A hierarchical framework for logical composition of web services

Original Research Paper


Automatically composing Web services to form processes in the context of service-oriented architectures has attracted significant research. Prevalent approaches for automatically composing Web services predominantly utilize planning techniques to achieve the composition. However, classical planning based approaches face the following challenges: (i) difficulty in modeling the uncertainty of Web service invocations, (ii) inability to optimize the composition using non-functional parameters, and (iii) difficulty in scaling efficiently to large compositions. In order to address these issues, we present a hierarchical framework for logically composing Web services, which we call Haley. In comparison to classical planners, Haley utilizes decision-theoretic planning that is able to model and reason with the uncertainty inherent in Web service invocations and provides an expected cost-based optimization. Haley uses symbolic planning techniques that operate directly on first-order logic based representations of the state space to obtain the compositions. Consequently, it supports automated elicitation of the corresponding planning problem from Web service descriptions and produces a domain representation that is more compact than that of classical planners. Furthermore, it promotes scalability by exploiting the natural hierarchy found in real-world processes. Due to the limitations of the existing approaches and the complexity of the Web service composition problem, few implemented tools exist, although many approaches have been proposed in the literature. We have implemented Haley and provided a comprehensive tool suite for composing Web services. The suite operates on Web services described using well-known languages such as SAWSDL. It provides process designers with an intuitive interface to specify composition requirements, goals and a hierarchical decomposition if available, and automatically generates BPEL compositions while hiding the complexity of the planning and of BPEL from users.


Web service composition Decision-theoretic planning First-order logic Hierarchy Probability 


  1. 1.
    Singh M, Huynhs M (2005) Service-oriented computing: semantics, processes and agents. Wiley, New YorkGoogle Scholar
  2. 2.
    Gudgin M, Hadley M, Mendelsohn N, Moreau JJ, Nielsen HF, Karmarkar A, Lafon Y (2007) Simple object access protocol (soap), version 1.2. http://www.w3.org/tr/soap12-part1
  3. 3.
    Chinnici R, Moreau JJ, Ryman A, Weerawarana S (2007) Web services description language (wsdl), version 2.0. http://www.w3.org/tr/2007/rec-wsdl20-20070626
  4. 4.
    Wu D, Parsia B, Sirin E, Hendler JA, Nau DS (2003) Automating DAML-S web services composition using SHOP2. In: International aemantic web conference (ISWC), pp 195–210Google Scholar
  5. 5.
    Kuter U, Sirin E, Nau D, Parsia B, Hendler J (2005) Information gathering during planning for web serivce composition. J Web Semant 3: 183–205Google Scholar
  6. 6.
    Rao J, Su X (2004) A survey of automated web service composition methods. In: Workshop on semantic web services and web process composition (SWSWPS), pp 43–54Google Scholar
  7. 7.
    McIlraith S, Son TC (2002) Adapting Golog for composition of semantic web services. In: International conference on principles and knowledge representation and reasoning (KR-02), Toulouse, France, pp 482–496Google Scholar
  8. 8.
    Medjahed B, Bouguettaya A, Elmagarmid AK (2003) Composing web services on the semantic web. VLDB J 12(4): 333–351CrossRefGoogle Scholar
  9. 9.
    Traverso P, Pistore M (2004) Automated composition of semantic web services into executable processes. In: International semantic web conference (ISWC), pp 380–394Google Scholar
  10. 10.
    Pistore M, Marconi A, Bertoli P, Traverso P (2005) Automated composition of web services by planning at the knowledge level. In: International joint conferences on artificial intelligence (IJCAI), pp 1252–1259Google Scholar
  11. 11.
    Oh SC, Lee D, Kumara SRT (2007) Web service planner (wspr): an effective and scalable web service composition algorithm. Int J Web Serv Res (JWSR) 4: 1–22Google Scholar
  12. 12.
    Qiu L, Chang L, Lin F, Shi Z (2007) Context optimization of ai planning for semantic web services composition. J Serv Oriented Comput Appl 1(2): 117–128CrossRefGoogle Scholar
  13. 13.
    Bylander T (1991) Complexity results for planning. In: International joint conference of artificial intelligence (IJCAI), pp 274–279Google Scholar
  14. 14.
    Blythe J (1999) Decision-theoretic planning. AI Mag 20(2): 37–54Google Scholar
  15. 15.
    Bellman RE (1957) Dynamic programming. Dover, New YorkGoogle Scholar
  16. 16.
    Doshi P, Goodwin R, Akkiraju R, Verma K (2005) Dynamic workflow composition: using markov decision processes. J Web Serv Res (JWSR) 2(1): 1–17Google Scholar
  17. 17.
    Zhao H, Doshi P (2006) A hierarchical framework for composing nested web processes. In: International conference on service oriented computing (ICSOC), pp 116–128Google Scholar
  18. 18.
    Martin DL, Burstein MH, McDermott DV, McIlraith SA, Paolucci M, Sycara KP, McGuinness DL, Sirin E, Srinivasan N (2007) Bringing semantics to web services with OWL-S. In: International world wide web conference (WWW), pp 243–277Google Scholar
  19. 19.
    Farrell J, Lausen H (2006) SAWSDL: semantic annotations for wsdl. http://www.w3.org/tr/sawsdl/
  20. 20.
    der Aalst WV, Hee KV (2004) Workflow management: models, methods and systems. MIT Press, CambridgeGoogle Scholar
  21. 21.
    Puterman M (1994) Markov decision processes: discrete stochastic dynamic programming. Wiley-Interscience, LondonMATHGoogle Scholar
  22. 22.
    Boutilier C, Reiter R, Price B (2001) Symbolic dynamic programming for first-order MDPs. In: International joint conferences on artificial intelligence (IJCAI), pp 690–700Google Scholar
  23. 23.
    Holldobler S, Skvortsova O (2004) A logic-based approach to dynamic programming. In: Learning and planning in Markov processes-advances and challenges-AAAI 04 workshop, pp 31–36Google Scholar
  24. 24.
    Kersting K, Otterlo MV, Raedt LD (2004) Bellman goes relational. In: Twenty-first international conference on machine learning (ICML), pp 465–472Google Scholar
  25. 25.
    Hirtle D, Boley H, Grosof B, Kifer M, Sintek M, Tabet S, Wagner G (2006) Schema specification of RuleML. http://www.ruleml.org/0.91/
  26. 26.
    McCarthy J (1963) Situations, actions and causal laws. Technical report, AI Laboratory, Stanford UniversityGoogle Scholar
  27. 27.
    Reiter R (2001) Knowledge in action: logical foundations for specifying and implementing dynamic systems. MIT Press, CambridgeGoogle Scholar
  28. 28.
    Sanner S, Boutilier C (2005) Approximate linear programming for first-order MDPs. In: Twenty-first conference in uncertainty in artificial intelligence, pp 509–517Google Scholar
  29. 29.
    Ludwig H, Keller A, Dan A, King R, Franck R (2003) Web service level agreement (wsla) language specification. http://www.research.ibm.com/wsla
  30. 30.
    Martin D, Burstein M, Hobbs J, Lassila O, McDermott D, McIlraith S, Narayanan S, Paolucci M, Parsia B, Payne T, Sirin E, Srinivasan N, Sycara K (2006) OWL-S: semantic markup for web services. http://www.daml.org/services/owl-s/1.1
  31. 31.
    Andrieux A, Czajkowski K, Dan A, Keahey K, Ludwig H, Nakata T, Pruyne J, Rofrano J, Tuecke S, Xu M (2007) Web services agreement specification (ws-agreement). http://forge.gridforum.org/sf/projects/graap-wg
  32. 32.
    Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M (2004) Swrl: a semantic web rule language combining owl and ruleml. http://www.w3.org/submission/swrl
  33. 33.
    Boutilier C, Reiter R, Soutchanski M, Thrun S (2000) Decision-theoretic, high-level agent programming in the situation calculus. In: Seventeenth conference on artificial intelligence, pp 355–362Google Scholar
  34. 34.
    Cimatti A, Pistore M, Roveri M, Traverso P (2003) Weak, strong, and strong cyclic planning via symbolic model checking. Artif Intell 147(1–2): 35–84MATHMathSciNetGoogle Scholar
  35. 35.
    Morell J, Swiecki B (2001) E-readiness of the automotive supply chain: just how wired is the supplier sector. Technical report, Center for Automotive Research, Center for Electronic Commerce, ERIMGoogle Scholar
  36. 36.
    Turing A (1936) On computable numbers, with an application to the entscheidungs problem. Proc Lond Math Soc 42: 230–265MATHCrossRefGoogle Scholar
  37. 37.
    Nau DS, Au TC, Ilghami O, Kuter U, Murdock JW, Wu D, Yaman F (2003) SHOP2: an HTN planning system. J Artif Intell Res (JAIR) 20: 379–404MATHGoogle Scholar
  38. 38.
    Sirin E, Parsia B, Wu D, Hendler JA, Nau DS (2004) HTN planning for web service composition using SHOP2. J Web Semant 1(4): 377–396Google Scholar
  39. 39.
    McIlraith SA, Son TC, Zeng H (2001) Semantic web services. IEEE Intell Syst 16: 45–53CrossRefGoogle Scholar
  40. 40.
    Benatallah B, Sheng QZ, Dumas M (2003) The Self-Serv environment for web services composition. IEEE Internet Comput 7(1): 40–48CrossRefGoogle Scholar
  41. 41.
    Aggarwal R, Verma K, Miller JA, Milnor W (2004) Constraint driven web service composition in METEOR-S. In: IEEE international conference on services computing (SCC), pp 23–30Google Scholar
  42. 42.
    Cardoso J, Miller J, Sheth A, Arnold J (2004) Quality of service for workflows and web service processes. J Web Semant 1: 281–308Google Scholar
  43. 43.
    Zeng L, Benatallah B, Dumas M, Kalagnanam J, Sheng QZ (2003) Quality driven web services composition. In: International world wide web conference (WWW), pp 411–421Google Scholar
  44. 44.
    Canfora G, Esposito R (2004) A lightweight approach for QoS-aware service composition. In: Second international conference on service oriented computing (ICSOC), pp 36–47Google Scholar
  45. 45.
    Wiesemann W, Hochreiter R, Kuhn D (2008) A stochastic programming approach for QoS-aware service composition. In: IEEE international symposium on cluster computing and the grid (CCGrid), pp 226–233Google Scholar
  46. 46.
    Agarwal V, Chafle G, Dasgupta K, Karnik NM, Kumar A, Mittal S, Srivastava B (2005) Synthy: a system for end to end composition of web services. J Web Semant 3(4): 311–339Google Scholar
  47. 47.
    Chafle G, Das G, Dasgupta K, Kumar A, Mittal S, Mukherjea S, Srivastava B (2007) An integrated development environment for web service composition. In: IEEE international conference on web services (ICWS), pp 839–847Google Scholar
  48. 48.
    Nagarajan M, Verma K, Sheth AP, Miller JA (2007) Ontology driven data mediation in web services. Int J Web Serv Res (JWSR) 4(4): 104–126Google Scholar
  49. 49.
    Rohanimanesh K, Mahadevan S (2001) Decision-theoretic planning with concurrent temporally extended actions. In: Uncertainty in artificial intelligence (UAI), pp 472–479Google Scholar
  50. 50.
    Kiepuszewski B, ter Hofstede AHM, Bussler C (2000) On structured workflow modelling. In: Conference on advanced information systems engineering (CAiSE), pp 431–445Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.LSDIS Lab, Department of Computer ScienceUniversity of GeorgiaAthensUSA

Personalised recommendations