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Abstract

Motivated by automating enterprise information derivation processes, we propose a new kind of business process - Data-Continuous SQL Process (DCSP), which is data-stream driven and continuously running.

The basic operators of a DCSP are database User Defined Functions (UDFs). However, we introduce a special kind of UDFs - Relation Valued Functions (RVFs) with both input and return values specified as relations. An RVF represents a relational transformation and can be composed with other relational operators. We allow an RVF to be triggered repeatedly by stream inputs, timers or event-conditions.Thesequence of executions generates a data stream. To capture such data continuation semantics we introduce the notion of station for hosting a continuously-executed RVF, and the notion ofpipe as the FIFO stream container for asynchronous communication between stations. A station is specified with the triggering factors and the outgoing pipes. A pipe is strongly typed by a relation schema with a stream key for identifying its elements. As an abstract object, a pipe can be implemented as aqueue or stream table.

To allow a DCSP to be constructed from stations and pipes recursively, we introduce the notion of Data Continuous Query (DCQ) that is a query applied to a stream data source – a stream table, a station (via pipe) or recursively a DCQ, with well defined data continuation semantics. A DCQ itself can be treated as a station, meaning that stations can be constructed from existing ones recursively in terms of SQL. Based on these notions a DCSP is modeled as a graph of stations (nodes) and pipes (links) and represented by a set of correlated DCQs. Specifying DCSP in SQL allows us to take advantage of SQL in expressing relational transformations on the stream elements, and potentially in pushing DCSP execution down to the database layer for performance and scalability. The implementation issues based on parallel database technology are discussed.

The proposed approach represents a major shift in process management from one-time execution to data stream driven, open-ended execution, and an initial step in bringing BPM technology and database technology together under the data-continuation semantics.

Keywords

Business Process Data Stream Relation Schema Continuous Query Relational Transformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abadi, D., Carney, D., Stonebraker, M., Zdonik, S., et al.: Aurora: a new model and architecture for data stream management. VLDB Journal (2003)Google Scholar
  2. 2.
    Arasu, A., Babu, S., Widom, J.: The CQL Continuous Query Language: Semantic Foundations and Query Execution. VLDB J. (2004)Google Scholar
  3. 3.
    Arasu, B., Babcock, S., Babu, M., Datar, K., Ito, I., Nishizawa, J., Rosenstein, J.: STREAM: The Stanford Stream Data Manager. In: Proceedings of SIGMOD (2003)Google Scholar
  4. 4.
    Avnur, R., Hellerstein, J.M.: Eddies: Continuously adaptive query processing. In: ACM SIGMOD, Dallas, TX (May 2000)Google Scholar
  5. 5.
    Chen, J., DeWitt, D., Naughton, J.: Design and evaluation of alternative selection placement strategies in optimizing continuous queries. In: ICDE, CA (2002)Google Scholar
  6. 6.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Principles of Database Systems (2002)Google Scholar
  7. 7.
    Chen, Q., Hsu, M.: User Defined Partitioning - Group Data based on Computation Model. In: Proc. 10th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2008) (2008)Google Scholar
  8. 8.
    Chen, Q., Hsu, M.: Correlated Query Process and P2P Execution. In: Hameurlain, A. (ed.) Globe 2008. LNCS, vol. 5187, pp. 82–92. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Chen, Q., Hsu, M.: CPM Revisited – An Architecture Comparison. In: Proc. of 10th Int’l Conf on Cooperative Information Systems (Coopis 2002), USA (2002)Google Scholar
  10. 10.
    Chen, Q., Hsu, M.: Inter-Enterprise Collaborative Business Process Management. In: Proc. of 17th Int’l Conf on Data Engineering (ICDE 2001), Germany (2001)Google Scholar
  11. 11.
    Chen, Q., Dayal, U.: Multi-Agent Cooperative Transactions for E-Commerce. In: Proc. Fifth IFCIS Conference on Cooperative Information Systems (CoopIS 2000) (2000)Google Scholar
  12. 12.
    Qiming Chen, P., Chundi, U., Dayal, M., Hsu, M.: Dynamic-Agents for Dynamic Service Provision. In: Proc. of 3rd Int Conf. on Cooperative Information Systems (CoopIS 1998) (1998) Google Scholar
  13. 13.
    Chen, Q., Kambayashi, Y.: Nested Relation Based Database Knowledge Representation. In: Proc. of ACM SIGMOD 1991 (ACM SIGMOD Rec. 20(2)) (1991)Google Scholar
  14. 14.
    Dayal, U., Hsu, M., Ladin, R.: A Transaction Model for Long-Running Activities. In: VLDB 1991 (1991)Google Scholar
  15. 15.
    Gray, J., Liu, D.T., Nieto-Santisteban, M.A., Szalay, A.S., Heber, G., DeWitt, D.: Scientific Data Management in the Coming Decade. SIGMOD Record 34(4) (2005)Google Scholar
  16. 16.
    Hanlon, M., Klein, J., Van der Linden, R., Zeller, H.: Publish/subscribe in NonStop SQL: transactional streams in a relational context. In: ICDE 2004 (2004)Google Scholar
  17. 17.
    Hsu, M., Xiong, Y.: Building a Scalable Web Query System. DNIS (2007)Google Scholar
  18. 18.
    HP Neoview enterprise datawarehousing platform, http://h71028.www7.hp.com/ERC/downloads/4AA0-7932ENW.pdf
  19. 19.
    IBM DB2 Universal Database V8.1 (2004), http://www.ibm.com/software/data/db2
  20. 20.
    IBM, MQSeries An Introduction to Messaging and Queuing, ftp://ftp.software.ibm.com/software/mqseries/pdf/horaa101.pdf
  21. 21.
    Isard, M., Budiu, M., Yu, Y., Birrell, A., Fetterly, D.: Dryad: Distributed data-parallel programs from sequential building blocks. In: EuroSys 2007 (March 2007)Google Scholar
  22. 22.
    Law, Y.-N., Wang, H., Zaniolo, C.: Query languages and data models for database sequences and data streams. In: VLDB 2004 (2004)Google Scholar
  23. 23.
    Madden, S., Shah, M., Raman, J.M.H.V.: Continuously Adaptive Continuous Queries over Stream. ACM-SIGMOD (1992)Google Scholar
  24. 24.
    Carino, F., O’Connell, W.: “Plan-per-tuple Optimization Solution – Parallel Execution of Expensive User Defined Functions in Object-Relational DBMS. In: VLDB (1998)Google Scholar
  25. 25.
    O’Connell, W., et al.: A Teradata Content-Based Multimedia Object Manager for Massively Parallel Architectures. In: ACM-SIGMOD Conf., Canada (1996)Google Scholar
  26. 26.
    Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig Latin: A Not-So-Foreign Language for Data Processing. ACM SIGMOD (2008)Google Scholar
  27. 27.
    Oracle, Oracle Stream, Oracle 9i documentationGoogle Scholar
  28. 28.
    Oracle, Oracle Pipelined Table Functions, Oracle 9i documentationGoogle Scholar
  29. 29.
    Witkowski, A., et al.: Continuous Queries in Oracle. In: VLDB 2007 (2007)Google Scholar
  30. 30.
    Workflow Management Coalition, www.aiim.org/wfmc/mainframe.htm

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Qiming Chen
    • 1
  • Meichun Hsu
    • 1
  1. 1.HP Labs, Hewlett Packard CoPalo AltoUSA

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