Debs2009 Event Processing Languages Tutorial

epts
event processing technical society

Event Processing
Language Tutorial
François Bry, Michael Eckert, Opher Etzion, Adrian Paschke, Jon Riecke
on behalf of the epts languages analysis working group

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TUTORIAL OUTLINE

• Introduction Opher Etzion
• stream processing languages Jon Riecke
• rule oriented languages Adrian Paschke Languages
Styles
Break
• Agent oriented languages Opher Etzion
• Temporal semantics Jon Riecke
Advanced
• Integrated development environments Jon Riecke
Topics
• Formal approaches Adrian Paschke
• Conclusion Opher Etzion

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TUTORIAL OUTLINE

• Introduction Opher Etzion
• stream processing languages Jon Riecke
Styles
Break
Advanced
Topics

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What is it all about?
Filter, transform, enrich, route,
Detect patterns, derive events

Event Processing

Event Event
Producer Consumer

Generate and emit Consume and react
events perform operations on to either raw or
events complex events

Event processing has
gained a lot of interest in
recent years; according to
analysts it is the fastest
growing segment of
middleware software

In this tutorial we are Pattern
exploring the language Match
issue – how the various
event processing
functions are expressed?

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An Observation The Babylon Tower symbolizes the tendency
Of humanity to talk in multiple languages.

The Event Processing area is no different: most languages in the industry really follow
the hammer and nails syndrome – and extended existing approaches
• imperative script language
• SQL extensions
• Extension of inference rule language

It does not seem that we’ll succeed to settle
In the near future around a single programming style

The epts language analysis workgroup is aimed to understand the various styles
And extract common functions that can be used to define what is an event
processing language; this tutorial is an interim report

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Existing Styles for EP languages (samples)
Inference TIBCO
Rules
Prova WBE XChangeEQ
ECA
Rules RuleCore
AMiT
Starview Agent Agent Logic
Oriented
EventZero Spade
SQL
Aleri extension Streambase

Coral8
State Oracle
oriented
Esper

Imperative/
Netcool Impact Script Based Apama
* - if we add simple and mediated event processing the picture is even more diversified
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Dimensions to describe languages

• Events Data and Meta-data
• State
• Computation/execution model
• Programming model

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The event Data and meta-data dimension

• Event structures supported (structured, semi-
structured, unstructured)
• Data types supported for event’s payload
• Event identifiers/identities
• Event time-stamps and ordering
• Event relations
• Other meta-data entities in the language

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The state dimension

• State of events / data
– Events and other data in states
– Life-cycle, duration policies
– Explicit visibility and manipulation of states
• State of execution
– Context-related grouping (e.g. time windows)
– Explicit visibility into state of execution

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The execution model dimension

• Execution timing – immediate or deferred ?
• Guarantees: real-time, determinism, latency ?
• Time point or time interval semantics
• Concurrency – implicit / explicit
• Event at a time / set at a time processing
• Explicit event flow

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The programming model dimension

• Programming style
• Abstractions
• Pattern matching
• Enrichment capabilities
• Transformation capabilities
• Filter capabilities

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TUTORIAL OUTLINE

Introduction Opher Etzion
stream processing languages Jon Riecke
Styles
Break
Advanced
Topics

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Hallmarks of Stream Processing

• Events are processed in a directed graph
• Nodes = computational elements
• Edges = communication of events between nodes
• Languages inspired by SQL/relational algebra (though not
all use an extended SQL as the language)
• Difference: write the query first
• Results of query are updated as data flows in
• Academic examples: STREAM, Aurora, Cayuga, …
• Commercial examples: Aleri, Coral8, Esper/Nesper,
StreamBase, System S, …

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Some pictures

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Some pictures

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Some pictures

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Historical Roots

• Relational Databases
• Triggers: change table when another table changes
• Materialized view maintenance: calculate small set of
changes to view given set of changes to base tables
• Signal processing
• Control and Audio: Ptolemy, LabVIEW, …
• Music and video: OpenMusic, jMax, Max, vvvv,

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Historical Roots: Static Dataflow Architecture

• Developed by Jack Dennis in 1960s
• Each arc carries one token
• Node fires when all data available on all arcs
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Historical Roots: Dynamic Dataflow Architecture

• Matching section in front of each node
• Tokens are tagged by invocation instance; when all
tokens with same tag are ready, node fires
• Allows more parallelism than static model
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Kahn Dataflow Networks

• Non-blocking write/blocking read queues
• Deterministic nodes implies deterministic network
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Stream Processing versus
Signal Processing and Dataflow Architecture

• Events carry more information than simple data:
records instead of, e.g., decimal numbers
• Slightly slower data rates (up to millions of events
per second)
• Publish/subscribe model of input/output

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Dimension Commonalities in Stream Processing

• Events: Non-mutable records (fields with values)
Values have scalar types (integer, float, string, blob, xml)
• Metadata: describes streams and types of records
• State: Unprocessed and processed events, and internal
state of operations (e.g., aggregations)
• Computational model: records traverse the dataflow graph
• Programming model: extensions of SQL/relational algebra

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Dimension Differences

• Events: small differences in scalar types
• State
• User-defined data structures (arrays, maps, …)?
• Disk-based persistence? What is persisted?
• Visual programming or text-based?
• Embedded in larger language, e.g., Java?
• Smaller language embedded in it?

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Dimension Differences

• Computational model
• Cycles in dataflow graph?
• Deterministic?
• Consistent? What form of consistency?
• Parallel or concurrent execution?
• Distributed computation? Distributed data?
• Synchronous or asynchronous communication?

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Aleri Streaming Platform Dataflow Networks

• One queue per node (instead of per edge)
• Nodes store records in table
• Events (insert/update/delete) change the table
• Nodes run in separate threads
• Optional persistence with roll-forward recovery
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• Events may arrive in different orders, and may cause
different output events
• Correctness: standard operators (join, compute, …)
satisfy “eventual consistency”: the sequence of
insert/update/deletes will produce the same table, no
matter how the events flow through the graph
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B

A C

• Node A sends insert:[Id=3, Symbol=IBM, Price=45.0] Node B sends update:
[Symbol=IBM, AvgPrice=43.40]
• Different arrival orders can cause different events
• (A then B)
• insert: [Id=3, Symbol=IBM, Price=45.0, AvgPrice=42.00]
• update: [Id=3, Symbol=IBM, Price=45.0, AvgPrice=43.40]
• (B then A)
• insert: [Id=3, Symbol=IBM, Price=45.0, AvgPrice=43.40]

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Coral8 Dataflow Networks

• No queues; synchronization buffer in front
• Two kinds of nodes:
• Stream nodes process records but don’t store them
• Window nodes process and store records

• Optional persistence for unprocessed events, records
28 in windows, aggregate state

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• Computational model (simplified)
• External data flows into synchronization buffer
• Events carry a timestamp (point in time, microsecond
granularity): set on arrival, or by source
• Events with same timestamp are fed into the network in
one step, and are processed until network quiesces
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• Deterministic: CCL programs produce the same output
events given the same input
• Priorities assigned to nodes in graph to maintain
determinacy

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• Computational model: concurrent/distributed
• Project = group of CCL streams, windows, queries
(code for calculating new events from old)
• Projects can send/receive events to/from other projects
• Each project has a thread (there are other threads too)

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• Projects can be distributed across machines

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Examples in CCL: Simple Market Data

• Schema: type of records
– CREATE SCHEMA Trades_t
– (Symbol STRING, Qty INTEGER, Price FLOAT);
– CREATE SCHEMA Book_t
– (Symbol STRING, Qty INTEGER, BuyPrice
FLOAT);

• Other scalar types: BOOLEAN, TIMESTAMP,
INTERVAL, XML, BLOB

• CCL streams: INPUT, OUTPUT, local
– CREATE INPUT STREAM Trades_s SCHEMA Trades_t;
– CREATE INPUT STREAM Book_s SCHEMA Book_t;

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CCL Windows

• Windows are like streams, but store records
– CREATE WINDOW Book_w SCHEMA Book_t KEEP ALL;
– INSERT INTO Book_w
– SELECT * FROM Book_s;

• Sample of KEEP policies (there are others):
– KEEP LAST PER Id
– KEEP 3 MINUTES
– KEEP EVERY 3 MINUTES
– KEEP UNTIL (”MON 17:00:00”)
– KEEP 10 ROWS
– KEEP LAST ROW
– KEEP 10 ROWS PER Symbol

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CCL Queries: SQL-based syntax

• Aggregation
– CREATE STREAM Vwap_s SCHEMA (Symbol STRING, Vwap FLOAT);
– INSERT INTO Vwap_s
– SELECT Symbol, sum(Qty * Price)/sum(Qty)
– FROM Trades_s KEEP 30 MINUTES
– GROUP BY Symbol;
• Join
– CREATE SCHEMA Value_t
– INHERITS FROM Book_t (CurVal FLOAT, AvgVal FLOAT);
– CREATE WINDOW BookValue_w
– SCHEMA Value_t KEEP LAST PER Symbol;
– INSERT INTO BookValue_w
– SELECT B.Symbol, B.SharesHeld, B.BuyPrice,
– B.SharesHeld * L.Price, B.SharesHeld*V.Vwap
– FROM Book_w B, LastTrade_w L, Vwap_s V
– WHERE B.Symbol = L.Symbol and B.Symbol = V.Symbol;

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CCL Pattern Matching

• MATCHING clause
– INSERT INTO OrdersMatch_s
– SELECT B.ID, S.ID, B.Price
– FROM BuyOrders_s B, Trades_s T, SellOrders_s S,
– MATCHING [30 SECONDS: B, !T, S]
– ON B.Price = S.Price = T.Price;

• Operators in patterns (cf. finite-state automata):
• Three boolean operators: ! (not), & (and), | (or)
• One temporal operator: , (followed by)
• Temporal scope can be nested
– MATCHING [30 SECONDS: [10 SECONDS: B, !T], S]

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Modularity
• Note: concrete syntax in future version of CCL
• Group of streams can be abstracted into a module
– CREATE MODULE OptionPrice
– CREATE INPUT STREAM Option_s;
– CREATE OUTPUT STREAM OptionPrice_s;
– CREATE PARAMETER FLOAT InterestRate = 0.05;
– // internal stream definitions
– END MODULE;

• Modules can be instantiated
● LOAD MODULE OptionPrice as LondonOptionPrices
● STREAMS
● Input = LondonOptions_s,
● Output = LondonOptionPrices_s
● PARAMETERS
● InterestRate = 0.03;
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Other Features from Other Languages

• Esper (Java)/Nesper (C#)
• Extensions of SQL
• Embedded within host language
• AleriML
• SPLASH: a C-like embedded language for writing nodes
• Data structures: vectors, dictionaries, event caches
• Mechanisms for discarding stored records (time, # records)
• StreamBase
• Primitive types include nested records and vectors (lists)
• LOCK/UNLOCK for holding and releasing records

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TUTORIAL OUTLINE

stream processing languages Jon Riecke
Rule oriented languages Adrian Paschke Languages
Styles
• Break
Advanced
Topics

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Production Rules

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Production Rules and CEP
• Production rules react to states changes (not events)
• PR systems with object model and external fact updates might
be extended to CEP
– Event types and classes are defined in the rule declarations
– New event data instances are added to the fact base / working
memory
• might be an external fact base, e.g. a event queue
– The instances of declarations are filtered and joined in the
conditions via typed pattern matching and if they pass the condition
list the action part of the rule is triggered
• Might be further extended with mechanisms like query
languages, state models and temporal constraints
• Examples: TIBCO Business Events, Drools
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Production Rules

• A production rules is a statement of the form:

“if Condition then Action”

• Members, e.g. OPS5, Clips, Jess, Drools, Fair Isaac Blaze
Advisor, ILog jRules, CA Aion, Haley, ESI Logist, …

• Operational semantics: forward-chaining
– primitive update actions (assert, retract, …);

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Operational Semantics

• Operational processing
– forward-chaining order-independent execution (e.g. based
on variations of the RETE algorithm)
– Procedural order-dependent sequential execution in a
(compiled) execution environment (rule engine).

• Conflict resolution
– Refraction
– Priority
– Recency
– Specificity
– …

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The Rete Pattern-Matching Algorithm

• Rete Pattern-Matching Algorithm (and variations of it)
– matches facts against the patterns in rules to determine which rule
conditions are satisfied
– incremental evaluation of production rules' conditions

• Developed by Charles L. Forgy in the late 70s for OPS5 (Official
Production System) shell
– stores information about the antecedents in a network
– in every cycle, it only checks for changes in the networks
 this greatly improves efficiency

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OMG Production Rules Representation (OMG PRR)

• Formal model for vendor-neutral rule-model
representation in UML for production rules
• Consortium of developers and supporters
– Rule vendors (including Fair Isaac, IBM/ILOG, LibRT, Pega,
Corticon, TIBCO)
– Academic community (RuleML Group)
– Related vendor community (Fujitsu)
• PRR is currently “adopted" as a standard and in
“finalization", meaning that it is in Beta, with a final
version 1.0 in 2009.

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OMG Production Rules Representation

• OMG MDA PIM model
• PRR beta spec applies to a PRR Core using OMG
MOF defined in UML
– Extends UML so production rules are 1st class citizens
alongside objects
– Spec. excludes an explicit expression language
• Can use other standards as expression language
– e.g. W3C Rule Interchange Format (W3C RIF) and the
production rule language of RuleML

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OMG PRR ProductionRule Classes

46 if [condition] then [action]

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W3C Rule Interchange Format
• W3C Rule Interchange Format (W3C RIF)
http://www.w3.org/2005/rules/wiki/RIF_Working_Group
• Goal
Standardization of a web-based Rule Interchange Format (RIF)

• RIF 1.0 three dialects:
– RIF Production Rules Dialect (RIF-PRD)
• specifies the RIF production rules dialect to enable the interchange
of production rules
– RIF Basic Logic Dialect (RIF-BLD)
• specifies a basic interchange format that allows logic rules (definite
Horn rules with equality) to be exchanged
– RIF Core
• RIF-Core, a common subset of RIF-BLD and RIF-PRD
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W3C RIF Production Rules Dialect
• Production Rules (Condition-Action)
– Based on RIF condition language (from RIF Core)
– Actions: Retract, Assert, Modify, Execute
– Negation (inflationary negation)
• Syntax
– Normative XML syntax
• Might be concrete representation language for OMG PRR
– Non-normative EBNF-based presentation syntax
• for presentation purposes

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RIF Production Rule

<Implies>
<if> FORMULA </if>
<then rif:ordered="yes">
<Do>
ACTION*
</Do>
</then> Syntax close to PR
RuleML syntax
</Implies>

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Event Condition Action Rules

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Event Condition Action Rules

• ECA Rule
“on Event if Condition do Action”;
– Explicit complex event part; separated from conditions and
actions
• e.g. expresses customer order (event); check if credit card is
valid (condition)

• Evolved from active databases
– extend databases with reactions, e.g. HiPac, ACCOOD, Chimera,
ADL, COMPOSE, NAOS
– Composite event algebra, e.g. SAMOS, COMPOSE, Snoop
• Sequence | Disjunction | Xor | Conjunction |
Concurrent | Not | Any | Aperiodic | Periodic

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Time-point vs. Interval-based Semantics

– Time-point semantics
• Event definition: B; (A;C) (Sequence)
event instance sequence EIS = b, a, c => detect event
event instance sequence EIS = a, b, c => detect event
– Interval-based semantics
• Event definition B;(A;C) (Sequence)
event instance sequence EIS = b, a, c => detect event
event instance sequence EIS = a, b, c => do not detect event

T1 T2 T3 T4

B A C

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1. Specification Phase
– 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 of a particular type in an event instance
sequence, 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

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1. Consumption
– Optionally defines which events are consumed after the detection of a
composite event
– An event may contribute to the detection of several composite 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
2. 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.
– Actions might have an external side effect

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Example: REWERSE MARS

• MARS (Modular Active Rules for the Semantic Web)
– rule uses specialized heterogeneous event language (E), one
or more query languages (Q), a test language (T), and an
action language (A)
– each of these languages and their constructs are described by
metadata and an ontology of its semantics
– well-defined interface for communication between the E, Q&T,
and A components by variables
– SNOOP (E), OWLQ (Q&T) and CCS - Calculus of
Communicating Systems (A)

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Example: XChangeEQ

• ECA rules ON Event query IF Web query DO Action
– the condition part is a declarative query to XML data
– the event part is as well a declarative query to XML event
data
• distributed and web-based: events issued by web nodes (with both
emission and reception times)
– - action: simple or complex updates send to web nodes
• Complex events are expressed as (compound) queries to the
event stream
– temporal restrictions and event compositions
• conjunction, disjunction, exclusion, quantifications, repetitions, and
ranks
• absolute temporal restrictions, also relative temporal restrictions
(within)
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XChangeEQ
• The four dimensions of event querying are kept separated:
– data extraction (eg oder number, book title)
– event composition (eg oder of a dictionary and a grammar both from the same
publisher)
– temporal (and other) relationships (eg two orders within a week)
– event accumulation (eg no default of paiement between the order of the two
abovementioned buys)

• Equivalent Semantics:
– Declarative semantics: Model and fixpoint theories
– Operational semantics: CERA (Complex Event Relational Algebra)

• XChange^EQ operational semantics:
– by language design, the system never wait forever for a complex event to possibly
resume.
– Incremental evaluation (refining RETE).
– Built-in garbage collection of irrelevant complex events (eg events that cannot
resume).

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Example: XChange Composite Event

andthen [
xchange:event {{
xchange:sender {"http://airline.com"},
cancellation-notification {{
flight {{ number { var Number } }} }}
}},
xchange:event {{
xchange:sender {"http://airline.com"},
important {"Accomodation is not granted!"}
}}
] within 2 h

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ruleCore Markup Language (rCML)

• XML language for ECA rules
• Complex Event defined in four sub-elements:
– <detect-event>. The <detect-event> element specifies the
event that is generated when the composite event is detected.
– <no-detect-event>. The <no-detect-event> element specifies
the event that is generated when the composite event can not
be detected.
– <event-selector> The <event-selector> element specifies a
logical condition (or filter) for the composite event.
– <detector>. The composite event detector consists of a number
of event algebra operators (the SNOOP algebra operators) that
describe the composite event.

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IBM AMIT Situation Manager Rule Language
• Definition of situations and their detection conditions
– syntactically equivalent to (complex) event patterns
– lifespans which hold references to events with relevant context for a
particular pattern calculation
• Conceptual event model defines an event type generalization
hierarchy
– Root event type defines a set of standard attributes e.g.,
• the event source, creation type, temporal dimensions like occurrence time,
detection time and /or transaction time of an event, and quantifications such
as status, priority, severity, count
– Application specific event types inherit from root event type and define
further user-defined attributes
– Two types of event relationships
• Based-on - a pure syntactical relationship to ease the development
process. If an event A is 'basedon' event B, event A inherits the entire
structure of event B and can extend it.
• Reference - a relationship that allows deriving events without pattern
detection. Useful for transformation and enrichment purposes.
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Example AMIT SMRL
AMIT SMRL markup language provides an event algebra with semantic notations such as
context (lifespan intervals), and semantic association (event group) to define situations

<eventTypes>
<eventType name="doctorEnterRoom">
<attributeType name="doctor" xsi:type="string"/>
<attributeType name="room" xsi:type="integer"/> </eventType>
<eventType name="patientEnterRoom">
<attributeType name="patient" xsi:type="string"/>
<attributeType name="r_o_o_m" xsi:type="integer"/>
</eventType>
<eventType name="lockRoom">
<attributeType name="room" xsi:type="integer"/> </eventType>
<eventType name="unlockRoom">
<attributeType name="room" xsi:type="integer"/>
</eventType>
</eventTypes>
<lifespan name="roomOpen">
<initiator> <eventInitiator name="unlockRoom"/> </initiator>
<terminator> <eventTerminator name="lockRoom"/> </terminator>
<keyBy name="sameRoom"/>
</lifespan>
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(Reaction) RuleML

• General (reaction) rule form that can be specialized as needed
• Platform-independent XML-based rule interchange format
– translation into platform-specific executable rule languages, e.g. Prova
• Three general execution styles:

– Active: 'actively' polls/detects occurred events in global ECA style, e.g. by a ping on
a service/system or a query on an internal or external event database
– Messaging: Waits for incoming complex event message
– Reasoning: KR event/action logic reasoning and transitions (as e.g. in Event
Calculus, Situation Calculus, TAL formalizations)
• Appearance

– Global: ‘globally’ defined reaction rule
– Local: ‘locally’ defined reaction rule in a specific context

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General Syntax for Reaction Rules
<Rule style="active" eval="strong">
<on>

</on>
<if>

</if>
<do>

</do>
<ifPost>

</ifPost>
<doAlternative>

</doAlternative>
</Rule>
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Selected Reaction RuleML Extended Features
• Action Algebra:
Succession (Ordered Succession of Actions), Choice
(Non-Determenistic Choice), Flow (Parallel Flow), Loop
(Loops)
• Event Algebra:
Sequence (Ordered), Disjunction (Or) , Xor (Mutal
Exclusive), Conjunction (And), Concurrent , Not, Any,
Aperiodic, Periodic
• Support for event / action messaging
• Support for different detection, selection and consumption
policies
• Support for intervals (Time, Event)
• Support for situations (States, Fluents)
• Support for external event query languages
• ...
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10 minutes break
TUTORIAL OUTLINE

 rule oriented languages Adrian Paschke Languages
Styles
Break
Advanced
Topics

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TUTORIAL OUTLINE

Styles
 Break
• agent oriented languages Opher Etzion
Advanced
Topics

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EPA and EPN

• In David Luckham’s “the
power of events” the term
EPN (Event Processing
Network) was used as a
collection of EPAs (Event
Processing Agents).
• The EPN describes the c1
p1

admittance
Filter: Diabetes
p5
c5

EPN Example
“programming in the large”, Pattern detection:
Alert physician
p6

p7
p12

while each individual agent Blood pressure reading
p8
Enrich:
p10
p11

describes the
c2 c6
p2 p9 Is diabetic?
p12
Aggregator: Max.

“programming in the
c7
Fever C p14 Translate: p13
c3 C2F fever p19
Fever F p3

small”. p4
Fever F

c8
p15
Pattern Detection:
Alert Nurse

p16
p20
Pattern Detection: p18
Continuous Fever

p17 c9

IBM Haifa Research Lab – Event Processing © 2008 IBM Corporation

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Some EPN, event flow and data flow oriented languages

Agent Logic Event Zero Oracle/BEA

Spade (IBM
System S)
IBM InfoSphere Streams Streambase
A SPADE program builds a data-flow network out of operators

source stream processing sink
operator operator operator
m
str

ea
ea

str
m

processing stream processing
operator operator
stre

m
ea
am

str

source sink
operator operator

68
194 Tutorial on the SPADE language © 2008, 2009 IBM Corporation

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What is the idea ?

• The EPN (event processing network) represents the event
processing application as a directed graph:
– The nodes represent event processing agents (and states in some
models)
– The edges represent either individual events or event streams
(depends on the processing type). A generalized EPN may support
both.
• The coverage of EPN varies
– It may cover some language operator (e.g. SQL like language)
– It may take a broader approach and cover also routing decisions, pub/
sub etc…
– EPN can have a specific programming model, or an hybrid
programming model, where one or more languages represent the
agents.

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Anatomy of Agents

Performs the processing
Selects the events that are
Derives output events
relevant for the processing detect patterns)
(e.g.

Selector Processor Deriver

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Event Processing Specialized Agent types
Event Processing Agent

Filter Transform Detect Pattern Route

Translate Aggregate Split Compose Enrich Project

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Routing – channel EPA

Itinerary-based routing

Subscription-based
routing

Intelligent routing

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Making the agents context sensitive
Time interval that starts by: {event, clock, offset from event}
and terminates by: {event, clock, offset from event, count of
certain events} – each can have multiple instances (e.g. several
events, periodic clock, multiple offsets}
When?
Examples: [9:00AM, angry-customer-alert], [angry-
customer-alert, +1 hour], [request – 2 hours,
request]

Spatial dimensions/ abstraction
Where? Inside building A, In Italy, within 1KM from here
(given GPS coordinate)

An entity or a collection of entities that determine the partition
of the context
Who? Class of customer, customer + order, large order…

The event happens when some state is in effect

Which state?
Traffic Jam, Red Alert, Bullish Market

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Filtering

Many of the raw events are not
relevant for processing, in some
applications only a sample is
selected for processing.

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Transformation

Translation / projection:
Translation and simple
derivations

Aggregation/ composition:
statistical aggregator or
concatenating events, can be
stand alone agent or a scalar
derivation. May be in network
edge.

Splitting: Splits events to
multiple events

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Enrichment

Enriches the content of events
from reference data in databases,
spreadsheets, Email messages,
text files etc..

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Pattern Matching Pattern definition
example

In many cases, the agent does not
consume raw events, but event that
are derived from pattern matching.

DEBS 2008 tutorial on
event processing patterns
provided a deep dive
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Dimensions - I

• Meta-data
– EPN and agent definitions may be defined as meta-data
– Event schema is implementation dependent
• State
– State can be represented as another node in the EPN
– Some EPAs may be state oriented, e.g. the input event is
change in state
– State is one of the context dimensions

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Dimensions - II

• Execution model:
– EPN representation is natural one for
concurrency/parallelism since the dependencies are
explicit
– Since it models explicitly the end-to-end event flow, it
serve as a convenient model for QoS related
optimizations (latency, throughput, real-time constraints)
• Programming model:
– Can support multiple programming styles
– Can support all types of event processing functionality

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p1 p5
c1 c5
Filter: Diabetes
admittance
p6
EPN example Pattern detection: p12
Alert physician p7

Blood pressure reading p11
p8 p10
c2 Enrich:
p2 Is diabetic? c6
p9
p12
Aggregator: Max.
c7
Fever C p14 Translate: p13
c3 C2F fever p19
Fever F p3
Fever F p15
Pattern Detection:
p4 Alert Nurse
c8
p16
p20
Pattern Detection: p18
Continuous Fever

p17 c9

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Event Zero example

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System S Example

Congestive Heart Failure
Freq alert
Arrhythmia
Analyzer

QRS RR P and T

Source PE Glucose
alert
BP S/D CHF

Weight trend IHE
detector
Adapter

*Daby Sow
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Agent Logic example

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TUTORIAL OUTLINE

Styles
 Break
agent oriented languages Opher Etzion
Advanced
Topics

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Varieties of Temporal Semantics

• Do events carry timestamps? Where does the timestamp
come from (CEP system or external world)?
• Granularity? Second, millisecond, microsecond, …?
• Point in time or interval of time?
• Can events arrive out-of-order from a single data source?
• Can events be reordered if they arrive out-of-order?
• How long does the system wait?
• What happens if system moves ahead, and old event arrives?
• In distributed case, is there a difference between emission
and reception time?

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Simple Temporal Semantics

• Events arrive in order from a single data source
• Events have timestamps, but not reordered
• Advantage
• Process as soon as possible
• Disadvantages
• Non-deterministic
• Hard to manage if time really matters
• Emission times de-synchronized in distributed case

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Global Clock

• Events have timestamps consistent across data sources
• Wait for events for specified time; discard if wait too long
• Advantages
• Deterministic
• Time might be relevant
• Disadvantages
• Speed of processing
• Buffering required for reordering events

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TUTORIAL OUTLINE

Styles
 Break
 Temporal semantics Jon Riecke
Advanced
Topics

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Integrated Development Environments (IDEs)

• Two main forms
• Text based
• Visual or graphical based
• Not an either-or; most systems have some
combination of text and visual display of dataflow

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Visual IDEs

• Streams = nodes, flows = edges
• Drag-and-drop streams from palette onto canvas
and wire together
• Configure streams with property sheets or text
• Advantages: simple metaphor; easy to see flow
• Disadvantages: managing screen real-estate

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Visual IDE Example

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Text-based IDEs

• Streams represented in text
• Advantages
• Appeals to programmers
• Can be quicker to write code
• Disadvantages
• Hard to see the data flow
• Hard to find bottlenecks in program

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Text-based IDE

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Another Text-based IDE

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Advanced Features in IDEs

• Ability to start/stop programs
• Debugging, including viewing intermediate events
and setting breakpoints
• Tests, including ability to control data feeds
• Dashboards for displaying results

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Breakpoints and Debugging

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Performance Monitoring

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Dashboard Construction

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Dashboard Construction

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Assessment: IDEs still have a ways to go

• Need more features that programmers have come
to expect
• Single-stepping with line numbers
• Examination of events and data
• Forward and backward replay of time
• Need to be integrated into more dashboards and
other visualization tools

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TUTORIAL OUTLINE

Styles
 Break
Advanced
 Integrated development environments Jon Riecke
Topics

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Formal Approaches
Temporal Event/Action Logics for
Reasoning on Changes

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(Temporal) Event Logics

• (Temporal) event/action logics
– Events with effects on changeable properties / states / fluents
– Focus: reasoning on effects of events/actions on knowledge
states and properties
– Not Complex Event Processing Languages, but can be used
in reasoning on/with complex events
• Members e.g.
– event calculus and variants,
– situation calculus,
– features and fluents calculus,
– various (temporal) action languages (TAL),
– fluent calculi and versatile event logics.
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The Situation Calculus

• A situation is a snapshot of the
world at some point in time
• Every true or false statement is
made with respect to a
particular situation

• When an agent performs an action A is situation S1, the
result is a new situation S2
• McCarthy 1963, McCarthy & Hayes 1969, Green 1969

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Event Calculus
• Kowalski and Sergot’s EC
– a formalism for temporal reasoning about events and their effects
• computation of earlier events (long-term "historical" perspective)
– model of change
• events happen at time-points and initiate and/or terminate properties (time-
varying fluents) of the world
– Law of inertia:
Things normally tend to stay the same

• Main differences in EC to SC
– branching time in SC vs. linear time in EC
– explicit notion of previous state of the world / situation in SC
– state transitions are functions in SC

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Classical Event Calculus Example
• EC Basic Axioms:
– happens(E,T) event E happens at time point T
– initiates(E,F,T) event E initiates fluent F for all time>T
– terminates(E,F,T) event E terminates fluent F for all time>T
– holdsAt(F,T) fluent F holds at time point T

• Many EC Extensions, e.g.:
– valueAt(P,T,X) parameter P has changeable value X at time point T
– planned(E,T) event E is believed to happen at time point T

Example:
initiates(stopService,serviceUnavailable,T) stopService startService
terminates(startService,serviceUnavailable,T)
happens(stopService,t1); happens(startService,t5)
t1 t3 ? t5 t7 ?
holdsAt(serviceUnavailable,t3)?  true
holdsAt(serviceUnavailable,t7)?  false

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Example: Interval-based Event Calculus Event Algebra

– Event initiate and terminate Situations (Fluents) which hold at an 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: B;(A;C) (Sequence)

T1 T2 T3 T4

A B C
(A;B;C) [T1,T3]) <=
holdsInterval([a,b] [T1,T2]) Λ holdsInterval([b,c] [T2,T3]) Λ [T1,T2]<=[T2,T3]

– Rule-based implementation of EC event algebra, e.g. as meta logic program
– Rule-based arithmetic involving times and durations, e.g. Allen’s interval logic
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Combinations of Rule Types
1. Rules that influence the operational / decision processes:
• Derivation rules (deduction rules): establish / derive new
information that is used e.g. in a decision process.
• Reaction rules that establish when certain activities should place
(e.g. ECA rules)

2. Constraints on system/organisation's structure, behavior or information:
• Structural constraints (e.g. deontic assignments), State constraints,
Process / flow constraints

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Example: Prova Messaging Reaction Rules
%Event Message
rcvMsg(EventID, jms, Requester, acl_query-ref, Task) :-
%Condition(s) – “find available service”
available(Service), variable data binding
%Action Message – “load task on service”
sendMsg(Sub-ID,jms,Service,acl_query-ref, load(Task)).

% Derivation rules
available(S) :-
service(S),
not(maintenance(S)),
not(loaded(S),T).

maintenance(S):- … automatic rule chaining

loaded(S) :- …
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Messaging Reaction Rules in Prova

• Send a message
sendMsg(XID,Protocol,Agent,Performative,[Predicate|Args]|Context)

• Receive a message
rcvMsg(XID,Protocol,Agent,Performative,[Predicate|Args]|Context)

• Receive multiple messages
rcvMult(XID,Protocol,Agent,Performative,[Predicate|Args]|Context)

Syntax:
– XID is the conversation / event instance sequence identifier
– Protocol: ESB transport protocols (>30 e.g. self, jade, jms, soap,…)
– Agent: denotes the target or sender of the message
– Performative: pragmatic context, e.g. FIPA ACL primitives
– [Predicate|Args] or Predicate(Arg1,..,Argn): Message payload

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TUTORIAL OUTLINE

Styles
 Break
Advanced
 Integrated development environments Jon Riecke
Topics
 Formal approaches Adrian Paschke
Conclusion Opher Etzion

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Event Processing Languages – insights from this tutorial

epts

epts

An Observation
Dimensions toTower symbolizeslanguages
The Babylon describe the tendency
Of humanity to talk in multiple languages.

• Events Data and Meta-data
• State
• no different: most languages in the model
The Event Processing area is Computation/execution industry really follow
the hammer and nails syndrome – and extended existing approaches

• imperative script language
• SQL extensions
• Extension of inference rule language

It does not seem that we’ll succeed to settle
In the near future around a single programming style

The epts language analysis workgroup is aimed to understand the various styles
And extract common functions that can be used to define what is an event
processing language; this tutorial is an interim report
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Towards event processing languages standard ?
• Eventually – a single language ?
1st generation
SQL standard approved
of relational DBMS products

Mid 1970-ies 1990

1st generation
EP standard approved
of EP products

Mid 200-ies ?

• OR – variety of language style standards ?

• Meta-language as an intermediate standard ?
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The epts “Event Processing Language Analysis” workgroup
Mission:
The purpose of this working group is to conduct a study of the features that exist in the contemporary and planned languages
in the area of event processing (including event stream processing).
The goal is to understand the functional properties that exist in event processing languages and abstract out semantic functions,
regardless of syntax and implementation considerations.
This is a first step in a way to determine future possible standardization in the event processing area

WG Leaders: Opher Etzion (IBM) and Jon Riecke (Aleri)
Members:
• Alex Kozlenkov (Betfair)
• Adrian Paschke (FU Berlin)
• Arno Jacobsen (U of Toronto)
• Bala Maniymaran (U of Toronto)
• Bob Hagmann (Aleri)
• David Tucker (EventZero)
• Dermot McPeake (FirstDerivatives)
• Francois Bry (LMU)
• Guy Sharon (IBM)
• Louis Lovas (Progress/Apama)
• Michael Eckert (TIBCO)
• Pedro Bizarro (University of Coimbra)
• Richard Tibbetts (Streambase)
• Robert McKeown (IBM)
• Susan Urban (Texas Tech. University)
• Simon Courtenage (Westminister U.)
• Serge Mankovskii (CA)

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If you want to participate in these discussions, contribute to this
topic and/or others topics, and be part of the community effort to
advance the state of the art and state of the practice:
JOIN EPTS
For details: http://www.ep-ts.com/

Debs2009 Event Processing Languages Tutorial

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