A Study of Two-Phase Retrieval for Process-Oriented Case-Based Reasoning

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 494)

Abstract

Process-Oriented Case-Based Reasoning (PO-CBR) systems often use structured cases, which in turn require effective structure-based retrieval methods, especially when dealing with large processes and/or large case bases. Good retrieval performance can be facilitated by two-phased retrieval methods which first winnow candidate cases with a comparatively inexpensive retrieval phase, and then apply a more expensive strategy to rank the selected cases. Examples of such processes have been shown to provide good retrieval results in limited retrieval time. However, studies of such methods have focused primarily on overall performance, rather than on how the individual contributions of each phase interact to affect overall performance. This misses an opportunity to tune the component algorithms in light of the system task and case base characteristics, for specific task needs. This chapter examines two-phased retrieval as a means of addressing the complexity in many PO-CBR domains, and specifically examines the performance of each phase of two-phased retrieval individually, demonstrating characteristics of the phases’ interaction and providing general lessons for how to design and deploy two-phased retrieval systems.

Notes

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. OCI-0721674 and by a grant from the Data to Insight Center of Indiana University. Portions of this chapter are adapted from a workshop paper [42] and a dissertation [24].

References

  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–52. http://www.iiia.csic.es/People/enric/AICom.pdf (1994)
  2. 2.
    Leake, D.: CBR in context: the present and future. In: Leake, D. (ed.) Case-Based Reasoning. Experiences, Lessons and Future Directions, pp. 3–30. AAAI Press, Menlo Park (1996)Google Scholar
  3. 3.
    Mantaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M., Cox, M., Forbus, K., Keane, M., Aamodt, A., Watson, I.: Retrieval, reuse, revision, and retention in CBR. Knowl. Eng. Rev. 20(3), 255–260 (2005)Google Scholar
  4. 4.
    Minor, M., Montani, S.: Proceedings of the ICCBR-2012 workshop on provenance-aware case-based reasoning (2012)Google Scholar
  5. 5.
    Han, W.S., Pham, M.D., Lee, J., Kasperovics, R., Yu, J.X.: igraph in action: performance analysis of disk-based graph indexing techniques. In: Proceedings of the International Conference on Management of Data, SIGMOD ’11, pp.1241–1242. ACM, New York (2011)Google Scholar
  6. 6.
    Kendall-Morwick, J., Leake, D.: Facilitating representation and retrieval of structured cases: principles and toolkit. Information Systems (2012, in press)Google Scholar
  7. 7.
    Leake, D.: An indexing vocabulary for case-based explanation. In: Proceedings of the 9th National Conference on Artificial Intelligence, pp. 10–15. AAAI Press, Menlo Park (1991)Google Scholar
  8. 8.
    Schank, R., Osgood, R., Brand, M., Burke, R., Domeshek, E., Edelson, D., Ferguson, W., Freed, M., Jona, M., Krulwich, B., Ohmayo, E., Pryor, L.: A content theory of memory indexing. Technical report 1, Institute for the Learning Sciences, Northwestern University (1990)Google Scholar
  9. 9.
    Reichherzer, T., Leake, D.: Towards automatic support for augmenting concept maps with documents. In: Proceedings of 2nd International Conference on Concept Mapping (2006)Google Scholar
  10. 10.
    Minor, M., Montani, S.: Preface. In: Proceedings of the ICCBR-12 Workshop on Process-Oriented Case-Based Reasoning (2012)Google Scholar
  11. 11.
    Floyd, M., Fuchs, B., Gonzlez-Calero, P., Leake, D., Ontañón, S., Plaza, E., Rubin, J.: Preface. In: Proceedings of the ICCBR-12 Workshop on TRUE: Traces for Reusing Users’ Experiences—Cases, Episodes, and Stories (2012)Google Scholar
  12. 12.
    Leake, D., Roth-Berghofer, T., Smyth, B., Kendall-Morwick, J. (eds.): Proceedings of the ICCBR-2010 workshop on Provenance-Aware Case-Based Reasoning (2010)Google Scholar
  13. 13.
    Ko, R.K.L.: A computer scientist’s introductory guide to business process management (bpm). Crossroads 15(4), 4:11–4:18 (2009)Google Scholar
  14. 14.
    Montani, S., Leonardi, G.: Retrieval and clustering for business process monitoring: results and improvements. In: Díaz-Agudo, B., Watson, I. (eds.) ICCBR. Lecture Notes in Computer Science, vol 7466, pp. 269–283. Springer, New York (2012)Google Scholar
  15. 15.
    Minor, M., Tartakovski, A., Bergmann, R.: Representation and structure-based similarity assessment for agile workflows. In: Proceedings of the 7th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development, ICCBR ’07, pp. 224–238. Springer, Berlin (2007)Google Scholar
  16. 16.
    Kapetanakis, S., Petridis, M., Ma, J., Knight, B., Bacon, L.: Enhanching similarity measures and context provision for the intelligent monitoring of business processes in cbr-wims. In: Proceedings of the ICCBR-11 Workshop on Process-Oriented Case-Based Reasoning (2011)Google Scholar
  17. 17.
    Oinn, T., Greenwood, M., Addis, M., Alpdemir, M.N., Ferris, J., Glover, K., Goble, C., Goderis, A., Hull, D., Marvin, D., Li, P., Lord, P., Pocock, M.R., Senger, M., Stevens, R., Wipat, A., Wroe, C.: Taverna: lessons in creating a workflow environment for the life sciences: research articles. concurr. Comput. 18(10), 1067–1100 (2006)CrossRefGoogle Scholar
  18. 18.
    Oinn, T., Addis, M., Ferris, J., Marvin, D., Senger, M., Greenwood, M., Carver, T., Glover, K., Pocock, M.R., Wipat, A., Li, P.: Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20(17), 3045–3054 (2004)CrossRefGoogle Scholar
  19. 19.
    Altintas, I., Berkley, C., Jaeger, E., Jones, M., Ludascher, B., Mock, S.: Kepler: An extensible system for design and execution of scientific workflows. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management, IEEE Computer Society, Washington (2004)Google Scholar
  20. 20.
    Barga, R., Jackson, J., Araujo, N., Guo, D., Gautam, N., Simmhan, Y.: The trident scientific workflow workbench. In: Proceedings of the 4th IEEE International Conference on eScience, pp. 317–318. IEEE Computer Society, Washington (2008)Google Scholar
  21. 21.
    Shirasuna, S.: A Dynamic Scientific Workflow System for the Web Services Architecture. PhD thesis, Indiana University (2007)Google Scholar
  22. 22.
    Goble, C.A., De Roure, D.C.: Myexperiment: social networking for workflow-using e-scientists. In: Proceedings of the 2nd Workshop on Workflows in Support of Large-Scale Science, WORKS ’07, pp. 1–2. ACM, New York (2007)Google Scholar
  23. 23.
    Bhagat, J., Tanoh, F., Nzuobontane, E., Laurent, T., Orlowski, J., Roos, M., Wolstencroft, K., Aleksejevs, S., Stevens, R., Pettifer, S., Lopez, R., Goble, C.A.: Biocatalogue: a universal catalogue of web services for the life sciences. Nucleic Acids Res. 38(Web-Server-Issue), 689–694 (2010)Google Scholar
  24. 24.
    Kendall-Morwick, J.: Leveraging Structured Cases: Reasoning from Provenance Cases to Support Authorship of Workflows. PhD thesis, Indiana University (2012)Google Scholar
  25. 25.
    Mileman, T., Knight, B., Petridis, M., Preddy, K., Mejasson, P.: Maintenance of a case-base for the retrieval of rotationally symmetric shapes for the design of metal castings. In: Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning, EWCBR ’00, pp. 418–430. Springer, London (2000)Google Scholar
  26. 26.
    Stahl, A., Minor, M., Traphöner, R.: Preface: computer cooking contest. In: Schaaf, M. (ed.) ECCBR Workshops, pp. 197–198 (2008)Google Scholar
  27. 27.
    Cañas, A.J., Leake, D.B., Maguitman, A.G.: Combining concept mapping with CBR: towards experience-based support for knowledge modeling. In: Proceedings of the 14th International Florida Artificial Intelligence Research Society Conference, pp. 286–290. AAAI Press, Menlo Calif (2001)Google Scholar
  28. 28.
    Bergmann, R., Gil, Y.: Retrieval of semantic workfows with knowledge intensive similarity measures. In: Proceedings of 19th International Conference on Case-Based Reasoning, Springer, Berlin (2011, in Press)Google Scholar
  29. 29.
    Bergmann, R., Minor, M., Islam, M.S., Schumacher, P., Stromer, A.: Scaling similarity-based retrieval of semantic workflows. In: Proceedings of the ICCBR-12 Workshop on Process-Oriented Case-Based Reasoning (2012)Google Scholar
  30. 30.
    Cook, S.A.: The complexity of theorem-proving procedures. In: Proceedings of the 3rd Annual ACM Symposium on Theory of Computing, STOC ’71, pp. 151–158. ACM, New York (1971)Google Scholar
  31. 31.
    Minor, M., Bergmann, R., Grg, S., Walter, K.: Adaptation of cooking instructions following the workflow paradigm. In: Marling, C. (ed.) ICCBR 2010 Workshop Proceedings (2010)Google Scholar
  32. 32.
    Gentner, D., Forbus, K.: MAC/FAC: A model of similarity-based retrieval. In: Proceedings of the 13th Annual Conference of the Cognitive Science Society, pp. 504–509. Cognitive Science Society, Chicago (1991)Google Scholar
  33. 33.
    Mntaras, R.L.D., Bridge, D., Mcsherry, D.: Case-based reasoning: an overview. AI Commun. 10, 21–29 (1997)Google Scholar
  34. 34.
    Börner, K.: Structural similarity as guidance in case-based design. In: Selected Papers from the 1st European Workshop on Topics in Case-Based Reasoning, EWCBR ’93, pp. 197–208. Springer, London (1994)Google Scholar
  35. 35.
    Kendall-Morwick, J., Leake, D.: A toolkit for representation and retrieval of structured cases. In: Proceedings of the ICCBR-11 Workshop on Process-Oriented Case-Based Reasoning (2011)Google Scholar
  36. 36.
    Bergmann, R., Gil, Y.: Retrieval of semantic workflows with knowledge intensive similarity measures. In: Proceedings of the 19th International Conference on Case-Based Reasoning Research and Development, ICCBR’11, pp. 17–31. Springer, Berlin (2011)Google Scholar
  37. 37.
    Wang, X., Smalter, A., Huan, J., Lushington, G.H.: G-hash: towards fast kernel-based similarity search in large graph databases. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT ’09, pp. 472–480. ACM, New York (2009)Google Scholar
  38. 38.
    Tian, Y., Patel, J.M., Nair, V., Martini, S., Kretzler, M.: Periscope/GQ: a graph querying toolkit. Proc. VLDB Endow. 1, 1404–1407 (2008)Google Scholar
  39. 39.
    Yan, X., Yu, P.S., Han, J.: Graph indexing: a frequent structure-based approach. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD ’04, pp. 335–346. ACM, New York (2004)Google Scholar
  40. 40.
    Giugno, R., Shasha, D.: Graphgrep: a fast and universal method for querying graphs. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 2, pp. 112–115 (2002)Google Scholar
  41. 41.
    Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. 40, 1:1–1:39 (2008)Google Scholar
  42. 42.
    Kendall-Morwick, J., Leake, D.: On tuning two-phase retrieval for structured cases. In: Proceedings of the ICCBR-12 Workshop on Process-Oriented Case-Based Reasoning (2012)Google Scholar
  43. 43.
    Aha, D., Breslow, L., Munoz-Avila, H.: Conversational case-based reasoning. Appl. Intell. 14, 9–32 (2001)CrossRefMATHGoogle Scholar
  44. 44.
    Weber, B., Reichert, M., Wild, W.: Case-base maintenance for ccbr-based process evolution. In: Roth-Berghofer, T., Göker, M.H., Güvenir, H.A. (eds.) ECCBR. Lecture Notes in Computer Science, vol. 4106, pp. 106–120. Springer, Berlin (2006)Google Scholar
  45. 45.
    Allampalli-Nagaraj, G., Bichindaritz, I.: Automatic semantic indexing of medical images using a web ontology language for case-based image retrieval. Eng. Appl. Artif. Intell. 22(1), 18–25 (2009)CrossRefGoogle Scholar
  46. 46.
    Yan, X., Yu, P.S., Han, J.: Substructure similarity search in graph databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD ’05, pp. 766–777. ACM, New York (2005)Google Scholar
  47. 47.
    Bogaerts, S., Leake, D.: Formal and experimental foundations of a new rank quality measure. In: Proceedings of the 9th European conference on Advances in Case-Based Reasoning, ECCBR ’08, pp. 74–88. Springer, Berlin (2008)Google Scholar
  48. 48.
    Leake, D., Kendall-Morwick, J.: Towards case-based support for e-science workflow generation by mining provenance. In: Proceedings of the 9th European Conference on Advances in Case-Based Reasoning, ECCBR ’08, pp. 269–283. Springer, Berlin (2008)Google Scholar
  49. 49.
    Bolton, E.E., Wang, Y., Thiessen, P.A., Bryant, S.H.: PubChem: integrated platform of small molecules and biological activities. Annu. Rep. Comput. Chem. 4, 217–241 (2008)Google Scholar
  50. 50.
    Beygelzimer, A., Kakade, S., Langford, J.: Cover trees for nearest neighbor. In: Proceedings of the 23rd International Conference on Machine Learning, ICML ’06, pp. 97–104. ACM, New York (2006)Google Scholar
  51. 51.
    Schaaf, J.W.: Fish and shrink. a next step towards efficient case retrieval in large-scale case bases. In: Proceedings of the 3rd European Workshop on Advances in Case-Based Reasoning, EWCBR ’96, pp. 362–376. Springer, London (1996)Google Scholar
  52. 52.
    Lenz, M., Burkhard, H.D.: Case retrieval nets: basic ideas and extensions. In: Grz, G., Hldobler, S. (eds.) KI-96: Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol. 1137, pp. 227–239. Springer, Berlin (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Computer Science DepartmentDePauw UniversityGreencastleUSA
  2. 2.School of Informatics and ComputingIndiana UniversityBloomingtonUSA

Personalised recommendations