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Bridging Socially-Enhanced Virtual Communities
SAC 2011, March 21-24, 2011, Taichung, Taiwan
Daniel SchallFlorian Skopik, Harald Psaier
Schahram Dustdar
Distributed Systems GroupVienna University of Technology, Austria
Open dynamic ecosystems People and software services
integrated into evolving “solutions“
Communications and coordination „Anytime-anywhere“ pervasive
infrastructures and mobility
Mass collaboration Knowledge sharing and
social interaction
MotivationParadigm: human and service interactions
… software service
… user
… human/service interaction
Human-Provided Services (HPS)
User contributions modeled as services Users define their own services (!= WS-HT) Reflect willingness to contribute
Technical realization Service description with
WSDL (capabilities) Communication via SOAP messages
Example: Document Translation Service Input: original document, deadline, constraints Output: translated text
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.
HPS
v
w
u
serviceprovider
Collaborative Environment
Collaborations and activities A concept to structure information in flexible
collaboration including the goal of ongoing tasks Involved actors, and utilized resources such as
documents or services
Monitoring of interactions Activity-based events (assignment, delegation, …) SOAP-based interactions (HPS)
Relations emerge from interactions Bound to particular scopes (expertise areas) Context in which interactions take place
tags applied to various artifacts4
Professional Virtual Communities
Various member groups collaborate in the context of different activities
These groups intersect since members may participate in different activities
Expert groups: the creation of new specifications or the discussion of future technology standards
Problem: missing expertise or know-how Idea: brokers bridge gaps (i.e. structural holes)
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Dynamic Brokers
Social relations (FOAF) between members
Clusters Actors u, v, w Actors j, k, l, m
Broker Actor u knows b and b knows j Broker bridges two separated clusters
Finding brokers Social network analysis Network metrics: shortest path and betweenness
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Broker Discovery
How to specify discoverypolicies? A broker (e.g., b) should
be connected to j but not to ‘some other actor’
The broker should betrusted by the community j, k, l, m
Should be trusted by at least one of them, all of them, …
How to discover new brokers Metrics and monitoring Weighted collaboration (social network) links
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Querying Social Networks
The social network graph Sets of nodes and edges Node attributes (-> user profiles) Edge attributes (-> relationship metrics)
Querying graph data SPARQL (Query Language for RDF)
But … Runtime logs must be mapped into “semantic layer”
(service infrastructure) How to define complex queries filtering edges, nodes
based on metrics?
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BQDL - Broker Query and Discovery Language (1/2)
Domain specific language To query social network data To find brokers that connect independent communities Metrics (link weights) Filters (nodes, edges)
SQL-like syntax Select … From … Where Add graph specific features Select node From G Where [Filter]
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BQDL - Broker Query and Discovery Language (2/2)
Example query: find broker to connect two predefined communities
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Select nodes connected to source community (potential brokers)
Select nodes connected to source community (potential brokers)
Broker must be directly connected to targetBroker must be directly connected to target
Prototype and Evaluation
Simulation environment Testbed to simulate dynamic interactions Distributed service-oriented environment SOAP-based logs + context identifiers (tags, …)
Performance study To test scalability of BQDL query stack implementation Concurrent processing of complex queries (see paper for detailed results)
Broker discovery tool Analyze properties of discovered brokers Visual frontend to test effectiveness of queries
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G2 (Genesis) Simulation Environment
Testbed for simulating behavior of services (HPSs and SBSs) To obtain interaction logs
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A WS Framework with: 3L: Services (Clients,
Registries, Brokers) 2L: Control Layer (Models,
Configuration) 1L: Plug-ins (Extensions,
e.g., logging, routing, adaptation language)
0L: Front-End and Back-End
Juszczyk L., Dustdar S. (2010). Script-based Generation of Dynamic Testbeds for SOA. 8th IEEE International Conference on Web Services (ICWS'10),5.-10. July 2010, Miami, USA.
Broker Discovery Tool
Search by tags Expertise areas Metrics (e.g., trust
threshold)
Broker View Brokers (blue nodes)
centered around query term (‘robustness’)
Communities attached to broker
Adapt visualization by adjusting thresholds
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Conclusion
Human participation in SOA Flexible interactions between HPSs and SBSs Monitoring of interactions
Broker patterns Shared and exclusive brokers Reputation management
Broker Query and Discovery Language SQL-based syntax Domain specific query language to indentify brokers Discovery policies Support of rich set of metrics