NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Preference-based Discovery of Dynamically Generated Service Offers ∗
Maciej Zaremba, DERI, National University of Ireland, Galway
maciej.zaremba@deri.org
Abstract
Service discovery and selection are active research topics
in Service Computing area. However, the majority
of service discovery and selection approaches operate on
coarse-grained service descriptions. We argue that dynamically
created fine-grained service offer descriptions are far
more beneficial to service consumers in contrast to coarsegrained
service descriptions. We define a preference-based
discovery model which operates on fine-grained search
requests and dynamically generated service offers. Our
search requests can express hard constraints, rich preferences,
and flexible input parameters. We use a combination
of utility functions and weighted rules for modeling users’
rich preferences. Our service model does not require background
in logics and can be used by web developers. We
apply our results to an international shipping scenario in
the experiment to prove the feasibility and usefulness of our
approach in a realistic scenarios. We also examine commercial
potential of our approach.
1 Introduction
Service descriptions are core artefacts used in the service
discovery and selection process. The quality of results
relies on the quality of the provided descriptions. However,
service descriptions are often underspecified and incomplete
what hinders service discovery process. We distinguish
two types of service descriptions: coarse-grained
service descriptions that represent service functionalities in
terms of service category, execution effects, and types of
input and outputs parameters, and fine-grained service offer
descriptions that represent concrete service offers created
for an individual service consumer. The majority of
existing service discovery and selection approaches operate
on coarse-grained service descriptions. From the point of
view of service consumers, discovery of service offers is
far more beneficial than discovery on the level of coarsegrained
service descriptions. Only descriptions of service
offers can satisfy the concrete needs of service consumers,
while coarse-grained service descriptions require further,
often manual examination.
∗ This work is supported by the Science Foundation Ireland under Grant
No. SFI/08/CE/I1380 (Lion-2).
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2 Service Offer Discovery (SOFFD)
We define a novel discovery approach called Service OF-
Fer Discovery (SOFFD) that includes: (1) search requests
which specify hard constraints and rich preferences as rules
and utility functions, (2) service descriptions with rules
describing relationships between service input and output
parameters, (3) rule-based service interfaces for fetching
service offer parameters at discovery time, and (4) discovery
algorithm that utilises above mentioned search requests
and service descriptions.
Over the last years we have observed a growing adoption
of Linked Data principles and growth of datasets specified
in RDF. Linked Data is the set of best practices for
publishing and interconnecting structured data on the Web.
Linked Data is published using RDF where URIs are the
means for connecting and referring between various entities
on the Web. Service Computing community can benefit
from the Linked Data and we capitalise on the Linked
Data by grounding our conceptual model in the combination
of RDF and SPARQL. We use RDF as a lightweight language
for describing and linking various entities, whereas
we use SPARQL as a rule language. We argue that there
is a strong need for lightweight RDF-based services in the
Linked Open Data landscape.
For the evaluation purposes and as a running example,
we use the shipping discovery scenario. In this scenario,
the best choice of a shipping service depends on customer’s
shipping preferences on detailed shipping service offers.
We chose shipping services as they are highly configurable
and their up-to-date, detailed descriptions cannot be easily
provided. Service offers in our discovery approach are
dynamically generated for individual service requests. We
have qualitatively evaluated our approach against other service
discovery approaches which tackled the same shipping
discovery problems.
We demonstrate an application of proposed approach in
an alpha prototype of a search engine comparing shipping
rates and delivery options across several package carriers
(FedEx, TNT, GLS, EcoParcel, AnPost and more). We discuss
the business value of our approach, possible revenue
model, existing competitors, and potential for commercialisation.