Using next generation databases to develop financial applications

  • Rakesh Chandra
  • Arie Segev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 819)


Conventional database systems lack temporal, object and rule support to model financial database applications. In [CS93a], we described the complexity of financial applications and studied the database requirements of such applications. We argued that next-generation databases are an appropriate platform for developing database applications. In this paper we build upon this research by studying strategies to model entities commonly encountered in financial applications. Specifically, the financial entities discussed in this paper are financial instruments and portfolios. Positions in financial instruments and the trading strategies that give meaning to these positions are also modeled. The paper proposes class definitions to model the structural and dynamic properties of financial entities and the interactions between them. These class definitions describe a generic set of attributes and operators for the financial entities discussed. Examples from the financial domain are used to illustrate the modeling constructs and class definitions proposed.


Financial Applications Next-Generation Databases Modeling Financial Entities 


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  1. [AG90]
    R. Agrawal and N. H. Gehani. ODE (Object Database and Environment): The Language and Data Model. In Proceedings of ACM SIGMOD International Conference on the Management of Data, pages 36–45, May 1990.Google Scholar
  2. [AGM88]
    R. Abbott and Hector Garcia-Molina. Scheduling Real-time Transactions. ACM SIGMOD Record, 17(1):71–81, March 1988.Google Scholar
  3. [BL92]
    R. J. Bauer and G.E. Liepins. Genetic Algorithms and Computerized Trading Strategies. In D.E. O'Leary and P.R. Watkins, editors, Expert Systems in Finance, pages 89–100. Elsevier Publishers, 1992.Google Scholar
  4. [Car87]
    M (ed.) Carey. Special Issue on Extensible Database Systems. Database Engineering, June 1987.Google Scholar
  5. [Car90]
    M. Carey. The Architecture of the EXODUS Extensible DBMS. In Stonebraker M., editor, Readings in Database Systems, pages 488–501. Morgan Kaufman, 1990.Google Scholar
  6. [CC87]
    J. Clifford and A. Croker. The historical relational data model HRDM and an algebra based on lifespans. In Proceedings of the Third International Conference on Data Engineering, pages 528–537, February 1987.Google Scholar
  7. [Cha94]
    R. Chandra. Managing Temporal Financial Data in Extensible Databases. Technical report, University of California, 1994.Google Scholar
  8. [CS93a]
    R. Chandra and A. Segev. Managing Temporal Financial Data in an Extensible Database. In Proceedings of the 19th Int. Conf. on Very Large Databases, Dublin, Ireland, August 1993.Google Scholar
  9. [CS93b]
    R. Chandra and A. Segev. Performance Optimization of Financial Database Applications. In Proceedings of the 3rd Workshop on Information Technologies and Systems, December 1993.Google Scholar
  10. [CSS94]
    R. Chandra, A. Segev, and M. Stonebraker. Implementing Calendars and Temporal Rules in Next-Generation Databases. In Proceedings of the 10th Int. Conf. on Data Engineering, February 1994.Google Scholar
  11. [Gad88]
    S.K. Gadia. The Role of Temporal Elements in a Temporal Database. Database Engineering, 7(2):197–203, 1988.Google Scholar
  12. [JCG+92]
    C.S. Jenson, J. Clifford, S.K. Gadia, A. Segev, and R.T. Snodgrass. A Glossary of Temporal Database Concepts. ACM SIGMOD Record, 21(3):35–43, 1992.Google Scholar
  13. [Lin87]
    B. Lindsay. A Data Management Extension Architecture. In Proceedings of ACM SIGMOD International Conference on the Management of Data, May 1987.Google Scholar
  14. [Man92]
    G. Mankiw. Macroeconomics. Worth Publishers, New York, 1992.Google Scholar
  15. [PS88]
    P. Peinl and H. Sammer. High Contention in a Stock Trading Database: A Case Study. In Proceedings of ACM SIGMOD International Conference on the Management of Data, pages 260–268, May 1988.Google Scholar
  16. [RS89]
    S. Rozen and D Shasha. Using a Relational Database on Wall Street, The Good, the Bad and the Ugly. Communications of the ACM, 1989.Google Scholar
  17. [Sam87]
    H. Sammer. Online stock Trading Systems: Study of an application. In Proceedings of Spring COMPCON 87 San Francisco, pages 161–163, 1987.Google Scholar
  18. [SC93]
    A. Segev and R. Chandra. A Data Model for Time-Series Analysis. In N. Adam and B. Bhargava, editors, Advanced Database Systems. Lectures Notes in Computer Science Series, Springer Verlag, Nov 1993.Google Scholar
  19. [Sno87]
    R. Snodgrass. The Temporal Query Language TQuel. ACM TODS, 12(2), 1987.Google Scholar
  20. [SS87]
    A. Segev and A. Shoshani. A Logical Modeling of Temporal Databases. In Proceedings of ACM SIGMOD International Conference on the Management of Data, May 1987.Google Scholar
  21. [SS88]
    A. Segev and A. Shoshani. The Representation of a Temporal Data Model in the Relational Environment. In M. Rafanelli, J.C. Klensin, and P. Svensson, editors, Lecture Notes in Computer Science No. 339, pages 39–61. Springer-Verlag, 1988.Google Scholar
  22. [Sto90]
    M.R. Stonebraker. The Implementation of POSTGRES. IEEE Transactions on Knowledge and Data Engineering, 2(1):125–142, March 1990.Google Scholar
  23. [Str85]
    B. Stroustrup. The C++ Programming Language. Addison-Wesley Publishing Company, 1985.Google Scholar
  24. [Vea88]
    J. Voelcker and et. al. How computers helped stampede the stock market. SPECTRUM, Oct 1988.Google Scholar
  25. [WD92]
    G.T.J Wuu and U. Dayal. A Uniform Model for Temporal Object-Oriented Databases. In Proceedings of the 8th Int. Conf. on Data Engineering, pages 584–593, February 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Rakesh Chandra
    • 1
  • Arie Segev
    • 2
    • 3
  1. 1.Bond Portfolio Analysis GroupSalomon Brothers Inc., 7 World Trade CenterNew York
  2. 2.Walter A. Haas School of BusinessUniversity of California at BerkeleyUSA
  3. 3.Information and Computing Sciences DivisionLawrence Berkeley LaboratoryBerkeley

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