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Automating Service Usage with Preferences Integration

This paper discusses the integration of preferences into service requests to automate service usage and enhance efficiency and robustness of dynamic service binding. It proposes an unbiased matching approach using fuzzy object sets to determine the degree of membership between service requests and offers.

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Automating Service Usage with Preferences Integration

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  1. First AKT Workshop on Semantic Web Services Milton Keynes, UK, December 8, 2004 Integrating Preferences into Service Requests to Automate Service Usage Michael KleinInstitute for Program Structures and Data Organization Universität Karlsruhe, Germany Birgitta König-RiesInstitute of Computer ScienceFriedrich-Schiller-Universität Jena, Germany DIANE Project http://www.ipd.uni-karlsruhe.de/DIANE/en

  2. Dynamic Service Binding Business Process Application Service C Service D Service A • Agile Networks • change inefficient or unavailable service providers on runtime •  Enhance efficiency and robustness Service B

  3. Matcher invoke [0..1] State of the Art • How is this typically achieved? • Attach service description to application and services • In case of a call, a matcher calculates similarity between request and all offers • Best offer is chosen and invoked Application Description of the desired service Description of the offered service Service B

  4. Difficulties EASY: • If descriptions are exactly equal: Matcher returns 1.0 • If descriptions are obviously different: Matcher returns 0.0 DIFFICULT: • In the common intermediate situations: • The offer differs somewhat from the request • What value in (0,1) is appropriate?

  5. Example for a Service Request :Service Service Request: I want a Service which books me a seat for Spider Man 2 presents :Profile :Booked effect entity price :CinemaTicket <= 8.00 validFor 2004-07-10 date spiderman2:Movie :SeatInShow visible 20:00 time cinema hortonPlaza:Cinema

  6. Example for a Service Offer :Service Service Offer: I can book you a ticket for Spider Man 2, this saturday at 8:15 pm in the Cinerama 6. presents :Profile :Booked effect entity :CinemaTicket validFor 2004-07-10 date spiderman2:Movie :SeatInShow visible 20:15 time cinema cinerama6:Cinema

  7. time :SeatInShow 20:00 The requestor wanted …but the offer can only book a ticket for 20:15. This is not ok, or is it? So assign 0.9? price :CinemaTicket <= 8.00 cinema The requestor wanted :SeatInShow hortonPlaza:Cinema The requestor wanted …but this information is missing in the offer. Return 0.0 and skip the service? Or just assign 0.5? Or 1.0? …but the offer can only book a ticket in the Cinerama 6. Is this ok? Say yes, because it‘s in the same city  Assign 0.8? Is it a Match? What is more important for the offerer: A good price, a good time, a near cinema?  Simply take the average, or the minimum, or…?

  8. Main Problem Main Problem Matcher does not know the preference/tolerance of the requestor. The Matcher • has to use general deviation heuristics • or is very conservative and only allows exact matchs • biased matching • often provides manyor no matching offers • Requestor has to choose manually or blindly rely on it.  Cannot or is not used for automatic service binding.

  9. Our Approach • Tell the matcher exactly what the preferences are FROM TO offers request offers Generic Matcher Personal Matcher creates request - generic - based on heuristics  biased - personal - only based on the specified preferences  unbiased

  10. Preference-Containing Requests • Request != single instance describing the perfect service • But: Set of suitable services • degrees of membership from (0,1] determines preference for this service

  11. represents a set of movies Membership conditions are defined in a structured manner Movie Introduction of Object Sets Movie contains all existing movies spiderman2 represents one single movie

  12. Cinema Request (Revisited) Service presents Profile Booked effect entity Double price CinemaTicket ~<= 8.00 validFor Date date Movie SeatInShow == 2004-07-10 visible ==spiderman2 min(date, time^2, cinema, visible) time Time cinema Cinema ~< 20:00 <= 21:00 near(hortonPlaza)

  13. Unbiased Matching • Now: Unbiased Matcher • Matching = Calculating the membership value of the request set • Matcher exactly takes the preferences of the requestor into account  Personal Matcher •  Automatically gained matching result is accepted.

  14. Summary • Main Goal of Service Oriented Computing: Dynamic Service Binding • Up to now: Generic Matcher tests similarity between the offer instance and the request as perfect service instance •  Problem: Unknown preferences of the requestor in case of deviations •  Matcher uses heuristics and is biased •  Result is not accepted and must be postprocessed manually • Our Thesis: Tell the matcher exactly what the preferences are • Possible technique: Fuzzy object sets • Matching is test on set membership unbiased •  Result is accepted without further manual processing

  15. Current & Further Work • Service descriptions can contain variables. How to match them?Can we derive binding from request? Currently finishing the implementing the matcher. • Difficult for the requestor to adjust the conditions and strategies Guide user through process • How to evaluate the approach? Other approaches?

  16. DIANE Project http://www.ipd.uni-karlsruhe.de/DIANE/en Thank you for your attention!

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