1. Semantic Event-based Service Oriented Architecture

2. Concept
We will develop and use secure semantic event-based service oriented architecture as a common infrastructure for our testbeds and prototype integrated applications.
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3. Team Faculty Interactions with other groups Core Students Anupam Joshi
Tim Finin
Interactions with other groups
Ravi Sandhu
C Zhai
Core Students
Kishor Datar
Audumbar Chormale
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4. Key Goals Infrastructure for Testbed Research
Adding Semantics and our Policy mechanisms to WSDL, WS-Policy, WS-Security etc.
Creating a system to capture events, infer context, and distribute it to the policy, information management, and social networking components.
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5. Use Open Source WS Frameworks
Axis is essentially a SOAP engine - a framework for constructing SOAP processors such as clients, servers, gateways, etc.
Axis also includes:
A simple stand-alone server
A server which plugs into servlet engines such as Tomcat
Extensive support for the Web Service Description Language (WSDL)
Tool that generates Java classes from WSDL.
Some sample programs
A tool for monitoring TCP/IP packets
Open source webservices frameworks such as WSO2 based on Axis that allow webservices to be written in C/C++, Perl, Ruby, …
Source:
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6. Adding Semantics to WSDL
The Web Services Description Language (WSDL)
Way to describe the abstract functionalities of a service and concretely how and where to invoke it.
WSDL 2.0 W3C Recommendation does not include semantics in the description
Two services can have similar descriptions while meaning totally different things, or they can have very different descriptions yet similar meaning
The Semantic Annotations for WSDL and XML Schema (SAWSDL)
Mechanisms to add semantic annotations to WSDL components.
Provides mechanisms by which concepts from the semantic models that are defined either within or outside the WSDL document can be referenced from within WSDL components as annotations.
Disambiguate the description of Web services during automatic discovery and composition of the Web services.
Source:
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7. Context Service
Policies will be evaluated “in context” – the need to share is a function of the state of the world
Should your medical records be proactively shared with EMTs that are responding to a natural disaster in your region ? Only if they are treating you
Similar considerations for Information Management and Knowledge Discovery and Social Networks
Two key issues
Inferring context in face of streaming knowledge
Distributing context information
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8. Inferring Context – a surgical scenario
Patient Monitor
Stream KB
Processor
(TelegraphCQ)
Continuous
Context Queries
Knowledge
Base
Assert facts
Assert Events
RFID
System
Medicines
Tools
Staff
Context
Aware
Agent
Database
Medical
Encounter
Record
Patient
History
Staff
Medical
Supplies

9. CQs on Semantic Streaming Data…
Reasoning process involves computationally heavy techniques such as graph traversals.
In case of streaming data, the incoming data rate is much higher than time taken by the reasoner for inferencing.
Hence the need for combining streaming data handling systems with reasoning techniques to build a Streaming Knowledge Base.
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10. Preliminary Steps
Combining the continuous query processors with Semantic Web techniques to build a partial RDFS and OWL reasoner that can deal with streaming data.
Comparing the performance of our approach with that of Jena and show that our approach achieves a soft real-time performance for fast streaming data.
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11. Pre-processing of an ontology
RDFS or OWL ontology
Static Data Store
Property - subproperty
Propertytree
Class - subclass
Classtree
Ontology
Processor
Predicate - range
RangeInfo
DomainInfo
Predicate - domain
InverseInfo
Property - inverse
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12. Pre-processing of an ontology
rdfs:subClassOf –
e.g. “Mammal” class is a subclass of “Animal” class.
It’s transitive in nature.
rdfs:subPropertyOf –
e.g. “fatherOf” property is a sub-property of the property “parentOf”.
rdfs:domain –
e.g. the domain of the property “fatherOf” is “Male” class.
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13. Pre-processing of an ontology
rdfs:range –
e.g. the range of the property “square” is “non-negative number”.
owl:inverseOf –
e.g. the inverse of the property “parentOf” is “childOf” property.
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14. Stream Processing Choice of stream processing engine TelegraphCQ –
Accepts a stream of tuples as input.
Allows a join between static tables and dynamic streams.
Can run in two modes –
PostgreSQL mode - runs as a normal DBMS system.
TelegraphCQ mode – can issue continuous queries to the stream.
Allows Windowed queries in two ways
Landmark Window – the start point of the window is fixed whereas the end point moves ahead continuously.
Sliding Window – the start point as well as the end point of the window move ahead continuously.
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15. Query for Class of Concern
Stream Processing
Query for Class of Concern
Input Triple Stream
Detected Instances
Input
Stream
Handler
Event
Detector
Enhanced Stream
Static Data Store
RangeInfo
DomainInfo
Classtree
PropertyTree
InverseInfo
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16. Comparing TCQ approach to Jena approach
Different ontology sizes (from 118 KB to 23.1 MB)
Classes with varying numbers of subclasses (from 49 to 56,731).
Classes with different depths of subclasses (from 2 to 9).
Different data rates (from 1 triple per second to 100 triples per second).
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17. Comparative Graphs
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18. Comparative Graphs
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19. Comparative Graphs
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20. Publish/Subscribe mechanism for webservices
Asynchronous messaging paradigm with lots of existing research and implementations
Published messages classified into classes, without knowledge of what subscribers there may be
Subscribers express interest in classes, and receive messages that are of interest, without knowledge of what publishers there are
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21. Problems in the Semantic Context World
How do you describe classes of interest, which are elements in some ontology?
A SPARQL based approach combines expressive power and efficiency
SPARQL query defines trigger event of interest and additional context properties to be reported
Results are RDF “graphlet” which satisfies that query and contains additional information
A component can add graph result to its KB for processing
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22. Extending SPARQL
RDF (including OWL and RIF) will be the standard data and knowledge interchange formalism
SPARQL has good support for queries
We’ll develop and implement extensions supporting other speech acts as needed, including
Request
Inform
Negotiation actions
Etc.
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