1 Mining and Composition of Emergent Collectives in Mixed Service-Oriented Systems IEEE Conference on Commerce and Enterprise Computing (CEC) 10-12 November 2010, Shanghai, China Daniel Schall and Florian Skopik Distributed Systems Group Vienna University of Technology, Austria [email protected][email protected]
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Mining and Composition of Emergent Collectives in Mixed Service-Oriented Systems
Complex service-oriented systems typically span interactions between people and services. Compositions in such systems demand for flexible interaction models. In this work we introduce an approach for discovering experts based on their dynamically changing skills and interests. We discuss human provided services and an approach for managing user preferences and network structures. Experts offer their skills and capabilities as human provided services that can be requested on demand. Our main contributions center around an expert discovery method based on the concept of hubs and authorities in Web-based environments. The presented discovery and interaction approach takes trust-relations and link properties in social networks into account to estimate the hub-expertise of users. Furthermore, we show how our approach supports flexible interactions in mixed service-oriented systems.
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1
Mining and Composition of Emergent Collectives in Mixed Service-Oriented Systems
IEEE Conference on Commerce and Enterprise Computing (CEC) 10-12 November 2010, Shanghai, China
Daniel Schall and Florian Skopik
Distributed Systems GroupVienna University of Technology, Austria
Open and dynamic environment humans and resources (e.g., services) joining/leaving the environment dynamically humans perform activities and tasks
Massive collaboration in SOA/Web 2.0 large number of humans and resources dynamic compositions distributed communication and coordination
Keep track of the dynamics to control future interactions resource selection compositions of actors activity and task assignments
computational
social network model
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Application Scenario: Expert Web
Process model is based on tasks and flow structure Embedding of Web 2.0 collaboration tools Connected experts provide online help and support
[IS10] F. Skopik, D. Schall, and S. Dustdar. Modeling and mining of dynamic trust in complex service-oriented systems. Information Syst., 2010.
conceptual draftprintout and
delivery
symbol library
CAD drawing
Process: CAD Drawing
conversionand archive
WSDL
Symbols:
human
software service
expert service (general)
expert service prov. by human
expert service implemented in software
WSDL
WSDL
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Expert Discovery in Crowdsourcing
How do actor discovery and selection mechanisms work? What is the technical grounding for the proposed system? How can actors be flexibly involved in a service-oriented
manner? How do interactions and behavior influence future
discovery?
4Expert Crowd
1) discovery and selection
2) delegations
s
x
r
q
u
w
z
yt
v Symbols:
expert
expertise area
network relation
RFS
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Hubs and Authorities
On the Web Hubs: pointing to authoritative pages Authorities: are being referenced by other important
pages→ Recursive definition
In social/collaborative networks Hubs: information “brokers” distributing work Authorities: experts processing received work
Based on HITS algorithm (Kleinberg, J. ACM 1999)5
WSDL
WSDL
WSDL
H1
H2
H3
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Human-Provided Services
Mixed System Mix of human- and software services collaboration Humans provide services using SOA concepts
Human-Provided Services (HPS) User contributions as services
Service description with WSDL Communication via SOAP messages
Example: Document Review Service Input: document, deadline Output: review comments
[EEE08] D. Schall, H.-L. Truong, S. Dustdar. The Human-Provided Services Framework. IEEE 2008 Conference on Enterprise Computing, E-Commerce and E-Services (EEE), Crystal City, Washington, D.C., USA, 2008. IEEE. 6