Seminar presented by Visiting Professor Adrian Paschke from Freie Universitaet Berlin as part of the BPM EduNet (http://bpmedu.net) staff exchange at University of Toronto, McGill University, Ryerson University, University of Ontario Institute of Technology
Semantic CEP with Reaction RuleML, Keynote at 8th International Web Rule Symposium (RuleML 2014) @ ECAI 2014, Prague, Czech Republic, August 18-22, 2014
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Semantic Complex Event Processing at Sem Tech 2010Adrian Paschke
Semantic Complex Event Processing - The Future of Dynamic IT
Presentation by Paul Vincent, Adrian Paschke, Harold Boley
at the RuleML Semantic Rules Track of the Semantic Technologies Conference 2010 (SemTech 2010), San Francisco, CA, USA
http://semtech2010.semanticuniverse.com/rules
Seminar about Semantic Complex Event Processing and Reaction RuleML presented at the School of Computer Science at McGill University on Sept. 9th, 2013 as part of the Transatlantic Business Process Management Education Network (http://bpmedu.net/) and presented at the DemAAL 2013 - Dem@Care Summer School on Ambient Assisted Living, 16-20 September 2013, Chania, Crete, Greece.
Recent trends of Big Data and the Internet of Things pose challenges to our current computational paradigms such as event processing systems. While three dimensions of Big Data are identified including Volume, Variety and Velocity, we think that more attention shall be given to the Variety aspect within distributed and event based systems, as it can have profound challenges to our computational systems. Event-based systems follow an interaction model based on three decoupling dimensions: space, time, and synchronization. However, event producers and consumers are tightly coupled by event semantics: types, attributes, and values. That limits scalability in large scale heterogeneous environments such as the Internet of Things (IoT) due to difficulties to establish semantic agreements on such scales. This tutorial studies this problem from various perspectives and investigates the suitability of semantic models such as vector space models for tackling the issue.
Semantic CEP with Reaction RuleML, Keynote at 8th International Web Rule Symposium (RuleML 2014) @ ECAI 2014, Prague, Czech Republic, August 18-22, 2014
Tutorial: Deliberation RuleML, Reaction RuleML, and LegalRuleML: Specification and Application - Part 2: Adrian Paschke - Reaction RuleML; in Adrian Paschke, Harold Boley, Zhili Zhao, Kia Teymourian and Tara Athan. Reaction RuleML 1.0: Standardized Semantic Reaction Rules, 6th International Conference on Rules (RuleML 2012) ECAI 2012, Montpellier, France, August 27-31, 2012.
Semantic Complex Event Processing at Sem Tech 2010Adrian Paschke
Semantic Complex Event Processing - The Future of Dynamic IT
Presentation by Paul Vincent, Adrian Paschke, Harold Boley
at the RuleML Semantic Rules Track of the Semantic Technologies Conference 2010 (SemTech 2010), San Francisco, CA, USA
http://semtech2010.semanticuniverse.com/rules
Seminar about Semantic Complex Event Processing and Reaction RuleML presented at the School of Computer Science at McGill University on Sept. 9th, 2013 as part of the Transatlantic Business Process Management Education Network (http://bpmedu.net/) and presented at the DemAAL 2013 - Dem@Care Summer School on Ambient Assisted Living, 16-20 September 2013, Chania, Crete, Greece.
Recent trends of Big Data and the Internet of Things pose challenges to our current computational paradigms such as event processing systems. While three dimensions of Big Data are identified including Volume, Variety and Velocity, we think that more attention shall be given to the Variety aspect within distributed and event based systems, as it can have profound challenges to our computational systems. Event-based systems follow an interaction model based on three decoupling dimensions: space, time, and synchronization. However, event producers and consumers are tightly coupled by event semantics: types, attributes, and values. That limits scalability in large scale heterogeneous environments such as the Internet of Things (IoT) due to difficulties to establish semantic agreements on such scales. This tutorial studies this problem from various perspectives and investigates the suitability of semantic models such as vector space models for tackling the issue.
Presentation from reactconf 2014 in San Francisco.
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Why Data Virtualization Is Good For Big Data Analytics?Tyrone Systems
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Presentation about Reaction RuleML at the Ontology, Rules, and Logic Programming for Reasoning and Applications (RulesReasoningLP) Session at the Ontolog Forum, 9 January 2014
Presentation from reactconf 2014 in San Francisco.
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Comparative Analysis of Personal FirewallsAndrej Šimko
This thesis describes the analysis of 18 personal firewalls. It discovers the differences in their behaviour while they are under various techniques of port scanning and Denial of Service (DoS) attacks. With port scanning, the detection ability, time consumption, leaked port states and obfuscation techniques are analysed. With using different DoS attacks, performance measurements of CPU and network adapter are taken. The potential of firewall fingerprinting based on the different behaviour across multiple products is also addressed.
Why Data Virtualization Is Good For Big Data Analytics?Tyrone Systems
What Is Data Virtualization ?
Data virtualization, like any virtualization, is an approach that allows you to access, administer, and optimize a heterogeneous infrastructure as if it were a single, logically unified resource. This enables you to abstract the external interface from the internal implementation of some service, functionality, or other resource.
The RuleML Perspective on Reaction Rule StandardsAdrian Paschke
Presentation about Reaction RuleML at the Ontology, Rules, and Logic Programming for Reasoning and Applications (RulesReasoningLP) Session at the Ontolog Forum, 9 January 2014
Four ways to represent computer executable rulesJeff Long
July 27, 2008: "Four Ways to Represent Computer-Executable Rules". Presented at InterSymp 2008 conference sponsored by the International Institute for Advanced Studies
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Monitoring and Instrumentation Strategies: Tips and Best Practices - AppSphere16AppDynamics
Monitoring is complicated, and in most organizations consists of far too many tools owned by too many teams. Fixing monitoring issues requires people, process, and technology. Hear common issues seen in the real world including what should be monitored or collected from a technology and a business perspective.
Investigate what instrumentation is most scalable and effective across languages, commonly used APIs, and possibilities for capturing data from common languages like Java, .NET, and PHP. Cover browser and mobile instrumentation techniques. Get tips on which APIs to use, what open source tools and frameworks can be leveraged, and how to coordinate and communicate requirements across your organization.
Key takeaways:
o What is instrumentation, and what to instrument, collect, and store
o How this can be accomplished on common software stacks
o How to work with application owners to collect business data
o How correlation works in custom open source or packaged monitoring tools
For more information, go to: www.appdynamics.com
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Existing tools to specify RM-ODP based system architectures include UML tools with ODP plugin, since one of the standards defines UML Profile for it. The presenter received an email regarding a more accessible and standalone ODP tool, which eventually initiated this project. It is a work in progress, Eclipse/Sirius-based tool. An overview of the tool and an experience of the development done so far will be presented.
Akira Tanaka, view5 LLC
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Rulelog is in process of industry standardization via RuleML and W3C:
RIF-Rulelog specification, version of of May 24, 2013, Michael Kifer, ed. RIF-Rulelog is a powerful dialect of W3C Rule Interchange Format (RIF) that is in draft as a submission from RuleML to W3C.
Several industry standards in the areas are based heavily on our team’s contributions to the authoring/editing of the specifications and conducting the underlying research and earlier-phase standards design. These include most notably the two most important industry standards on rules knowledge:
W3C Rule Interchange Format (RIF), which is primarily based on the RuleML standards design (semantic web rules)
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This training camp teaches you how FIWARE technologies and iSHARE, brought together under the umbrella of the i4Trust initiative, can be combined to provide the means for creation of data spaces in which multiple organizations can exchange digital twin data in a trusted and efficient manner, collaborating in the development of innovative services based on data sharing and creating value out of the data they share. SMEs and Digital Innovation Hubs (DIHs) will be equipped with the necessary know-how to use the i4Trust framework for creating data spaces!
The differing ways to monitor and instrumentJonah Kowall
FullStack London July 15th, 2016
Monitoring is complicated, and in most organizations consists of far too many tools owned by many teams. These tools consist of monitoring tools each looking at a component myopically. These tools metrics and logs from devices and software emitting them. Increasingly modern companies are creating their own instrumentation, but there is a large base of generic instrumentation of software. Fixing monitoring issues requires people, process, and technology. In this talk we will cover many common issues seen in the real world. For example decisions on what should be monitored or collected from a technology and a business perspective. This requires process and coordination.
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Attendees of this session will walk away with a clear understanding of:
What is instrumentation, and what do I instrument, collect, and store?
The understanding of overhead and how this can be accomplished on common software stacks?
How to work with application owners to collect business data.
How correlation works in custom open source or packaged monitoring tools.
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Semantic Complex Event Processing with Reaction RuleML 1.0 and Prova 3.0
1. Semantic Complex Event Processing
with
Reaction RuleML 1.0 and Prova 3.0
BPM EduNet Seminar
presented at
University of Toronto, McGill University, Ryerson University, University of
Ontario Institute of Technology
by
Adrian Paschke
AG Corporate Semantic Web
Freie Universität Berlin, Germany
0
2. About Freie Universitaet Berlin
• Freie Universitaet Berlin was founded 1948 with the support of the
American Allies and Berlin politicians as a response to the political
influence of the UDDSR in the Soviet sector of the divided city of Berlin.
• Freie Universitaet Berlin among the best ranked universities in Germany
• Freie Universitaet Berlin is the largest of the three universities in Berlin –
number of students: 28,500
3. About Corporate Semantic Web Group at FU Berlin
Corporate Semantic Web
Corporate
Semantic
Engineering
Corporate
Semantic
Search
Corporate
Semantic
Collaboration
Public Semantic Web
Corporate Business Information Systems
Business Context
www.corporate-
semantic-web.de
4. Some of our Projects ...
• Corporate Semantic Web
http://www.corporate-semantic-web.de
• Transatlantic Business Process
Management Network (BPM EduNet)
• DBPedia Deutschland
• RuleML
• Reaction RuleML
• W3C Rule Interchange Format (RIF)
• OASIS LegalRuleML
• Semantic Complex Event Processing
and EP Reference Architectures
• Event Processing Technical Society (EPTS)
epts
event processing technical society
5. About the BPM EduNet – http://bpmedu.net
Europe
Canada
6. Mission
• To promote understanding and advancement of
Business Process Management from the perspective
of Computer Science
– Real-Time Data and Complex Event Processing
– Corporate Semantic Web / Semantic BPM
– Semantic Business Rules and Decision Management
– Internet-based, Distributed IT and Knowledge
Management
7. Objectives
• to enhance the educational experiences, technological
and cultural skills, and global mobility of students
• to support the teaching of modern BPM and underlying IT
systems/service technologies in computer science studies
• to facilitate collaboration between Canadian and
European universities to expand their programs in BPM
studies in computer science curricula
• to provide a virtual campus as forum for international
collaboration and sharing
9. RuleML Organization
• Has an open non-profit structure
• Drives the specification of standard
semantic technology & business rules
• Coordinates rule research & development and
holds international meetings
8
10. RuleML Standards Effort
• Connects Web rule efforts across
– academia
– standards bodies
– industry
• Complements Web ontology efforts as part of
the semantic technology stack
• Provides a de facto standard for
Web knowledge representation
9
13. RuleML Basis for other Languages
• W3C Semantic Web Rule Language (SWRL)
– Uses RuleML Version 0.89
• W3C Semantic Web Services Language (SWSL)
– Uses RuleML Version 0.89
• W3C Rule Interchange Format
– Uses RuleML Version 0.91 with frames and slots
• OASIS LegalRuleML
– Uses RuleML Version 1.0
• OMG PRR
– Input from RuleML
• … 12
14. Application A
Data
Rules
Rule
system 1
Application B
Data
Rules
Rule
system 2
Ontologies
(ODM, OWL,
RDF-S, CL)
RuleML Rule interchange
(on the PIM level)
RuleML
docserialize de-serial.
Rules
<XML doc>
(or others)serialize de-serial.
data
13
21. RuleML – Striped Syntax
''The discount for a customer buying a product is 5.0 percent
if the customer is premium and the product is regular.''
<Implies>
<then>
<Atom>
<op><Rel>discount</Rel></op>
<Var>customer</Var>
<Var>product</Var>
<Ind>5.0 percent</Ind>
</Atom>
</then>
<if>
<And>
<Atom>
<op><Rel>premium</Rel></op>
<Var>customer</Var>
</Atom>
<Atom>
<op><Rel>regular</Rel></op>
<Var>product</Var>
</Atom>
</And>
</if>
</Implies>
Implies
then
Atom
op Rel discount
Var customer
Var product
Ind 5.0 percent
if
And
Atom
op Rel premium
Var customer
Atom
op Rel regular
Var product
node tag
edge tag
Iteration of node and edge
tags maps to (RDF) graph
structure
28. <-- rule interface with two alternative interpretation semantics and a signature.
The interface references the implementation identified by the corresponding key -->
<Rule keyref=“#r1">
<evaluation index="1">
<!-- WFS semantic profile define in the metamodel -->
<Profile type="&ruleml;Well-Founded-Semantics" direction="backward"/>
</evaluation>
<evaluation index="2">
<!-- alternative ASS semantic profile define in the metamodel -->
<Profile type="&ruleml;Answer-Set-Semantics" direction="backward"/>
</evaluation>
<!-- the signature defines the queryable head of the backward-reasoning rule -->
<signature>
<Atom><Rel>getPapers</Rel><Var mode="+"/><Var mode="+"/><Var mode="-"/></Atom>
</signature>
</Rule>
<!-- implementation of rule 1 which is interpreted either by WFS or by ASS semantics
and onyl allows queries according to it's signature definition. -->
<Rule key=“#r1" style="reasoning">
<if>... </if>
<then>
<Atom><Rel>getPapers</Rel><Var>Type</Var><Var>Status</Var><Var>Papers</Var></Atom>
</then>
</Rule>
Example: Distributed Rule Interface and Implementation
Interface
with
evaluation
semantics
and
public rule
signature
Implemen
tation
27
29. <Assert key="#module1”>
<meta> <!-- descriptive metadata -->
<Atom><Rel iri="dc:creator"/><Ind>Adrian Paschke</Ind></arg></Atom>
</meta>
<meta>
<Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2011-01-01</Data></Time>
</meta>
<meta>
<Atom><Rel>source</Rel><Ind>./module1.prova</Ind></Atom>
</meta>
<qualification> <!-- qualifying metadata -->
<Atom> <!-- the module is valid for one year from 2011 to 2012 -->
<Rel>valid</Rel>
<Interval type="&ruleml;TimeInstant">
<Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2011-01-01</Data></Time>
<Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2012-01-01</Data></Time>
</Interval>
</Atom>
</qualification>
<Rulebase key="#innermodule1.1">
<meta>…</meta> <qualification> … </qualification>
<Rule key=“#rule1”>
<meta>…<meta> <qualification> … </qualification>
</Rule>
<Rule key=“#rule2”>
<meta>…<meta> <qualification> … </qualification>
</Rule>
</Rulebase>
</Assert>
Descriptive and Qualifying Metadata
Descriptive
Metadata
Qualifying
Metadata
28
30. <Assert key="#module1”>
<meta> <!-- descriptive metadata -->
<Atom><Rel iri="dc:creator"/><Ind>Adrian Paschke</Ind></arg></Atom>
</meta>
<qualification> <!-- qualifying metadata -->
<Atom> <!-- the module is valid for one year from 2011 to 2012 -->
<Rel>valid</Rel>
<Interval type="&ruleml;TimeInstant">
<Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2011-01-01</Data></Time>
<Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2012-01-01</Data></Time>
</Interval>
</Atom>
</qualification>
<Rulebase key="#innermodule1.1">
<!– the rule base scopes apply to all knowledge in the rule base -->
<scope> <!– scope : only knowledge authored by „Adrian Paschke“ -->
<Atom><Rel iri="dc:creator"/><Ind>Adrian Paschke</Ind></arg></Atom>
</scope>
<scope> <!– scope : only valid knowledge; validity value will be bound to variable -->
<Atom><Rel>valid</Rel><Var>Validity</Var></Atom>
</scope>
<Rule key=“#rule1”>
<scope> <Atom><Rel>source</Rel><Ind>./module1.prova</Ind></Atom></scope>
<guard> <!-- guard on the validity: “current date during validity of module1”
<Operator type="&ruleml;During"><Expr iri="…getDate()“/><Var>Validity</Var></Operator>
</guard>
<if> … </if> <then> </then>
</Rule>
</Rulebase>
</Assert>
Dynamic Views with Scopes and Guards
Inner
Scope
+
Guard
Outer
Scope
29
31. 30
Example Selection Implementation in Prova
% Select stock ticker events from stream "S&P500"
% Each received event starts a new subconversation (CID)
which further processes the selected event (select)
rcvMult(CID,stream,“S&P500“, inform, tick(S,P,T)) :-
select(CID,tick(S,P,T)).
% Indefinitely (count=-1) receive further ticker events from
other streams that follow the previous selected event in
event processing group (group=g1). If the price differs
for the same stock at the same time [T1=T2, P1!=P2] then …
select(CID,tick(S,P1,T1)) :-
@group(g1) @count(-1)
rcvMsg(CID,stream, StreamID ,inform, tick(S,P2,T2))
[T1=T2, P1!=P2] %guard: if at same time but different price,
println (["Suspicious:",StreamID, tick(S,P2,T2)]," ").
32. Example - Rating Implementation in Prova:
Metadata Scoped Reasoning and Guards
% stream1 is trusted but stream2 is not, so one
solution is found: X=e1
@src(stream1) event(e1).
@src(stream2) event(e2).
%note, for simplicity this is just a simple fact, but
more complicated rating, trust, reputation policies
could be defined
trusted(stream1).%only event from „stream1“ are trusted
ratedEvent(X):-
@src(Source) %scoped reasoning on @src
event(X) [trusted(Source)]. %guard on trusted sources
:-solve(ratedEvent(X)). % => X=e1 (but not e2)
33. 32
Example - Scoring Implementation in Prova:
Metadata Scoped Reasoning
% event instances with metadata @key(value,[,value]*)
@score(1) @src(stream1) @label(e1) tick("Opel",10,t1).
@score(3) @src(stream2) @label(e2) tick("Opel",15,t1).
happens(tick(S,P),T):-
% scope defined over @score metadata bound to "Value"
@score(Value) tick(S,P,T) [Value>2]. %guard Value > 2
@score(1) @src(stream1) @label(e1) tick("Opel",10,t1).
@score(3) @src(stream2) @label(e2) tick("Opel",15,t1).
happens(tick(S,P),T):-
% select ticks from „stream1“
@score(Value) @src(stream1) @label(E) tick(S,P,T),
NewValue = Value + 3, %add 3 to score „Value“
%update selected events from „stream1“ with „NewValue“
update(@label(E), @score(NewValue) tick(S,P,T)),
@score(S) tick(S,P,T) [S>3]. %guard Value > 3
37. Reaction RuleML Metamodel
Top Level Ontologies
General concepts such as space, time, event, action and their properties and relations
Temporal
Ontology
Action
Ontology Process
Ontology
Agent
Ontology
Situation
Ontology
Domain
Ontologies
Vocabularies related
to specific domains
by specializing the
concepts introduced
in the top-level
ontology
Task
Activities
Ontologies
Vocabularies related
to generic tasks or
activities by
specializing the
concepts introduced in
the top-level ontology
Application
Ontologies
Specific
user/application
ontologies
E.g. ontologies describing roles
played by domain entities while
perfoming application activities
Spatio
Ontology
Event
Ontology
36
38. Time Top Ontology Metamodel
Time
Time Instant Time Interval
Time Types
37
39. Interval Top Ontology Metamodel
Interval
Empty
Interval
Left
Open
Interval
Interval
Types
Degene
rated
Interval
Left Closed
Right Open
Interval
Left Open
Right Closed
Interval
Left
Closed
Interva
l
Right
Closed
Interva
l
Right
Open
Interva
l
38
48. <Rule style="active">
<evaluation>
<Profile> e.g. selection and consumptions policies </Profile>
</evaluation>
<signature>
<Event key="#ce2">
… see example from the previous slide
</Event>
</signature>
<on>
<Event keyref="#ce2"/> <!– use defined event pattern for detecting events -->
</on>
<do>
<Assert safety="transactional"> <!-- transactional update -->
<formula>
<Atom>
<op><Rel>likes</Rel></op>
<arg><Ind>John</Ind></arg>
<arg><Var>X</Var></arg>
</Atom>
</formula>
</Assert>
</do>
</Rule>
Rule Interface and Rule Implementation
Interface
(Note in reaction
rules the signature
is the event pattern
which detects the
event instances)
Implementation
47
49. 48
Example Implementations in Prova:
Semantic Event Processing Example
Event Stream
{(Name, “OPEL”)(Price, 45)(Volume, 2000)(Time, 1) }
{(Name, “SAP”)(Price, 65)(Volume, 1000) (Time, 2)}
Semantic Query Filer:
Stocks of companies, which have production facilities in Europe and
produce products out of metal and
Have more than 10,000 employees.
{(OPEL, is_a, car_manufacturer),
(car_manufacturer, build, Cars),
(Cars, are_build_from, Metall),
(OPEL, hat_production_facilities_in, Germany),
(Germany, is_in, Europe)
(OPEL, is_a, Major_corporation),
(Major_corporation, have, over_10,000_employees)}
Semantic
Knowledge Base
rcvMult(SID,stream,“S&P500“, inform,
tick(Name^^car:Major_corporation,P^^currency:Dollar,
T^^time:Timepoint)) :- … <semantic filter inference> .
50. Execution Semantics
(can be defined in the <evaluation> semantics element)
1. Definition
– Definition of event/action pattern e.g. by event algebra
– Based on declarative formalization or procedural implementation
– Defined over an atomic instant or an interval of time, events/actions, situation, transition
etc.
2. Selection
– Defines selection function to select one event from several occurred events
(stored in an event instance sequence e.g. in memory, database/KB) of a particular type,
e.g. “first”, “last”
– Crucial for the outcome of a reaction rule, since the events may contain different
(context) information, e.g. different message payloads or sensing information
3. Consumption
– Defines which events are consumed after the detection of a complex event
– An event may contribute to the detection of several complex events, if it is not consumed
– Distinction in event messaging between “multiple receive” and “single receive”
– Events which can no longer contribute, e.g. are outdated, should be removed
4. Execution
– Actions might have an internal effect i.e. change the knowledge state leading to state
transition from (pre-)condition state to post-condition state
– The effect might be hypothetical (e.g. a hypothetical state via a computation) or
persistent (update of the knowledge base),
– Actions might have an external side effect
4949
51. EPTS - Reference Architecture: Functional View
Event Production
Publication,
Retrieval
EventProcessMonitoring,Control
Event Preparation
Identification, Selection, Filtering,
Monitoring, Enrichment
Complex Event Detection
Consolidation, Composition,
Aggregation
Event Reaction
Assessment, Routing, Prediction,
Discovery, Learning
Event Consumption
Dashboard, Apps,
External Reaction
Run time Administration
EventandComplexEvent
(Pattern,Control,Rule,Query,RegEx.etc)
Definition,Modeling,(continuous)Improvement
Design time
Event Analysis
Analytics, Transforms, Tracking,
Scoring, Rating, Classification
0..*
0..*
0..*
0..*
StateManagement
Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Tutorial on advanced design patterns in event processing.
DEBS 2012: 324-334; http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design-patterns-v204b
50
52. 51
Example – Enrichment Implementation in Prova:
Example with SPARQL Query
% Filter for car manufacturer stocks and enrich the stock tick
event with data from Wikipedia (DBPedia) about the manufacturer
and the luxury cars
rcvMult(SID,stream,“S&P500“, inform, tick(S,P,T)) :-
carManufacturer(S,Man), % filter car manufacturers
findall([luxuryCar|Data],luxuryCar(Man,Name,Car),Data), % query
EnrichedData = [S,Data], % enrich with additional data
sendMsg(SID2,esb,"epa1", inform, happens(tick(EnrichedData,P),T).
% rule implementing the query on DBPedia using SPARQL query
luxuryCar(Manufacturer,Name,Car) :-
Query="SELECT ?manufacturer ?name ?car % SPARQL RDF Query
WHERE {?car <http://purl.org/dc/terms/subject>
<http://dbpedia.org/resource/Category:Luxury_vehicles> .
?car foaf:name ?name .
?car dbo:manufacturer ?man .
?man foaf:name ?manufacturer.
} ORDER by ?manufacturer ?name“,
sparql_select(Query,manufacturer(Manufacturer),name(Name),car(Car)).
54. Coupling Approaches for
(Distributed) Reaction RuleML Bases
• Strong coupling
– Interaction through a stable interface
– API call is hard coded
• Loose coupling
– Resilient relationship between two or
more systems or organizations with
some kind of exchange relationship
– Each end of the transaction makes its
requirements explicit, e.g. as an
interface description, and makes few
assumptions about the other end
• Decoupled
– de-coupled in time using (event)
messages (e.g. via Message-oriented
Middleware (MoM))
– Often asynchronous stateless
communication (e.g. publish-
subscribe or CEP event detection)
Event
Cloud
(unordered events)
new auto pay account login
account logindeposit
withdrawal
logout
account balance
transfer
deposit new auto pay
enquiry
enquiry
logout
new auto pay account login
account login
deposit
activity history
withdrawal
logouttransfer
deposit new auto pay
enquiry
enquiry
book
requestincident
Event Streams
(ordered events)
Agent / Service Interface
Rule Service
Consumer
Reaction RuleML
Message Interchange
0.0000
Request
0.0000
Response
Rule KB1
Rule KBn
…
Computer
KB
direct
implementation
53
55. Send and Receive Messaging
•Send a message
• Send @directive (oid,protocol,agent,content)
•Receive a message
• Receive @directive (oid,protocol,agent,content)
–oid is the conversation identifier (enabling also subconverstations)
–protocol: protocol definition (high-level protocols and transport prot.)
–agent: denotes the target or sender of the message
–@directive: pragmatic context, e.g. FIPA
Agent Communication Language (ACL) primitives
–content: Message payload 54
56. Messaging Reaction Rules
<Rule>
…
<do><Send><oid>xid1</oid> …query1 </Send></do>
<do><Send><oid>xid2</oid> …query2 </Send></do>
<on><Receive><oid>xid2</oid> …response2 </Receive></on>
<if> prove some conditions, e.g. make decisions on the received
answers </if>
<on><Receive><oid>xid1</oid> …response1 </Receive></on>
….
</Rule>
Note: The „on“, „do“, „if“ parts can be in
arbitrary combinations to allow for a flexible
workflow-style logic with subconversations
and parallel branching logic 55
57. Reaction – Routing Implementation Example in Prova:
Example with Agent (Sub-) Conversations
rcvMsg(XID,esb,From,query-ref,buy(Product) :-
routeTo(Agent,Product), % derive processing agent
% send order to Agent in new subconversation SID2
sendMsg(SID2,esb,Agent,query-ref,order(From, Product)),
% receive confirmation from Agent for Product order
rcvMsg(SID2,esb,Agent,inform-ref,oder(From, Product)).
% route to event processing agent 1 if Product is luxury
routeTo(epa1,Product) :- luxury(Product).
% route to epa 2 if Product is regular
routeTo(epa2,Product) :- regular(Product).
% a Product is luxury if the Product has a value over …
luxury(Product) :- price(Product,Value), Value >= 10000.
% a Product is regular if the Product ha a value below …
regular(Product) :- price(Product,Value), Value < 10000.
corresponding serialization with Reaction
RuleML <Send> and <Receive>
56
58. Reaction RuleML Messaging
•<Message directive="<!-- pragmatic context -->" >
• <oid> <!-- conversation ID--> </oid>
• <protocol> <!-- transport protocol --> </protocol>
• <sender> <!-- sender agent/service --> </sender>
• <receiver> <!-- receiver agent/service --> </receiver>
• <content> <!-- message payload --> </content>
•</Message>
• oid is the conversation identifier (enabling also subconverstations)
• protocol: protocol definition (high-level protocols and transport prot.)
• agent (send, receiver): denotes the target or sender of the message
• @directive: pragmatic context, e.g. FIPA ACL primitives
• content: Message payload
57
59. Loosley-Coupled Communication via
Messages to Agent Interface
<Message directive="acl:query-ref">
<oid> <Ind>conversation1</Ind> </oid>
<protocol> <Ind>esb</Ind> </protocol>
<sender> <Ind>Agent1</Ind> </sender>
<receiver> <Ind>Agent2</Ind> </receiver>
<content>
<Atom>
<Rel>likes</Rel>
<Ind>John</Ind>
<Ind>Mary</Ind>
</Atom>
</content>
</Message>
• Event Message is local to the conversation state (oid)
and pragmatic context (directive)
FIPA ACL directive
Interpreted by Agent 2 as
query according to
ACL:query-ref
Note: the receiver „Agent2“
needs to specify an
appropriate <signature> for
„likes“
58
60. Message Driven Routing
Prova : Event Routing in Event-Driven Workflows
rcvMsg(XID,Process,From,event,["A"]) :-
fork_b_c(XID, Process).
fork_b_c(XID, Process) :-
@group(p1) rcvMsg(XID,Process,From,event,["B"]),
execute(Task1), sendMsg(XID,self,0,event,["D"]).
fork_b_c(XID, Process) :-
@group(p1) rcvMsg(XID,Process,From,event,["C"]),
execute(Task2), sendMsg(XID,self,0,event,["E"]).
fork_b_c(XID, Process) :-
% OR reaction group "p1" waits for either of the two
event message handlers "B" or "C" and terminates the
alternative reaction if one arrives
@or(p1) rcvMsg(XID,Process,From,or,_).
A
B
C
Task1
Task2
D
E
59
61. Distributed Rule Base Interchange
Prova: Mobile Code
% Manager
upload_mobile_code(Remote,File) :
Writer = java.io.StringWriter(), % Opening a file fopen(File,Reader),
copy(Reader,Writer),
Text = Writer.toString(),
SB = StringBuffer(Text),
sendMsg(XID,esb,Remote,eval,consult(SB)).
% Service (Contractor)
rcvMsg(XID,esb,Sender,eval,[Predicate|Args]):- derive([Predicate|Args]).
Contract Net Protocol
62. Example: Distributed Rule-
Based Agents in Rule
Responder
http://responder.ruleml.org
61
Adrian Paschke and Harold Boley: Rule
Responder: Rule-Based Agents for the
Semantic-Pragmatic Web, in Special Issue on
Intelligent Distributed Computing in
International Journal on Artificial Intelligence
Tools (IJAIT), V0l. 20,6, 2011
http://dx.doi.org/10.1142/S0218213011000528
63. Test Cases for Self-validating Rule Bases
• Test Cases constrain the possible models and approximate the intended models of
the rule base
– Queries are used to test the rule base
• A test case is defined by T := {X, A, N}, where
– assertion base (input data, e.g. facts )
– a formula denoting a test query
– N := + , – a positive or negative label
• Semantics
– |=TC compatibility relation
– Mod association function between sets of formulas and sets of models
– Σ model selection function
for T:={X, A, +} and for T:={X,A,-}
– CR(X) deductive closure of X. Decidable inference operator based on formal proofs
LX
LA
)(R)(Mod(X),m:Mmiff)A,(X,|M 0TC0 AModm
)(R)(Mod(X),m:Mmiff)A,(X,|M 0TC0 AModm
)(XCA R)(XCA R
+
Intended
Model
Partial
Model
+ +
-
Test Cases (+,-)
inconsistent: +
Intended
Model
+ +
-
Test Cases (+,-)
consistent:
-
64. Rule Markup for Test Cases / Test Suites<TestSuite keyref=“#rulebase1”>
<evaluation>
<Profil><InferenceEngineSemantics>minimal</InferenceEngineSemantics> </Profile>
</evaluation>
<Test key="ID001" purpose="...">
<Assert><formula>
<And>
<Atom>
<Rel>parent</Rel>
<Ind>John</Ind>
<Ind>Mary</Ind>
</Atom>
</And>
</formula></Assert>
<TestItem expectedAnswer="yes">
<Query><formula>
<Atom closure="universal">
<Rel>uncle</Rel>
<Ind>Mary</Ind>
<Var>Nephew</Var>
</Atom>
</formula></Query>
<expectedResults>
<VariableValuePair>
<Var>Nephew</Var>
<Ind>Tom</Ind>
</VariableValuePair>
<VariableValuePair>
<Var>Nephew</Var>
<Ind>Irene</Ind>
</VariableValuePair>
</expectedResults>
</TestItem></Test></TestSuite>
Adrian Paschke, Jens Dietrich, Adrian Giurca, Gerd Wagner, Sergey Lukichev:
On Self-Validating Rule Bases, Proceedings of 2nd International Workshop on
Semantic Web Enabled Software Engineering (SWESE 2006), Athens,
Georgia, USA (6th November 2006), November 2006
http://km.aifb.kit.edu/ws/swese2006/final/paschke_full.pdf
65. Coupling Approaches for
(Distributed) Reaction RuleML Bases
• Strong coupling
– Interaction through a stable interface
– API call is hard coded
• Loose coupling
– Resilient relationship between two or
more systems or organizations with
some kind of exchange relationship
– Each end of the transaction makes its
requirements explicit, e.g. as an
interface description, and makes few
assumptions about the other end
• Decoupled
– de-coupled in time using (event)
messages (e.g. via Message-oriented
Middleware (MoM))
– Often asynchronous stateless
communication (e.g. publish-
subscribe or CEP event detection)
Event
Cloud
(unordered events)
new auto pay account login
account logindeposit
withdrawal
logout
account balance
transfer
deposit new auto pay
enquiry
enquiry
logout
new auto pay account login
account login
deposit
activity history
withdrawal
logouttransfer
deposit new auto pay
enquiry
enquiry
book
requestincident
Event Streams
(ordered events)
Agent / Service Interface
Rule Service
Consumer
Reaction RuleML
Message Interchange
0.0000
Request
0.0000
Response
Rule KB1
Rule KBn
…
Computer
KB
direct
implementation
64
67. Summary of Selected Reaction RuleML Features
• Different Rule Families and Types (DR, KR, PR, ECA, CEP)
• Support for Event/Action/Situation… Reasoning and Processing
• Rule Interface and Implementation for Distributed Knowledge
• Test Cases for Verification, Validation and Integrity (VVI Testing)
• Knowledege Life Cycle Management, Modularization and Dynamic
Views (metadata, scopes, guards, situations/fluents, update actions)
• Decoupled event messaging and loosley-coupled send/receive
interaction against rule (KB) interface within conversations,
coordination/negotiation protocols and pragmatic directives
• Different detection, selection and consumption semantics (profiles)
• Different algebras (Event, Action, Temporal,…)
• Specialized Language Constructs
– Intervals (Time, Event), situations (States, Fluents)
• Top Level Meta Model for semantic Type Definitions and predefined
Types in Top-Level Ontologies
• External (event) query languages, external data and ontology models
• ... 66
69. RuleML Online Community
• RuleML MediaWiki (http://wiki.ruleml.org)
• Mailing lists (http://ruleml.org/mailman/listinfo)
• Technical Groups
(http://wiki.ruleml.org/index.php/Organizational_Structure#Technical_Groups)
– Uncertainty Reasoning
– Defeasible Logic
– Reaction Rules
– Multi-Agent Systems
– …
• RuleML sources are hosted on Github
(https://github.com/RuleML) 68
70. Further Reading
• RuleML wiki page corresponding to this talk
(http://wiki.ruleml.org/index.php/Introducing_RuleML)
• RuleML 1.0: The Overarching Specification of Web Rules
– Talk (http://cs.unb.ca/~boley/talks/RuleML-Overarching-Talk.pdf)
– Paper (http://link.springer.com/chapter/10.1007%2F978-3-642-16289-3_15)
• Reaction RuleML 1.0: Standardized Semantic Reaction
Rules
– Paper (http://link.springer.com/chapter/10.1007%2F978-3-642-32689-9_9)
• Grailog 1.0: Graph-Logic Visualization of Ontologies and
Rules
– Talk (http://cs.unb.ca/~boley/talks/RuleMLGrailog.pdf)
– Paper (http://link.springer.com/content/pdf/10.1007%2F978-3-642-39617-5_9) 69
71. Further Reading – Surveys and Tutorials
• Paschke, A., Boley, H.: Rules Capturing Event and Reactivity, in Handbook of Research on Emerging
Rule-Based Languages and Technologies: Open Solutions and Approaches, IGI Publishing, ISBN:1-
60566-402-2, 2009
http://www.igi-global.com/book/handbook-research-emerging-rule-based/465
Sample Chapter: http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=rtdoc&an=16435934&article=0&fd=pdf
• Adrian Paschke, Alexander Kozlenkov: Rule-Based Event Processing and Reaction Rules. RuleML
2009: 53-66
http://link.springer.com/chapter/10.1007%2F978-3-642-04985-9_8
• Adrian Paschke, Paul Vincent, Florian Springer: Standards for Complex Event Processing and
Reaction Rules. RuleML America 2011: 128-139
http://link.springer.com/chapter/10.1007%2F978-3-642-24908-2_17
http://www.slideshare.net/isvana/ruleml2011-cep-standards-reference-model
• Adrian Paschke: The Reaction RuleML Classification of the Event / Action / State Processing and
Reasoning Space. CoRR abs/cs/0611047 (2006)
http://arxiv.org/ftp/cs/papers/0611/0611047.pdf
• Jon Riecke, Opher Etzion, François Bry, Michael Eckert, Adrian Paschke, Event Processing Languages,
Tutorial at 3rd ACM International Conference on Distributed Event-Based Systems. July 6-9, 2009 -
Nashville, TN
http://www.slideshare.net/opher.etzion/debs2009-event-processing-languages-tutorial
• Paschke, A., Boley, H.: Rule Markup Languages and Semantic Web Rule Languages, in Handbook of
Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, IGI
Publishing, ISBN:1-60566-402-2, 2009
http://www.igi-global.com/chapter/rule-markup-languages-semantic-web/35852
Sample: Chapter: http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=rtdoc&an=18533385
72. Further Reading – RuleML and Reaction RuleML
• Harold Boley, Adrian Paschke, Omair Shafiq: RuleML 1.0: The Overarching Specification of Web
Rules. RuleML 2010: 162-178
http://dx.doi.org/10.1007/978-3-642-16289-3_15
http://www.cs.unb.ca/~boley/talks/RuleML-Overarching-Talk.pdf
• Adrian Paschke, Harold Boley, Zhili Zhao, Kia Teymourian and Tara Athan: Reaction RuleML 1.0:
Standardized Semantic Reaction Rules, 6th International Conference on Rules (RuleML 2012),
Montpellier, France, August 27-31, 2012
http://link.springer.com/chapter/10.1007%2F978-3-642-32689-9_9
http://www.slideshare.net/swadpasc/reaction-ruleml-ruleml2012paschketutorial
• Paschke, A., Boley, H.: Rule Markup Languages and Semantic Web Rule Languages, in Handbook of
Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, IGI
Publishing, ISBN:1-60566-402-2, 2009
http://www.igi-global.com/chapter/rule-markup-languages-semantic-web/35852
Sample chapter: http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=rtdoc&an=18533385
• Paschke, A., Boley, H.: Rules Capturing Event and Reactivity, in Handbook of Research on Emerging
Rule-Based Languages and Technologies: Open Solutions and Approaches, IGI Publishing, ISBN:1-
60566-402-2, 2009
http://www.igi-global.com/book/handbook-research-emerging-rule-based/465
Sample Chapter: http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=rtdoc&an=16435934&article=0&fd=pdf
• Adrian Paschke and Harold Boley: Rule Responder: Rule-Based Agents for the Semantic-Pragmatic
Web, in Special Issue on Intelligent Distributed Computing in International Journal on Artificial
Intelligence Tools (IJAIT), V0l. 20,6, 2011
http://dx.doi.org/10.1142/S0218213011000528
73. Further Reading – EPTS Event Processing Reference
Architecture and Event Processing Patterns
• Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Advanced design patterns in event
processing. DEBS 2012: 324-334;
http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design-patterns-v204b
• Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Architectural and functional design
patterns for event processing. DEBS 2011: 363-364
http://www.slideshare.net/isvana/epts-debs2011-event-processing-reference-architecture-and-patterns-tutorial-
v1-2
• Paschke, A., Vincent, P., and Catherine Moxey: Event Processing Architectures, Fourth ACM
International Conference on Distributed Event-Based Systems (DEBS '10). ACM, Cambridge, UK,
2010.
http://www.slideshare.net/isvana/debs2010-tutorial-on-epts-reference-architecture-v11c
• Paschke, A. and Vincent, P.: A reference architecture for Event Processing. In Proceedings of the
Third ACM International Conference on Distributed Event-Based Systems (DEBS '09). ACM, New York,
NY, USA, 2009.
• Francois Bry, Michael Eckert, Opher Etzion, Adrian Paschke, Jon Ricke: Event Processing Language
Tutorial, DEBS 2009, ACM, Nashville, 2009
http://www.slideshare.net/opher.etzion/debs2009-event-processing-languages-tutorial
• Adrian Paschke. A Semantic Design Pattern Language for Complex Event Processing, AAAI Spring
Symposium "Intelligent Event Processing", March 2009, Stanford, USA
http://www.aaai.org/Papers/Symposia/Spring/2009/SS-09-05/SS09-05-010.pdf
• Paschke, A.: Design Patterns for Complex Event Processing, 2nd International Conference on
Distributed Event-Based Systems (DEBS'08), Rome, Italy, 2008.
http://arxiv.org/abs/0806.1100v1
74. Further Reading – Rule-Based Semantic CEP
• Corporate Semantic Web – Semantic Complex Event Processing
– http://www.corporate-semantic-web.de/semantic-complex-event-processing.html
• Adrian Paschke: ECA-RuleML: An Approach combining ECA Rules with temporal interval-based KR
Event/Action Logics and Transactional Update Logics CoRR abs/cs/0610167: (2006);
http://arxiv.org/ftp/cs/papers/0610/0610167.pdf
• Adrian Paschke: Semantic Rule-Based Complex Event Processing. RuleML 2009: 82-92
http://link.springer.com/chapter/10.1007%2F978-3-642-04985-9_10
• Adrian Paschke, Alexander Kozlenkov, Harold Boley: A Homogeneous Reaction Rule Language for
Complex Event Processing, In Proc. 2nd International Workshop on Event Drive Architecture and
Event Processing Systems (EDA-PS 2007) at VLDB 2007; http://arxiv.org/pdf/1008.0823v1.pdf
• Adrian Paschke: ECA-LP / ECA-RuleML: A Homogeneous Event-Condition-Action Logic Programming
Language CoRR abs/cs/0609143: (2006); http://arxiv.org/ftp/cs/papers/0609/0609143.pdf
• Teymourian, K., Coskun, G., and Paschke, A. 2010. Modular Upper-Level Ontologies for Semantic
Complex Event Processing. In Proceeding of the 2010 Conference on Modular ontologies:
Proceedings of the Fourth international Workshop (WoMO 2010) O. Kutz, J. Hois, J. Bao, and B.
Cuenca Grau, Eds. Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, The
Netherlands, 81-93.
http://dl.acm.org/citation.cfm?id=1804769
• Adrian Paschke and Harold Boley: Rule Responder: Rule-Based Agents for the Semantic-Pragmatic
Web, in Special Issue on Intelligent Distributed Computing in International Journal on Artificial
Intelligence Tools (IJAIT), V0l. 20,6, 2011
http://dx.doi.org/10.1142/S0218213011000528
• Kia Teymourian, Adrian Paschke: Towards semantic event processing. DEBS 2009
75. Further Reading –Rules and Logic Programming, Prova
• Adrian Paschke: Rules and Logic Programming for the Web. A. Polleres et al. (Eds.):
Reasoning Web 2011, LNCS 6848, pp. 326–381, 2011.
http://dx.doi.org/10.1007/978-3-642-23032-5_6
http://rw2011.deri.ie/lectures/05Paschke_Rules_and%20Logic_Programming_for_the_Web.pdf
• Prova Rule Engine http://www.prova.ws/
– Prova 3 documentation http://www.prova.ws/index.html?page=documentation.php
• Journal: Adrian Paschke and Harold Boley: Rule Responder: Rule-Based Agents for the Semantic-
Pragmatic Web, in Special Issue on Intelligent Distributed Computing in International Journal on Artificial
Intelligence Tools (IJAIT), V0l. 20,6, 2011 ; http://dx.doi.org/10.1142/S0218213011000528
– Prova 3 Semantic Web Branch
• Paper: Paschke, A.: A Typed Hybrid Description Logic Programming Language with Polymorphic Order-
Sorted DL-Typed Unification for Semantic Web Type Systems, OWL-2006 (OWLED'06), Athens, Georgia,
USA.
• pre-compiled Prova 3 version with Semantic Web support from http://www.csw.inf.fu-
berlin.de/teaching/ws1213/prova-sw.zip.
(The Java sources of the Prova 3 Semantic Web are managed on GitHub (
https://github.com/prova/prova/tree/prova3-sw)
– Prova ContractLog KR: http://www.rbsla.ruleml.org/ContractLog.html
• Paschke, A.: Rule-Based Service Level Agreements - Knowledge Representation for Automated e-
Contract, SLA and Policy Management, Book ISBN 978-3-88793-221-3, Idea Verlag GmbH, Munich,
Germany, 2007.
http://rbsla.ruleml.org
– Prova CEP examples: http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-
architecture-design-patterns-v204b