Journal of Systems Integration

, Volume 5, Issue 1, pp 23–60 | Cite as

An integrating view on the viewing abstraction: Contexts and perspectives in software development, AI, and databases

  • Renate Motschnig-Pitrik


Viewing entities from different situations and representing and processing them in different contexts constitutes a fundamental concern in various disciplines of computer science. Not surprisingly, the viewing abstraction is supported by many languages and techniques employed either for programming or “world modelling”. This paper presents an overview on various manifestations of viewing mechanisms in formal notations including software development techniques, knowledge representation languages, and data models. The concepts of context and perspective are introduced in form of a language-independent framework in order to capture and systematically discuss features that characterize viewing mechanisms, such as the relationship between the two, the relation between different perspectives on the same conceptual entity, or operations supporting effective construction of contexts. In addition, it is argued that the full power of viewing can be exploited by supporting both notions: contexts as well as perspectives. In order to achieve this support, any formal notation has to fulfill a number of general requirements which are stated as a result of the investigation and the survey.


software development cooperative work contexts views information bases 


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  1. 1.
    S. Abiteboul and A. Bonner A, “Objects and views,” inProc. of ACM SIGMOD International Conference on Management of Data, Denver, Colorado, May 1991, pp. 238–247.Google Scholar
  2. 2.
    R. Ahmed, S. B. Navathe, “Version management of composite objects in CAD databases,” inACM SIGMOD. Proc. of the Internat. Conference on Management of Data, Denver, Colorado, May 1991, pp. 218–227.Google Scholar
  3. 3.
    A. Albano, L. Cardelli, and R. Orsini, “Galileo: A strongly-typed, interactive conceptual language,” inACM Transactions on Database Systems 10(2), pp. 230–260, June 1985.Google Scholar
  4. 4.
    J. Anderson, 1990.Cognitive Psychology and its Implications. Third edition, Freeman.Google Scholar
  5. 5.
    G. R. Barber, “Office semantics.” Ph.D. Thesis, Dept. of Electrical Engineering and Computer Science, MIT, Cambridge, MA, 1982.Google Scholar
  6. 6.
    C. Beeri, “New data models and languages—The challenge,” inProc. of ACM PODS 92. Symposium on Principles of Database Systems, 1992.Google Scholar
  7. 7.
    E. Bertino, “A view mechanism for object-oriented database,” inProc. of EDBT 92’; 3rd International Conference on Extending Database Technology, Lecture Notes in Computer Science No 580, Springer Verlag, Vienna, March 1992.Google Scholar
  8. 8.
    E. Bertino and H. Weigand, “An approach to authorization modeling in object-oriented database systems.”Data & Knowledge Engineering 12(1), pp. 1–29, February 1994.Google Scholar
  9. 9.
    D. G. Bobrow and T. Winograd, “An overview of KRL a knowledge representation language.”Cognitive Science 1(1), pp. 3–46, 1977.CrossRefGoogle Scholar
  10. 10.
    D. G. Bobrow and M. J. Stefik, 1993.The LOOPS Manual. Xerox Corporation, December 1983.Google Scholar
  11. 11.
    M. Brodie, “Association: A database abstraction for semantik modelling.” In P. P. Chen, editor,E-R Approach to Information Modelling and Analysis. ER Institute, pp. 583–608, 1981.Google Scholar
  12. 12.
    M. Brodie, “On the development of data models.” In M. Brodie, J. Mylopoulos, J. W. Schmidt, editors,On Conceptual Modeling, Springer Verlag, 1984.Google Scholar
  13. 13.
    E. F. Codd, 1990.The Relational Model for Database Management, Version 2. Addison-Wesley.Google Scholar
  14. 14.
    J. de Kleer, “An assumption-based truth, maintenance system.”Artificial Intelligence 28(2), pp. 127–162, January 1986.CrossRefGoogle Scholar
  15. 15.
    R. Fikes and Kehler, “The role of frame-based representations in reasoning.”Communications of the ACM 28(9) 1985.Google Scholar
  16. 16.
    R. E. Filman, “Reasoning with worlds and truth maintenance in a knowledge-based programming environment.”Communications of the ACM 31(4), pp. 382–401, April 1988.CrossRefGoogle Scholar
  17. 17.
    N. Findler, 1979.Associative Networks: Representation and Use of Knowledge by Computers. Academic Press.Google Scholar
  18. 18.
    A. Finkelstein, J. Kramer, and B. Nuseibeh, “Viewpoints: A framework for integrating multiple perspectives in system development.”Int. Journal of Software Engineering and Knowledge Engineering 2(1), World Scientific, pp. 31–58, March 1992.CrossRefGoogle Scholar
  19. 19.
    D. Gottlob, G. Kappel, and M. Schrefl, “Semantics of object-oriented data models—The evolving algebra approach.” Int. Workshop on Information Systems for the 90's, J. W. Schmidt, editor,Lecture Notes in Computer Science, Springer Verlag, 1991.Google Scholar
  20. 20.
    A. N. Habermann, Ch. Krueger, B. Pierce, B. Staudt, and J. Wenn, “Programming with views” Technical Report CMU-CS-87-177, Carnegie Mellon University, January 1988.Google Scholar
  21. 21.
    M. Hammer and D. McLeod, “Database description with SDM: A semantic database model.”ACM Transactions on Databas Systems 6(3), pp. 351–386, September 1981.CrossRefGoogle Scholar
  22. 22.
    W. Harrison, “The RPDE3 environment—A framework for integrating tool fragments.”IEEE Software 4(6), pp. 46–57, November 1987.Google Scholar
  23. 23.
    G. Hendrix, “Expanding the utility of semantic networks through partitioning,” inAdvance Papers of the IJCAI'75, International Joint Conf. on AI, 1975, pp. 115–121.Google Scholar
  24. 24.
    G. Hendrix, “Encoding knowledge in partitioned networks,” in [17].Associative Networks: Representation and Use of Knowledge by Computers. Academic Press.Google Scholar
  25. 25.
    C. Hewitt, “Procedural embedding of knowledge in PLANNER,” inProc. of the 2nd IJCAI, International Joint Conf. on AI, September 1971, pp. 167–182.Google Scholar
  26. 26.
    C. Hewitt, G. Attardi, and M. Simi, “Knowledge embedding with a description system.”AI Memo, MIT, August 1980.Google Scholar
  27. 27.
    R. Hull and R. King, “Semantic database modelling: Survey, applications and research issues”ACM Computing Surveys 19(3), September 1987.Google Scholar
  28. 28.
    G. Kaiser and D. Garlan, “Melding data flow and object-oriented programming” inProc. of the OOPSLA 87', International Conf. on Object-Oriented Programming Systems, Languages and Applications, pp. 254–267, Proc. published asSIGPLAN Notices 22(12), December 1987.Google Scholar
  29. 29.
    R. H. Katz, “Toward a unified framework for version modelling in engineering databases.”ACM Computing Surveys 22(4), pp. 375–408, December 1990.CrossRefGoogle Scholar
  30. 30.
    M. Kifer, W. Kim, and Y. Sagiv, “Querying object-oriented databases,” inProc. of the ACM SIGMOD Conference on Management of Data, June 1992, pp. 393–402.Google Scholar
  31. 31.
    W. Kim, E. Bertino, J. F. Garza, “Composite objects revisited,” inProc. of the International Conf. on Object-Oriented Programming Systems, Languages and Applications, October 1989, pp. 337–347.Google Scholar
  32. 32.
    D. B. Leblang and R. P. Chase, “Computer-aided software engineering in a distributed workstation environment,” inProc. of the ACM SIGPLAN/SIGSOFT Conf. on Practical Software Development Environments, ACM, New York, April 1984, pp. 1041–112.Google Scholar
  33. 33.
    J. C. Leite and P. A. Freeman, “Requirements validation, through viewpoint resolution,”IEEE Transactions on Software Engineering 17(12), pp. 1253–1269, December 1991.CrossRefGoogle Scholar
  34. 34.
    H. Levesque and J. Mylopoulos, “Procedural semantics for semantic networks,” in [17].Google Scholar
  35. 35.
    H. Levesque, “A logic of implicit and explicit belief,” inProc. of the National Conference on Artificial Intelligence, Austin, Texas, August 6–10 1984., pp. 198–202.Google Scholar
  36. 36.
    R. Motschnig-Pitrik., “A framework for the support of a common structural level for software, database-, and knowledge based systems”The Journal of Systems and Software North Holland., (12)12, pp. 125–137, December 1990.CrossRefGoogle Scholar
  37. 37.
    R. Motschnig-Pitrik and J. Mylopoulos, “Classes and instances,”Int. Journal of Intelligent and Cooperative Information Systems 1(1), pp. 61–92, March 1992.CrossRefGoogle Scholar
  38. 38.
    R. Motschnig-Pitrik, “The semantics of parts versus aggregates in data/knowledge modelling,” inProceedings of the 5th International Conference on Advanced Information Systems Engineering, CAiSE 93', C. Rolland et al., editors,Lecture Notes in Computer Science No. 685, Springer-Verlag June 1993.Google Scholar
  39. 39.
    R. Motschnig-Pitrik, “Analyzing the notions of atribbute, aggregate, part and member in data/knowledge modelling,” inProc. of the 4th Internat. Conf. on Information Systems Development, Bled, Slovenia, September 1994.Google Scholar
  40. 40.
    R. Motschnig-Pitrik,Requirements and Analysis of View Mechanisms in Object-Oriented Databases. University of Vienna, submitted for publication, June 1994.Google Scholar
  41. 41.
    G. Mullery, “CORE—A method for controlled requirements specification” inProc. of the 4th Int. Conference on Software Engineering, IEEE CS Press, pp. 126–135, 1979.Google Scholar
  42. 42.
    J. Mylopoulos, A. Borgida, M. Jarke, and M. Koubarakis, “Telos: Representing knowledge about information systems.”ACM Transactions on Information Systems 8(4), pp. 325–362, October 1990.CrossRefGoogle Scholar
  43. 43.
    J. Mylopoulos and R. Motschnig-Pitrik, “Partitioning an information base through contexts,”Proc. of Coop 15–95 3rd International Conference on Cooperative Information Systems, Vienna, May 1995.Google Scholar
  44. 44.
    B. Nuseibeh, J. Kramer, and A. Finkelstein, “Expressing the relationships between multiple views in requirements specification,” inProc. of the 15th Int. Conf. on Software Engineering, IEEE Baltimore, Maryland, May 1993, pp., 187–196.Google Scholar
  45. 45.
    V. Prevelakis and D. Tsichritzis, “Perspectives on software development environments,” In C. Rolland et al., editors,Proc. of the CAiSE'93, Paris, France, June 1993, Lecture Notes in Computer Science 685, Springer Verlag, 1993.Google Scholar
  46. 46.
    F. Rabitti, E. Bertino, W. Kim, and D. Woelk, “A model of authorization for next-generation database systems”ACM Transactios on Database Systems 16(1), pp. 88–131, March 1991.CrossRefGoogle Scholar
  47. 47.
    D. T. Ross, “Structured analysis (SA): A language for communicating ideas”.IEEE Transactions on Software Engineering 3(1), pp. 16–34, January 1977.Google Scholar
  48. 48.
    P.F. Schneider, “Contexts in PSN,” inProc. of the AI-CSCSI-SCEIO Conference, Victoria, B.C, May 1980. pp. 71–78.Google Scholar
  49. 49.
    M.H. Scholl, C. Laasch, and M. Tresch, “Updateable views in object oriented databases,” inProc. of the 2nd DOOD 91′, Internat. Conf. on Deductive and Object-Oriented Databases, C. Delobel et al., editors,Lecture Notes in Computer Science No. 566, Munich, Germany, December 1991, pp. 189–207.Google Scholar
  50. 50.
    M. Shaw, “The impact of modelling and abstraction concerns on modern programming languages.” In M. Brodie, J. Mylopoulos, J. W. Schmidt, editors,On Conceptual Modeling, Springer Verlag, 1984.Google Scholar
  51. 51.
    J. J. Shilling and P. F. Sweeney, “Three steps to views: Extending the object-oriented paradigm,” inACM Proceedings of OOPSLA ′89, Int. Conf. on Object-Oriented Programming, Systems, Languages, and Applications, October 1989, pp. 353–360.Google Scholar
  52. 52.
    Y.M. Shyy and S.Y.W. Su, “K: A high-level knowledge base programming language for advanced database applications,” inProc. ACM-SIGMOD Conference, 218–227, Denver, Colorado, May 1991, pp. 338–347.Google Scholar
  53. 53.
    J. M. Smith and D. C. P. Smith, “Database abstractions: Aggregation and generalization.”ACM Transactions on Database Systems 2(2), 105–133, June 1977.CrossRefGoogle Scholar
  54. 54.
    M. S. Stefik and D. G. Bobrow, “Object-oriented programming: Themes and variations,”The AI Magazine 6(4), pp. 40–62, 1986.Google Scholar
  55. 55.
    V.C. Storey, “Understanding semantic relationships,”Very Large Data Bases Journal 2(2), 455–488, October 1993.Google Scholar
  56. 56.
    SUN Microsystems, 1988.Introduction to the NSE.SUN Part No. 800-2362-1300. March.Google Scholar
  57. 57.
    G. J. Sussman and D. V. McDermott, “From PLANNER to CONNIVER—A genetic approach,” inProc. of the 3rd IJCAI 72′, Internat. Joint Conference on AI, Anaheim, CA, 1972, pp. 1171–1179.Google Scholar
  58. 58.
    G. Vinek, P. F. Rennert, and A. M. Tjoa, 1982.Datenmodellierung: Theorie und Praxis des Datenbankentwurfs. Physica-Verlag.Google Scholar
  59. 59.
    Y. Wand and R. Weber, “A unified model of software and data decomposition,” inProc. of the 12th Annual International Conference on Information Systems, New York, December 1991, pp. 101–110.Google Scholar
  60. 60.
    P. Wegner, “Dimensions of object-based language design,” inACM Proceedings of OOPSLA′87 Int. Conf. on Object-Oriented Programming Systems, Languages, and Applications, October 1987, pp. 168–182.Google Scholar
  61. 61.
    D. S. Wile and D. G. Allard, “Worlds: An organizing structure for object-bases,”ACM SIGPLAN Notices 22(1), pp. 16–26, January 1986.Google Scholar
  62. 62.
    E. Yourdon and L. L. Constantine, 1979,Structured Design—Fundamentals of a Discipline of Computer Program and Systems Design. Prentice Hall.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Renate Motschnig-Pitrik
    • 1
  1. 1.Department of Applied Computer Science and Information SystemsViennaAustria

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