provenance for the cloud. In: the 8th USENIX conference on file and storage technologies, San Jose, California, USENIX Association, 1855526, pp 15–14Google Scholar
  38. 38.
    Muniswamy-Reddy K-K, Macko P, Seltzer MI (2009) Making a cloud provenance-aware. In: Workshop on the theory and practice of provenance, San Francisco, California, USENIX AssociationGoogle Scholar
  39. 39.
    Marinho A, Murta L, Werner C, Braganholo V, Cruz SMS, Ogasawara E, Mattoso M (2012) ProvManager: a provenance management system for scientific workflows. Concurr Comput Pract Exp 24(13):1513–1530.  https://doi.org/10.1002/cpe.1870 CrossRefGoogle Scholar
  40. 40.
    Chapman A, Blaustein BT, Seligman L, Allen MD (2011) PLUS: a provenance manager for integrated information. In: IEEE international conference on information reuse and integration (IRI), 3–5 August 2011, pp 269–275.  https://doi.org/10.1109/IRI.2011.6009558
  41. 41.
    Buchert T, Nussbaum L, Gustedt J (2015) Towards complete tracking of provenance in experimental distributed systems research. In: Hunold S, Costan A, Giménez D et al (eds) Euro-Par 2015: parallel processing workshops: Euro-Par 2015 international workshops, Vienna, Austria, 24–25 August 2015, Revised Selected Papers. Springer, Cham, pp 604-616.  https://doi.org/10.1007/978-3-319-27308-2_49
  42. 42.
    Carata L, Akoush S, Balakrishnan N, Bytheway T, Sohan R, Seltzer M, Hopper A (2014) A primer on provenance. Commun ACM 57(5):52–60.  https://doi.org/10.1145/2596628 CrossRefGoogle Scholar
  43. 43.
    Cruz SMS, Campos MLM, Mattoso M (2009) Towards a taxonomy of provenance in scientific workflow management systems. In: IEEE congress on services, Los Angeles, California, 6–10 July 2009. IEEE, pp 259–266.  https://doi.org/10.1109/SERVICES-I.2009.18
  44. 44.
    Glavic B, Dittrich KR (2007) Data provenance: a categorization of existing approaches. In: Conference on Datenbanksysteme in Buisness, Technologie und Web (BTW), Aachen, Germany, vol 12, pp 227–241Google Scholar
  45. 45.
    Simmhan YL, Plale B, Gannon D (2005) A survey of data provenance in e-science. ACM Sigmod Rec 34(3):31–36.  https://doi.org/10.1145/1084805.1084812 CrossRefGoogle Scholar
  46. 46.
    Sarikhani M (2015) An adaptive provenance collection architecture in scientific workflow systems. Ph.D. Thesis, The University of Adelaide, Adelaide, AustraliaGoogle Scholar
  47. 47.
    Lee EA, Parks TM (1995) Dataflow process networks. Proc IEEE 83(5):773–801.  https://doi.org/10.1109/5.381846 CrossRefGoogle Scholar
  48. 48.
    Brooks C, Lee EA, Liu X, Neuendorffer S, Zhao Y, Zheng H (2008) Heterogeneous concurrent modeling and design in Java (volume 3: Ptolemy ii domains). EECS Department, University of California, Berkley, CaliforniaGoogle Scholar
  49. 49.
    Muniswamy-Reddy K-K (2010) Foundations for provenance-aware systems. Harvard University, CambridgeGoogle Scholar
  50. 50.
    Anand MK (2010) Managing scientific workflow provenance. Univeristy of California Davis, DavisGoogle Scholar
  51. 51.
    Bowers S, McPhillips TM, Ludäscher B (2008) Provenance in collection-oriented scientific workflows. Concurr Comput Pract Exp 20(5):519–529.  https://doi.org/10.1002/cpe.1226 CrossRefGoogle Scholar
  52. 52.
    Moreau L, Freire J, Futrelle J, McGrath R, Myers J, Paulson P (2008) The open provenance model: an overview. In: Freire J, Koop D, Moreau L (eds) Provenance and annotation of data and processes, vol 5272. Lecture notes in computer science. Springer, Berlin, Germany, pp 323–326.  https://doi.org/10.1007/978-3-540-89965-5_31
  53. 53.
    Sonntag M, Karastoyanova D, Deelman E (2010) Bridging the gap between business and scientific workflows: humans in the loop of scientific workflows. In: Sixth international conference on e-science (e-science 2010), Brisbane, Queensland, Australia, pp 206–213. IEEE.  https://doi.org/10.1109/eScience.2010.12
  54. 54.
    Ludäscher B, Weske M, McPhillips T, Bowers S (2009) Scientific workflows: business as usual? In: Dayal U, Eder J, Koehler J, Reijers H (eds) Business process management, vol 5701. Lecture notes in computer science. Springer, Berlin, Germany, pp 31–47.  https://doi.org/10.1007/978-3-642-03848-8_4
  55. 55.
    Barga R, Gannon D (2007) Scientific versus business workflows. In: Taylor I, Deelman E, Gannon D, Shields M (eds) Workflows for e-science. Springer, London, pp 9–16.  https://doi.org/10.1007/978-1-84628-757-2_2
  56. 56.
    Andrews T, Curbera F, Dholakia H, Goland Y, Klein J, Leymann F, Liu K, Roller D, Smith D, Thatte S (2003) Business process execution language for web services. versionGoogle Scholar
  57. 57.
    Juric MB, Mathew B, Sarang PG (2006) Business process execution language for web services: an architect and developer’s guide to orchestrating web services using BPEL4WS. Packt Publishing Ltd, BirminghamGoogle Scholar
  58. 58.
    Altintas I, Barney O, Jaeger-Frank E (2006) Provenance collection support in the Kepler scientific workflow system. In: Moreau L, Foster I (eds) Provenance and annotation of data, vol 4145. Lecture notes in computer science. Springer, Berlin, Germany, pp 118–132.  https://doi.org/10.1007/11890850_14
  59. 59.
    Deelman E, Gannon D, Shields M, Taylor I (2009) Workflows and e-science: an overview of workflow system features and capabilities. Future Gener Comput Syst 25(5):528–540.  https://doi.org/10.1016/j.future.2008.06.012 CrossRefGoogle Scholar
  60. 60.
    Mattoso M, Werner C, Travassos GH, Braganholo V, Ogasawara E, Oliveira D, Cruz SMS, Martinho W, Murta L (2010) Towards supporting the life cycle of large scale scientific experiments. Int J Bus Process Integr Manag 5(1):79–92CrossRefGoogle Scholar
  61. 61.
    Stevens R, Zhao J, Goble C (2007) Using provenance to manage knowledge of in silico experiments. Briefings Bioinform 8(3):183–194.  https://doi.org/10.1093/bib/bbm015 CrossRefGoogle Scholar
  62. 62.
    Cruz SMS, Barros PM, Bisch PM, Campos MLM, Mattoso M (2008) Provenance services for distributed workflows. In: 8th IEEE international symposium on cluster computing and the grid (CCGRID), Lyon, France, 19–22 May 2008. IEEE, pp 526–533.  https://doi.org/10.1109/CCGRID.2008.73
  63. 63.
    Belhajjame K, Wolstencroft K, Corcho O, Oinn T, Tanoh F, William A, Goble C (2008) Metadata management in the Taverna workflow system. In: 8th IEEE international symposium on cluster computing and the grid, CCGRID’08. IEEE, pp 651–656Google Scholar
  64. 64.
    Davidson SB, Freire J (2008) Provenance and scientific workflows: challenges and opportunities. In: ACM SIGMOD international conference on management of data, Vancouver, Canada. ACM, 1376772, pp 1345–1350.  https://doi.org/10.1145/1376616.1376772
  65. 65.
    Lim C, Lu S, Chebotko A, Fotouhi F (2010) Prospective and retrospective provenance collection in scientific workflow environments. In: International Conference on Services Computing (SCC), Miami, Florida. IEEE, pp 449–456.  https://doi.org/10.1109/SCC.2010.18
  66. 66.
    McPhillips T, Bowers S, Belhajjame K, Ludascher B (2015) Retrospective provenance without a runtime provenance recorder. Paper presented at the proceedings of the 7th USENIX conference on theory and practice of provenance, Edinburgh, ScotlandGoogle Scholar
  67. 67.
    Marinho A, Werner C, Cruz S, Mattoso M, Braganholo V, Murta L (2009) A strategy for provenance gathering in distributed scientific workflows. In: International conference on services computing (SCC), Bangalore, India. IEEE, pp 344–347.  https://doi.org/10.1109/SERVICES-I.2009.53
  68. 68.
    Groth PT (2005) On the record: provenance in large scale, open distributed systems. A mini-thesis for transfer from M.Phil. to Ph.D., University of Southampton, Southampton, EnglandGoogle Scholar
  69. 69.
    Spillane RP, Sears R, Yalamanchili C, Gaikwad S, Chinni M, Zadok E (2009) Story book: an efficient extensible provenance framework. In: Theory and practice of provenance (TaPP’09), San Francisco, CaliforniaGoogle Scholar
  70. 70.
    Vahdat A, Anderson TE (1998) Transparent result caching. In: USENIX annual technical conference, New Orleans, LouisianaGoogle Scholar
  71. 71.
    Malik T, Gehani A, Tariq D, Zaffar F (2013) Sketching distributed data provenance. In: Liu Q, Bai Q, Giugni S, Williamson D, Taylor J (eds) Data provenance and data management in eScience. Studies in computational intelligence. Springer, Berlin, Germany, pp 85–107.  https://doi.org/10.1007/978-3-642-29931-5_4
  72. 72.
    Malik T, Nistor L, Gehani A (2010) Tracking and sketching distributed data provenance. In: Sixth international conference on e-science (e-Science 2010), Brisbane, Queensland, Australia. IEEE, pp 190–197.  https://doi.org/10.1109/eScience.2010.51
  73. 73.
    Gehani A, Tariq D (2012) SPADE: support for provenance auditing in distributed environments. In: Narasimhan P, Triantafillou P (eds) Middleware 2012, vol 7662. Lecture notes in computer science. Springer, Berlin, Germany, pp 101–120.  https://doi.org/10.1007/978-3-642-35170-9_6
  74. 74.
    Widom J (2005) Trio: a system for integrated management of data, accuracy, and lineage. In: Conference on innovative data systems research (CIDR), Asilomar, CaliforniaGoogle Scholar
  75. 75.
    Ikeda R, Widom J (2010) Panda: a system for provenance and data. IEEE Data Eng Bull 33(3):42–49Google Scholar
  76. 76.
    Foster IT, Vöckler J-S, Wilde M, Zhao Y (2003) The virtual data grid: a new model and architecture for data-intensive collaboration. In: Conference on innovative data systems research (CIDR), Asilomar, California, pp 18–29Google Scholar
  77. 77.
    Cao B, Plale B, Subramanian G, Robertson E, Simmhan Y (2009) Provenance information model of karma version 3. In: International conference on services computing (SCC), Bangalore, India. IEEE, pp 348–351.  https://doi.org/10.1109/SERVICES-I.2009.54
  78. 78.
    Simmhan YL, Plale B, Gannon D (2008) Karma2: provenance management for data-driven workflows. Int J Web Serv Res 5(2):1–22CrossRefGoogle Scholar
  79. 79.
    Lanter DP (1990) Lineage in gis: The problem and a solution. In: National center for geographic information and analysis (NCGIA), Santa Barbara, CaliforniaGoogle Scholar
  80. 80.
    Hasan R, Sion R, Winslett M (2009) The case of the fake Picasso: preventing history forgery with secure provenance. Paper presented at the proccedings of the 7th conference on file and storage technologies, San Francisco, CaliforniaGoogle Scholar
  81. 81.
    Asghar MR, Ion M, Russello G, Crispo B (2012) Securing data provenance in the cloud. In: Camenisch J, Kesdogan D (eds) Open problems in network security: IFIP WG 11.4 international workshop, iNetSec 2011, Lucerne, Switzerland, June 9, 2011, Revised Selected Papers. Springer, Berlin, pp 145–160.  https://doi.org/10.1007/978-3-642-27585-2_12
  82. 82.
    Murta L, Braganholo V, Chirigati F, Koop D, Freire J (2015) noWorkflow: capturing and analyzing provenance of scripts. In: Ludäscher B, Plale B (eds) Provenance and annotation of data and processes: 5th international provenance and annotation workshop, IPAW 2014, Cologne, Germany, 9–13 June 2014. Revised selected papers. Springer International Publishing, Cham, pp 71–83.  https://doi.org/10.1007/978-3-319-16462-5_6
  83. 83.
    McPhillips T, Song T, Kolisnik T, Aulenbach S, Belhajjame K, Bocinsky K, Cao Y, Chirigati F, Dey S, Freire J (2015) YesWorkflow: a user-oriented, language-independent tool for recovering workflow information from scripts. arXiv preprint arXiv:1502.02403
  84. 84.
    Reynolds P, Killian C, Wiener JL, Mogul JC, Shah MA, Vahdat A (2006) Pip: detecting the unexpected in distributed systems. Paper presented at the proceedings of the 3rd conference on networked systems design & implementation—volume 3, San Jose, CAGoogle Scholar
  85. 85.
    Singh A, Maniatis P, Roscoe T, Druschel P (2006) Using queries for distributed monitoring and forensics. Paper presented at the Proceedings of the 1st ACM SIGOPS/EuroSys European conference on computer systems 2006, Leuven, BelgiumGoogle Scholar
  86. 86.
    Ruth P, Xu D, Bhargava B, Regnier F (2004) E-notebook middleware for accountability and reputation based trust in distributed data sharing communities. In: Jensen C, Poslad S, Dimitrakos T (eds) Trust management: second international conference, iTrust 2004, Oxford, UK, March 29–April 1, 2004. Proceedings. Springer, Berlin, pp 161–175.  https://doi.org/10.1007/978-3-540-24747-0_13
  87. 87.
    Marathe AP (2001) Tracing lineage of array data. J Intell Inf Syst 17(2–3):193–214.  https://doi.org/10.1023/A:1012857830230 CrossRefzbMATHGoogle Scholar
  88. 88.
    Otero C (2012) Software engineering design: theory and practice, 1st edn. CRC Press, Boca RatonGoogle Scholar
  89. 89.
    Aktas MS, Plale B, Leake D, Mukhi NK (2013) Unmanaged workflows: their provenance and use. In: Liu Q, Bai Q, Giugni S, Williamson D, Taylor J (eds) Data provenance and data management in eScience. Studies in computational intelligence. Springer, Berlin, Germany, pp 59–81.  https://doi.org/10.1007/978-3-642-29931-5_3
  90. 90.
    De Nies T, Coppens S, Van Deursen D, Mannens E, Van de Walle R (2012) Automatic discovery of high-level provenance using semantic similarity. In: Groth P, Frew J (eds) Provenance and annotation of data and processes. Lecture notes in computer science. Springer, Berlin, pp 97–110.  https://doi.org/10.1007/978-3-642-34222-6_8
  91. 91.
    Magliacane S (2012) Reconstructing provenance. In: Cudré-Mauroux P, Heflin J, Sirin E et al (eds) The semantic web (ISWC), vol 7650. Lecture notes in computer science. Springer, Berlin, pp 399–406.  https://doi.org/10.1007/978-3-642-35173-0_29
  92. 92.
    Tariq D, Ali M, Gehani A (2012) Towards automated collection of application-level data provenance. In: Theory and practice of provenance (TaPP’12), Boston, Massachusetts. USENIX AssociationGoogle Scholar
  93. 93.
    Web-Page (2013) getting started with kepler provenance 2.4. https://code.kepler-project.org/code/kepler/trunk/modules/provenance/docs/provenance.pdf
  94. 94.
    Simmhan Y, Barga R, Van Ingen C, Lazowska E, Szalay A (2009) Building the trident scientific workflow workbench for data management in the cloud. In: 3rd international conference on advanced engineering computing and applications in sciences (ADVCOMP), pp 41–50.  https://doi.org/10.1109/ADVCOMP.2009.14
  95. 95.
    Barga R, Simmhan Y, Withana EC, Sahoo S, Jackson J, Araujo N (2010) Provenance for scientific workflows towards reproducible research. IEEE Data Eng Bull 33:50–59Google Scholar
  96. 96.
    Barga R, Jackson J, Araujo N, Guo D, Gautam N, Simmhan Y (2008) The trident scientific workflow workbench. In: IEEE fourth international conference on eScience (eScience ’08), Indianapolis, Indiana, pp 317–318.  https://doi.org/10.1109/eScience.2008.126
  97. 97.
    Freeman E, Robson E, Bates B, Sierra K (2004) Head first design patterns. O’Reilly Media, Inc., Sebastopol, CA, USAGoogle Scholar
  98. 98.
    Forman IR, Forman N (2004) Java reflection in action. Manning Publications Co., Greenwich, CT, USAGoogle Scholar
  99. 99.
    Maes P (1987) Concepts and experiments in computational reflection. In: Meyrowitz N (ed) Object-oriented programming systems, languages and applications (OOPSLA), Orlando, Florida, USA. ACM, 38821, pp 147–155.  https://doi.org/10.1145/38765.38821
  100. 100.
    Oliva A, Garcia IC, Buzato LE (1998) The reflective architecture of Guaraná. State University of Campinas, Sao PauloGoogle Scholar
  101. 101.
    Corradi A, Lodolo E, Monti S, Pasini S (2009) Dynamic reconfiguration of middleware for ubiquitous computing. In: the 3rd international workshop on Adaptive and dependable mobile ubiquitous systems, London, UK. ACM, pp 7–12Google Scholar
  102. 102.
    Smith BC (1984) Reflection and semantics in Lisp. In: The 11th ACM SIGACT-SIGPLAN symposium on principles of programming languages (POPL84), Salt Lake City, Utah, USA. ACM, 800513, pp 23–35.  https://doi.org/10.1145/800017.800513
  103. 103.
    Coulson G (2001) What is reflective middleware. IEEE Distrib Syst Online 2(8):165–169Google Scholar
  104. 104.
    Barbosa R, Pinho LM (2004) Monitoring of real time systems: a case for reflection. Polytechnic Institute of Porto, PortoGoogle Scholar
  105. 105.
    McKinley PK, Sadjadi SM, Kasten EP, Cheng BHC (2004) Composing adaptive software. Computer 37(7):56–64.  https://doi.org/10.1109/MC.2004.48 CrossRefGoogle Scholar
  106. 106.
    Aksit M, Choukair Z (2003) Dynamic, adaptive and reconfigurable systems overview and prospective vision. In: the 23rd international conference on distributed computing systems workshops, 19–22 May 2003. IEEE, pp 84–89.  https://doi.org/10.1109/ICDCSW.2003.1203537
  107. 107.
    Webb D, Wendelborn A (2003) The PAGIS grid application environment. In: Sloot PA, Abramson D, Bogdanov A, Gorbachev Y, Dongarra J, Zomaya A (eds) Computational science—ICCS 2003, vol 2659. Lecture notes in computer science. Springer, Berlin, pp 1113–1122.  https://doi.org/10.1007/3-540-44863-2_110
  108. 108.
    Lopes CV (2002) Aspect-oriented programming: an historical perspective (what’s in a name?). University of California, IrvineGoogle Scholar
  109. 109.
    Pawlak R, Seinturier L, Retaillé J-P, Younessi H (2005) Foundations of AOP for J2EE Development. Apress.  https://doi.org/10.1007/978-1-4302-0063-5 Google Scholar
  110. 110.
    Elrad T, Aksit M, Kiczales G, Lieberherr KJ, Ossher H (2001) Discussing aspects of AOP. Commun ACM 44(10):33–38CrossRefGoogle Scholar
  111. 111.
    Web-Page Provenance Aware Service Oriented Architecture (PASOA) (2014). http://twiki.pasoa.ecs.soton.ac.uk/bin/view/PASOA/WebHome. Accessed 10 July 2014
  112. 112.
    Ding L, Michaelis J, McCusker J, McGuinness DL (2011) Linked provenance data: a semantic web-based approach to interoperable workflow traces. Future Gener Comput Syst 27(6):797–805.  https://doi.org/10.1016/j.future.2010.011 CrossRefGoogle Scholar
  113. 113.
    Moreau L, Ludäscher B, Altintas I, Barga RS, Bowers S, Callahan S, Chin G, Clifford B, Cohen S, Cohen-Boulakia S, Davidson S, Deelman E, Digiampietri L, Foster I, Freire J, Frew J, Futrelle J, Gibson T, Gil Y, Goble C, Golbeck J, Groth P, Holland DA, Jiang S, Kim J, Koop D, Krenek A, McPhillips T, Mehta G, Miles S, Metzger D, Munroe S, Myers J, Plale B, Podhorszki N, Ratnakar V, Santos E, Scheidegger C, Schuchardt K, Seltzer M, Simmhan YL, Silva C, Slaughter P, Stephan E, Stevens R, Turi D, Vo H, Wilde M, Zhao J, Zhao Y (2008) Special issue: the first provenance challenge. Concurr Comput Pract Exp 20(5):409–418.  https://doi.org/10.1002/cpe.1233 CrossRefGoogle Scholar
  114. 114.
    Web-Page (2010) The fourth and last provenance challenge. http://twiki.ipaw.info/bin/view/Challenge/FourthProvenanceChallenge
  115. 115.
    Web-Page Provenance Challenge Wik. http://twiki.ipaw.info/bin/view/Challenge/WebHome. Accessed November 2008
  116. 116.
    Garijo D, Gil Y (2012) The OPMW ontology. http://www.opmw.org/model/OPMW_20121009/
  117. 117.
    Garijo D, Gil Y (2012) Towards open publication of reusable scientific workflows: abstractions, standards and linked data. http://www.isi.edu/~gil/papers/garijo-gil-opmw12.pdf
  118. 118.
    Altintas I, Berkley C, Jaeger E, Jones M, Ludascher B, Mock S (2004) Kepler: an extensible system for design and execution of scientific workflows. In: 16th international conference on scientific and statistical database management, 21–23 June 2004. IEEE, pp 423–424.  https://doi.org/10.1109/SSDM.2004.1311241
  119. 119.
    Kim J, Deelman E, Gil Y, Mehta G, Ratnakar V (2008) Provenance trails in the wings/pegasus system. Concurr Comput Pract Exp 20(5):587–597.  https://doi.org/10.1002/cpe.1228 CrossRefGoogle Scholar
  120. 120.
    Web-Page Pegasus workflow management system. http://pegasus.isi.edu/. Accessed 10 March 2015
  121. 121.
    Lin C, Lu S, Lai Z, Chebotko A, Fei X, Hua J, Fotouhi F (2008) Service-oriented architecture for VIEW: a visual scientific workflow management system. In: IEEE international conference on services computing (SCC’08), Honolulu, Hawaii. IEEE, pp 335–342Google Scholar
  122. 122.
    Simmhan Y, Plale B, Gannon D, Marru S (2006) Performance evaluation of the karma provenance framework for scientific workflows. In: Moreau L, Foster I (eds) Provenance and annotation of data, vol 4145. Lecture notes in computer science. Springer, Berlin, pp 222–236.  https://doi.org/10.1007/11890850_23
  123. 123.
    Wolstencroft K, Haines R, Fellows D, Williams A, Withers D, Owen S, Soiland-Reyes S, Dunlop I, Nenadic A, Fisher P (2013) The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucl Acids Res 41(W1):W557–W561CrossRefGoogle Scholar
  124. 124.
    Oinn T, Greenwood M, Addis M, Alpdemir MN, Ferris J, Glover K, Goble C, Goderis A, Hull D, Marvin D (2006) Taverna: lessons in creating a workflow environment for the life sciences. Concurr Comput Pract Exp 18(10):1067–1100CrossRefGoogle Scholar
  125. 125.
    Marinho A, Murta L, Werner C, Braganholo V, Ogasawara E, Cruz SMS, Mattoso M (2010) Integrating provenance data from distributed workflow systems with ProvManager. In: Provenance and annotation of data and processes. Springer, pp 286–288Google Scholar
  126. 126.
    Marinho A, Murta L, Werner C, Braganholo V, Cruz SMS, Ogasawara E, Mattoso M (2010) Managing provenance in scientific workflows with ProvManager. In: International workshop on challenges in e-Science (CIS2010), Petrópolis, Rio de Janeiro, Brazil, pp 17–24Google Scholar
  127. 127.
    Olston C, Reed B, Srivastava U, Kumar R, Tomkins A (2008) Pig latin: a not-so-foreign language for data processing. Paper presented at the proceedings of the 2008 ACM SIGMOD international conference on management of data, Vancouver, CanadaGoogle Scholar
  128. 128.
    Green TJ, Karvounarakis G, Tannen V (2007) Provenance semirings. Paper presented at the proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, Beijing, ChinaGoogle Scholar
  129. 129.
    Dey S, Belhajjame K, Koop D, Raul M, Ludascher B (2015) Linking prospective and retrospective provenance in scripts. Paper presented at the proceedings of the 7th USENIX conference on theory and practice of provenance, Edinburgh, ScotlandGoogle Scholar
  130. 130.
    Kim J, Deelman E, Gil Y, Mehta G, Ratnakar V (2008) Provenance trails in the wings/pegasus system. Concurr Comput Pract Exper 20(5):587–597.  https://doi.org/10.1002/cpe.1228 CrossRefGoogle Scholar
  131. 131.
    Williams DN, Bremer T, Doutriaux C, Patchett J, Williams S, Shipman G, Miller R, Pugmire DR, Smith B, Steed C, Bethel EW, Childs H, Krishnan H, Prabhat P, Wehner M, Silva CT, Santos E, Koop D, Ellqvist T, Poco J, Geveci B, Chaudhary A, Bauer A, Pletzer A, Kindig D, Potter GL, Maxwell TP (2013) Ultrascale visualization of climate data. Computer 46(9):68–76.  https://doi.org/10.1109/MC.2013.119 CrossRefGoogle Scholar
  132. 132.
    Web-Page vistrails. http://www.vistrails.org/index.php/Main_Page. Accessed 20 March 2015
  133. 133.
    Silva CT, Freire J, Callahan SP (2007) Provenance for visualizations: reproducibility and beyond. Comput Sci Eng 9(5):82–89.  https://doi.org/10.1109/MCSE.2007.106 CrossRefGoogle Scholar
  134. 134.
    Hey AJG, Tansley S, Tolle KM (2009) The fourth paradigm: data-intensive scientific discovery, 1st edn. Microsoft Research Redmond, WashangtonGoogle Scholar
  135. 135.
    Delaney J, Heath G, Chave A, Howe B, Kirkham H (2000) NEPTUNE: real-time ocean and earth sciences at the scale of a tectonic plate. Oceanography 13(2):71–79CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  1. 1.Department of Computer Engineering and Information TechnologySafashahr Branch, Islamic Azad UniversitySafashahrIran
  2. 2.School of Computer ScienceLevel 4 Ingkarni Wardli building, University of AdelaideAdelaideAustralia

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