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Enabling Knowledge-Based Complex Event Processing Kia Teymourian - - PowerPoint PPT Presentation

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Enabling Knowledge-Based Complex Event Processing Kia Teymourian Research Assistant at Free University Berlin http://www.teymourian.de VLDB 2011, Ph.D Workshop 29 Aug - Sep 3, 2011, Seattle AG Corporate Semantic Web Freie Universitt

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AG Corporate Semantic Web Freie Universität Berlin http://www.inf.fu-berlin.de/groups/ag-csw/

Enabling Knowledge-Based Complex Event Processing

Kia Teymourian

Research Assistant at Free University Berlin

http://www.teymourian.de

VLDB 2011, Ph.D Workshop 29 Aug - Sep 3, 2011, Seattle

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Outline

  • Complex Event Processing (CEP)
  • Why Semantics + CEP?
  • Semantic CEP (SCEP)
  • Knowledge Representation for Events & Event Patterns
  • Real-Time Semantic Event Processing
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Events

  • Anything that happens, or is contemplated as happening
  • A Notification is a message that contains information

about an event that has occurred.

  • Content-based data and filter model:
  • Tuples
  • Structured records
  • Name/value pair (n, v) with name n and value v
  • Samples:
  • {(type, StockQuote), (name, “Siemens”), (price,45)}
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Big Picture of Complex Event processing

Event Producer Event Consumer Event Processing Generate & publish Consume and react on raw/complex events perform operations on events

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CEP vs. Databases ?

Database Database Queries Query Processor Incoming Events Time

? ? ? ?

Processing moment of events Future past Event Subscriptions

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Event Processing Methods

  • Syntactic processing of low-level events
  • Real-time processing

State Machines Petri Nets

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Keep the event data moving!

AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/

Event notifications in real time

Event Stream

Alarms, Actions

Storage

Permanent storage Optional Query over the historical data of Events Optional storage Main memory

Event Engine

Load the event data very short time in main memory

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My Research Challenge Semantic Complex Event Processing (SCEP)

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Research Challenges

Three Main research questions: 1) Can we represent events and event patterns based on

  • ntological background knowledge and use it for CEP?
  • Relation to other non-event concepts, e.g. Situations,

Actions, Actors, Processes, … 2) Is it enough to use Datalog as processing semantic? 3) Is it possible to process the events in timely manner and do inferencing on a background knowledge?

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Example – Semantic Event Processing

AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/

Event Stream

{(Name, “OPEL”)(Preis, 45)(Volumen, 2000)} {(Name, “SAP”)(Preis, 65)(Volumen, 1000)}

Query: Buy stocks of companies, who have in Europe production facilities and produce products from iron and more than 10,000 employees and are at the moment in reconstruction phase and their price/volume increased stable in the past 5 minutes. {(OPEL, is_a, automobil_company), (automobil_company, build, Cars), (Cars, are_build_from, Iron), (OPEL, hat_production_facilities_in, Germany), (Germany, is_in, Europe) (OPEL, is_a, Major_corporation), (Major_corporation, have, over_10,000_employees), (OPEL, is_in, reconstruction_phase)} Knowledge Base

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Knowledge-based Event Processing

Knowledge Base

Events

Event Stream

Complex Events

Event Processing Knowledge Base in OWL / Description Logic T-Box A-Box A-Box Update Stream

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Representation of Events and Event Patterns

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Event Query Representation

  • SQL – Like:
  • Esper , Event Processing Language http://esper.codehaus.org/
  • XchangeEQ , (LMU, Munich)
  • Cayuga Event Language (CEL), Cornel University
  • Declarate Language
  • Prolog
  • Drools Fusion , http://www.jboss.org/drools/drools-fusion.html
  • Rule Core, XML-based rule language http://rulecore.com
  • ETALIS http://code.google.com/p/etalis/ using Prolog
  • Prova http://www.prova.ws Prolog + Java + MAS
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Example: A Semantic Query Language

@prefix fin:<http://csw.fu-berlin.de/fin#>. ACTION{ buy(?S1); } STREAM{ e1:($S1, $P1, $V1), e2:($S2, $P2, $V2) }WHERE{ (?X1, fin:company, $S1), (?X2, fin:company, $S2), (?X1, fin:produce, ?Z), (?Z, fin:buildfrom, mtr:metal), (?X1, fin:facilitiesin, geo:Europe), (?X1, fin:employees, 12,000), (?X1, fin:is_in, fin:reconstruction), (?X2, fin:oilconsume, 2.000.000), }ON{ e2 AFTER e1 , ($P1 * $V1 >= 20000) }WITHIN{ 10 min }

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Complex Events vs. Situations

  • What is a complex event?
  • An event that is an abstraction of other events called its

members (EPTS Glossary)

  • What is a Situation?
  • Is the same as complex event? Or is the result of Complex

Event.

  • Situation calculus, Event calclus?

e1 e2 e3 e4 e5 Time

S1

CE1 CE2

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Event Processing Methods

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Semantic CEP Requirments

  • General CEP Requirements:
  • Timely Processing (real time, near real time)
  • Scalability
  • High throughput of events
  • Number of Processing Rules
  • Special SCEP Requirements:
  • Scalability
  • Size of background knowledge
  • Level of reasoning on KB
  • Frequency of KB updates
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Processing Methods

1)Storage-based 2)Central rule engine 3)Semantic Enrichment of Event Stream (SEES) 4)Event Query Pre-Processing (EQPP)

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Semantic Enrichment of Event Stream e1

Event Stream

e2 e1 Knowledge Base

Derived Events

e1 e2 e1 e1 e2 e1 e3 e2 e1 e4 e3

Semantic Enrichment

Raw Events Complex Events Final Processing

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Event-Query Pre-Processing

  • The complex query is pre-processed and rewritten in

several simple queries.

  • Simple query is a query which can be processed

without the external KB

  • New simple queries can be generated using the

knowledge base. Q ∪ KB q1 , q2 , q3, q4, ... →

  • Simple queries can be in conjunction and disjunction
  • Queries are processed by several Event Processing

Agents

  • Results are jointed together by EPAs
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Event Query Pre-Processing Knowledge Base

Rewrite Simple Queries

e1 e2 e1 q3 q2 q1

Event Query Pre- Processing

e1

Event Stream

e2 e1

Raw Events

Q

Complex Query Complex Events Final Processing distributed on a network of processing Agents Event Processing Network

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Comparison of Methods

DB-Based Rule Engine SEES EQPP Performance

low high limited high

Scalability

limited limited limited high

Elasticity

no no high high

Reasoning on KB

No/limited No/limited high high

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Future Work

  • Representation of Event Patterns/event query
  • Algorithms for rewriting complex event query
  • Prove of concept implementation
  • Evaluation
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Excluded, but related Subjects

  • Noisy event stream
  • Uncertain events stream
  • Event pattern mining
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Thank You!

Please give me feedback! I am here in Seattle until Sunday ...

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Thank you!

http://www.corporate-semantic-web.de

AG Corporate Semantic Web Freie Universität Berlin http://www.inf.fu-berlin.de/groups/ag-csw/