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
Brief introduction on CEP and Terminology
o Drools Vision
o Drools Fusion: Complex Event Processing extensions
o Event Declaration and Semantics
o Event Cloud, Streams and the Session Clock
o Temporal Reasoning
o Sliding Window Support
o Streams Support
o Memory Management
EDA With Glassfish ESB Jfall IEP Intelligent Event ProcessingEugene Bogaart
Presentation talks about theory of Event Driven Architecture and Complex Event Processing. Afterwards Intellligent Event Processing with GlassFish ESB is explaind.
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.
Brief introduction on CEP and Terminology
o Drools Vision
o Drools Fusion: Complex Event Processing extensions
o Event Declaration and Semantics
o Event Cloud, Streams and the Session Clock
o Temporal Reasoning
o Sliding Window Support
o Streams Support
o Memory Management
EDA With Glassfish ESB Jfall IEP Intelligent Event ProcessingEugene Bogaart
Presentation talks about theory of Event Driven Architecture and Complex Event Processing. Afterwards Intellligent Event Processing with GlassFish ESB is explaind.
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.
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The increasing demand for real-time monitoring and decision making requires complex event processing (CEP) architectures, frameworks and tools. In this presentation Peter will introduce you to the concept CEP and in particular event stream analysis, the typical use cases, how it relates to event sourcing and implemented as event sourcing, a comparison of a couple of open source frameworks (Storm and Disruptor) and a comprehensive overview of Esper
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.
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Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing.
Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and
Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science
use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated
with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing
style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We
present a performance study that exhibits that the resulting CEP Engine—Siddhi—has significantly improved performance.
Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying
suggestions for improvements, implementing those improvements
through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.
Complex Event Processing (CEP) for Next-Generation Security Event Management,...Tim Bass
Complex Event Processing (CEP) for Next-Generation Security Event Management, Fraud and Intrusion Detection , April 17, 2007 (First Draft), London, Tim Bass, CISSP, Director, Principal Global Architect
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Mindmajix is the industry leader in Tibco Business Events Training which emphasize on hands on experience with examples from real-time scenarios by experts.
Combating Fraud and Intrusion Threats with Event ProcessingTim Bass
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Scalable complex event processing on samza @UBERShuyi Chen
The Marketplace data team at Uber has built a scalable complex event processing platform to solve many challenging real time data needs for various Uber products. This platform has been in production for almost a year and it has proven to be very flexible to solve many use cases. In this talk, we will share in detail the design and architecture of the platform, and how we employ Samza, Kafka, and Siddhi at scale.
This slides was presented at Stream Processing Meetup @ LinkedIn on June 15 2016.
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Semantic Complex Event Processing with Reaction RuleML 1.0 and Prova 3.0Adrian Paschke
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
Complex Event Processing in Practice at jDays 2012Peter Norrhall
The increasing demand for real-time monitoring and decision making requires complex event processing (CEP) architectures, frameworks and tools. In this presentation Peter will introduce you to the concept CEP and in particular event stream analysis, the typical use cases, how it relates to event sourcing and implemented as event sourcing, a comparison of a couple of open source frameworks (Storm and Disruptor) and a comprehensive overview of Esper
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.
Siddhi: A Second Look at Complex Event Processing ImplementationsSrinath Perera
Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing.
Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and
Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science
use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated
with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing
style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We
present a performance study that exhibits that the resulting CEP Engine—Siddhi—has significantly improved performance.
Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying
suggestions for improvements, implementing those improvements
through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.
Complex Event Processing (CEP) for Next-Generation Security Event Management,...Tim Bass
Complex Event Processing (CEP) for Next-Generation Security Event Management, Fraud and Intrusion Detection , April 17, 2007 (First Draft), London, Tim Bass, CISSP, Director, Principal Global Architect
Emerging Technologies Group
Presentation of functional design patterns for event processing with examples from TIBCO, Oracle, IBM and Prova event processing technologies. Superset of the Tutorial at DEBS2012. Editors Alex Alves, Catherine Moxey
Optimizing Your SOA with Event Processing, TIBCO, TUCON 2007, Tim Bass, Principal Global Architect, DirectorEmerging Technologies Group TIBCO Software Inc.
MindMajix is the Globally Professional Online Training Institute. We believe “The Art of Teaching is The Art of Assisting Discovery”
Mindmajix is the industry leader in Tibco Business Events Training which emphasize on hands on experience with examples from real-time scenarios by experts.
Combating Fraud and Intrusion Threats with Event ProcessingTim Bass
Combating Fraud and Intrusion Threats with Event Processing, TIBCO, TUCON 2007, Tim Bass, CISSP, Principal Global Architect, DirectorEmerging Technologies Group, TIBCO Software Inc.
Scalable complex event processing on samza @UBERShuyi Chen
The Marketplace data team at Uber has built a scalable complex event processing platform to solve many challenging real time data needs for various Uber products. This platform has been in production for almost a year and it has proven to be very flexible to solve many use cases. In this talk, we will share in detail the design and architecture of the platform, and how we employ Samza, Kafka, and Siddhi at scale.
This slides was presented at Stream Processing Meetup @ LinkedIn on June 15 2016.
EVAM (Event & Action Manager) is a real-time event processing engine capable of responding to complex event sequences and produce real-time actions based on the current event and historical data.
An introduction to the different phases of the BPM life cycle: Analyze, Design, Implement, Measure, Improve.
Key Takeaways:
- Analysis Frameworks provide context
- Transactions perform work, Analytics manage work
- The diagram isn’t everything
- Improve your capabilities in 3 areas:
-- Process Maturity
-- Process Management Maturity
-- Organizational Maturity
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Microsoft StreamInsight, part of the recent SQL Server 2008 R2 release, is a new platform for building rich applications that can process high volumes of event stream data with near-zero latency.
Mark Simms of Microsoft's SQLCAT will demonstrate the core skill sets and technologies needed to deliver StreamInsight enabled solutions, and discuss some of the core scenarios.
Mark will provide a detailed walkthrough of the three major components of StreamInsight: input and output adapters, the StreamInsight engine runtime, and the semantics of the continuous standing queries hosted in the StreamInsight engine.
This presentation includes hands-on demos, including building out a real-time data processing solution interacting with SQL Server and Sharepoint.
You will learn:
• The new capabilities StreamInsight brings to data processing and analytics, unlocking the ability to extract real time business intelligence from streaming data.
• How StreamInsight interacts with and compliments other components of SQL Server and the rest of the Microsoft technology stack.
• How to ramp up on the skills and technology necessary to build out end to end solutions leveraging streaming data sources.
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Semantic Complex Event Processing at Sem Tech 2010
1. Semantic
Complex Event Processing
„The Future of Dynamic IT“
Paul Vincent
Adrian Paschke
Harold Boley
1
2. Agenda
Complex Event Processing – What is it?
Semantic Complex Event Processing (SCEP) –
Why is semantics important in event processing?
SCEP + Ontologies
SCEP + (Reaction) Rules
– Example: Reaction RuleML
Use Cases for Semantics in CEP Systems
Summary
2
3. Complex Events – What are they?
Complex Events are aggregates, derivations, etc. of Simple Events
Simple Events Event
Simple Events Complex Events
Simple Events Patterns
Simple Events
Complex Event Processing (CEP) will enable, e.g.
– Detection of state changes based on observations
– Prediction of future states based on past behaviors
3
4. Complex Event Processing – What is it?
CEP is about complex event detection and reaction to complex events
– Efficient (near real-time) processing of large numbers of events
– Detection, prediction and exploitation of relevant complex events
– Situation awareness, track & trace, sense & respond
new auto pay
logout deposit
account login Continuous Event Queries
withdrawal new auto pay
transfer INSERT INTO OrdersMatch_s
book logout account login SELECT B.ID, S.ID, B.Price
deposit
Complex Events
enquiry
Event Cloud FROM BuyOrders_s B, Trades_s T, SellOrders_s S,
MATCHING [30 SECONDS: B, !T, S]
(unordered events) enquiry ON B.Price = S.Price = T.Price;
account balance request
incident
new auto pay enquiry
account login
deposit withdrawal new auto pay Patterns, Rules
transfer account login
logout
enquiry deposit
activity history
B
Event Streams A C
(ordered events)
4
5. CEP Functions
5 Source: Event Processing Technical Society Reference Architecture Working Group
6. Some CEP Scenarios …
Real Time BAM, Agile ITSM
Enterprise and BPM
Reactions to threats Monitor and detect
and opportunities IT service, SLA, and
according to business behavior
events in exceptions from
business observed events
transactions
Semantic
Systems Enterprise Decision
Valuable Management
Information at
the Right Time Detect Real-time Decision
Decide Guidance and Expert
to the Right Respond
Recipient Decision Support
Dynamic Real Time Decision
Information Management
Dissemination
6
7. The Many Roots of Event Processing
Agents
Distributed Event-based
Active Databases Computing
Event-driven
CEP
Rules Event-based
Workflows /
Business
Processes
Discrete event simulation
Detect Messaging /
High performance Decide Middleware
databases
Respond
7
9. Semantic CEP: The Benefits
Event data becomes meaningful information / declarative knowledge
while conforming to an underlying formal semantics
– e.g., automated mediation between different heterogeneous domains and
abstraction levels
Better understanding of situations (states) by machines (agents)
– e.g., a process is executing when it has been started and not ended
Better understanding of the relationships between events
e.g., temporal, spatial, causal, .., relations between events, states, activities,
processes
– e.g., a service is unavailable when the service response time is longer than X
seconds and the service is not in maintenance state
Declarative processing of events and reaction to situations
– Semantically grounded event-driven rules (= reaction rules)
9
10. Ontologies used for SCEP
Top-Level Ontologies required for SCEP
(core selection)
– Spatial
– Temporal
– Event
– Situation
– Process (can be further specialized by domain ontologies such as OWL-S, WSMO, PSL)
– Actor/Agent (can be further specialized, by special ontologies such as FIPA, ACL, ...)
– Action: (can be used in e.g. RIF, RuleML, …)
Domain Ontologies for application verticals
(samples domains of CEP applications)
– Healthcare - e.g., Hospital Activity Monitoring
– Finance - e.g., Fraud Detection
– Logistics and Cargo
– Supply Chain Management
– Insurance
– Mortgage
10
11. Examples of Ontologies which include Events
CIDOC CRM: museums and libraries
ABC Ontology: digital libraries
Event Ontology: digital music
DOLCE+DnS Ultralite: event aspects in social reality
Event-Model-F: event-based systems
VUevent Model: An extension of DOLCE and other event conceptualizations
IPTC. EventML: structured event information
GEM: geospatial events
Event MultiMedia: multimedia
LODE: events as Linked Data
CultureSampo: Publication System of Cultural Heritage
OpenCyC Ontology: human consensus reality, upper ontology with lots of terms and assertions
Super BPEL: ontology for the business process execution language
Semantic Sensor Net Ontology: ontology for sensor networks
11
12. Modular Ontology Model for SCEP
Top Level Ontologies
General concepts such as space, time, event and their properties and relations
Event Situation Process Temporal Spatio
Ontology Ontology Ontology Ontology Ontology
specializing the specialize the concepts
concepts introduced in the top-
introduced in the Domain Task level ontology for
top-level ontology generic tasks or activities
for a specific Ontologies Ontologies
domain
Specific
user/application Application e.g. ontologies describing roles
played by domain entities while
ontologies Ontologies perfoming application activities
Source: Kia Teymourian and Adrian Paschke: Towards
12 Semantic Event Processing, DEBS 2009, July 6-9, 2009
13. Example:
Situation Top-Level Ontology Model
Situation use the other Situation Situation
top-level Descriptions
Properties ontologies
Content
(time, location, participants, …)
hasProperties hasContent
Situation Situation
Types
Heterogeneous Homogenous Situation
Situation
Dynamic Time Frequency State Process Iterative Habitual
Change Frame Situation Situation Situation Situation Situation
Situation Situation
TrafficLight Within Rings3 LaysOn He He He Situation
Changes 5Minutes Times TheFloor Runs Coughs Smokes Individuals
13
14. Summary of Ontology Benefits for SCEP
Ontologies can be applied to CEP application
domains as in any other IT system
Ontologies for time-based events + event
operations etc can also improve formalisation
of CEP
15
15. Rule-based Complex Event Processing
CEP complex event detection can use reaction rules
– e.g. event-condition-action rule:
incoming event +
conditions over situations (effects + context) (re)action
Detected Complex (aggregated) events and
their effects (situations) trigger
reaction rules for decision + behavioral reaction logic
– e.g. CEP event-condition-action rule:
complex event + decisioning conditions response actions
Rule-based Event Processing Languages (EPLs)
– e.g. Reaction RuleML, IBM Amit Situation Manager Rule,
16
TIBCO BusinessEvents, ...
16. Production Rules and CEP
Production Rules react to fact changes (facts = data)
However, PR systems typically use an object model + events:
can represent external fact updates extensible for use in CEP
– Event types and object classes are defined in the rule declarations
– New events are added to the fact base / working memory
– might become an event channel (such as an event queue)
(facts = events and data)
– Instance tuples are filtered and joined in the rule conditions
and for valid tuples the action part of the rule is executed
Can be further extended with mechanisms such as query languages,
state models and temporal constraints
Examples: TIBCO BusinessEvents, Red Hat Drools
17
17. Event Condition Action Rules and CEP
ECA Rule
“on Event if Condition do Action”
– Explicit event part (trigger) + separate data conditions actions
– e.g. on customer order (event) + check if credit card is valid (condition)
process order (action)
Evolved from active databases
– extend databases with trigger-type reactions,
e.g. HiPac, Chimera, ADL, COMPOSE, NAOS
– Composite event algebras, e.g. SAMOS, COMPOSE, Snoop
– Sequence | Disjunction | Xor | Conjunction |
Concurrent | Not | Any | Aperiodic | Periodic
18
18. Research Standards Area: Reaction RuleML
RuleML = family of rule languages in homogeneous interchange format
– Technology input to standards bodies like W3C (RIF) and OMG (PRR)
Reaction RuleML = general reaction rule format
that can be specialized as needed for different rule types (e.g. PR, ECA style rules, …)
– Platform-independent XML-based rule interchange format
– translation into platform-specific executable rule languages, e.g. Prova
– Three general execution semantics:
– Active: actively 'poll' external event source for events
e.g. ping a service/system or query an internal or external event database
– Messaging: wait for incoming event from a messaging system
– Reasoning: KR event/action logic reasoning and transitions/transactions
(e.g. as in Event Calculus, Situation Calculus, Temporal Action Logic formalizations)
– Appearance
– Global: „globally‟ defined reaction rule
– Local: „locally‟ defined reaction rule (switched on) for a specific context
19
19. General Syntax for Reaction Rules
<Rule style="active|messaging|reasoning" eval="strong|weak|defeasible|fuzzy">
<oid> <!– object id --> </oid>
<label> <!– meta data of the rule --> </label>
<scope><!– scope of the rule e.g. a rule module --> </scope>
<qualification> <!– e.g. priorities, validity, fuzzy levels -->
</qualification>
<quantification> <!- e.g. variable bindings--> </quantification>
<on> <!– event part --> </on>
<if> <!– condition part --> </if>
<then> <!– (logical) conclusion part --> </then>
<do> <!-- action part --> </do>
<after> <!– postcondition part after action,
e.g. to check effects --> </after>
</Rule>
20
22. Underlying Semantics: Interval-based Event Calculus
– Events initiate and terminate Situations (Fluents) which hold during a time interval
– Interval-based Event Calculus semantics (model-theory + proof theory) based on
time intervals modeled as fluents
I: Ti x Fl {true, false}
– Example: A;(B;C) (Sequence)
T1 T2 T3 T4
A B C
– Rule-based interpretation of typical complex event detection operators
– Rule-based inference involving times and durations, e.g. Allen‟s interval logic
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23. CEP Use Cases
Current CEP technologies and applications mostly concentrate on
performance (vs knowledge, semantics, provable correctness )
CEP technologies like TIBCO BusinessEvents provide certain level of
semantics:
– UML-level for class, event relationships (inheritance, containment, reference)
– UML-level for state behaviors
– BPMN-level for process behaviors
– UML PRR-level for declarative rule behaviors
– etc.
Utilize essentially fixed-schema data representations (e.g. object
definitions)
Machine learning, currently limited to rule / query parameterization (etc.)
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24. Live CEP Use Cases that Involve Semantics (1)
Logistics
• Optimization of shipping movements in transit
and in port to obtain greater efficiencies and to
reduce fuel costs
• Real-time tracking of cargo, packages border
crossing manifests
• Revenue management, baggage handling in
airlines
Telco
• Track missing events, SLA violations, re-
sequence out-of-order events according to
context
• Identify under-performing business systems to
ensure service levels can be met and enhanced
• Location-based services for targeted campaigns
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25. Live CEP Use Cases that Involve Semantics (2)
Energy
• Predictive energy usage / energy event processing
• Smart Grid Initiatives
• Transmission, Outage Intelligence Fault
Management
• Finance
Credit derivative trades: real-time workflow and
matching of trades
• Visualize market data, order executions
• Fraud / Intrusion Detection
• Track and Trace for Trades/Deals/Settlements and
Pre/Post trade exceptions
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26. Live CEP Use Cases that Involve Semantics (3)
Government
• Track and Analyze „patterns‟ that were otherwise
very difficult to detect
• Real-time tracking of people, places and things
• Capacity planning
Adaptive Marketing
• Capture opportunity with customer while “window”
is open
• Ability to (automatically) learn from
successful/failures of campaign in progress to fine
tune the offer
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29. SCEP Research: Event-Driven Business Process Management
(edBPM) BPMN -> Orchestrated BPEL + Choreography Rule Workflow
Prova Rule Engine
Rules-enabled BPEL+
Application
events, BRMS
(Business Rules
facts Management
System)
CEP Logic
Reaction
Logic % receive query and delegate it to another party
results rcvMsg(CID,esb, Requester, acl_query-ref, Query) :-
Decision
Logic
responsibleRole(Agent, Query),
Constraints
sendMsg(Sub-CID,esb,Agent,acl_query-ref, Query),
BPEL run-time Rule Inference
rcvMsg(Sub-CID,esb,Agent,acl_inform-ref, Answer),
Service
... (other goals)...
SBPMN -> BPEL+
sendMsg(CID,esb,Requester,acl_inform-ref,Answer).
•Paschke, Rule Responder BPM / ITSM Project
•Barnickel, Böttcher, Paschke, Semantic Mediation of Information Flow in
Cross-Organizational Business Process Modeling, 5th Int. Workshop on Semantic
Business Process Management at ESWC 2010
•Adrian Paschke and Kia Teymourian, Rule Based Business Process Execution with
Oryx BPEL+ , i-Semantics 2009, Graz
SBPM • Paschke, A., Kozlenkov, A.: A Rule-based Middleware for Business Process
Execution, at MKWI'08, München, Germany, 2008.
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30. Summary for Semantic Complex Event Processing
Event processing engines understand what is happening
in terms of (top-level and domain) ontologies
Reaction rule engines know what (re)actions and processes they can
invoke and what events they can signal
Makes it possible to detect and process semantically related (complex)
events
Needs a formal semantics to concepts relevant in event processing, e.g.
time, spatio, state, action behavior …
– Also support interchange across domain boundaries with different vocabularies
Many use cases in different industrial domains demonstrate usefulness of
the combination of Semantics (Rules & Ontologies) + CEP
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