Event Processing on the Web

7/28/2019 Event Processing on the Web

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Event Processing on the Web

Conceptual Overview, Current Trends and Technologies, and

Challenges

Srdjan Komazec

www.sti-innsbruck.at

© Copyri ght 2011 STI INNSBRUCK www .sti-i nnsb ruck .at



Outline

• Motivation

• Technical solutions• Possible extensions

• Conclusions

• References

www.sti-innsbruck.at 2



Are we there yet?

Motivation




Motivation A predic tion

y , w re ess y ne wor e sensors n every ng

we own will form a new Web. But it will only be of

value if the ‘terabyte torrent’ of data it generates canbe collected, analyzed and interpreted.*


* Mark Raskino, J ackie Fenn, and Alexander Linden. Extracting Value From the Massively Connected World of 2015.

Gartner Research, 1 April 2005. http://www.gartner.com/resources/125900/125949/extracting_valu.pdf



Motivation A predic tion

Are we there yet?




MotivationSemantic Sensor Web

• Semantic Sensor Web*

– Collecting and processing avalanches of data about the world around us while relying

information for situational knowledge.


*

Amit Sheth, Cory Henson, Satya S. Sahoo, "Semantic Sensor Web," IEEE Internet Computing, pp. 78-83, J uly/August, 2008



MotivationSemantic Sensor Web

• Linked Sensor Data*

– Hurricane and blizzard observations in the United States.

– epar men o e eroogy a e nvers y o a .

– Measurements of phenomena such as temperature, visibility, precipitation, pressure,wind speed, humidity, etc.

…sens- obs: Observat i on_Wi ndSpeed_3CLO3_2005_10_16_9_35_00

a weat her : Wi ndObser vat i on ;om- owl : obser vedPr oper t y weat her : _Wi ndSpeed ;

om- owl : pr ocedur e sens- obs: Syst em_3CLO3 ;om- owl : r esul t sens- obs: MeasureDat a_Wi ndSpeed_3CLO3_2005_10_16_9_35_00 ;om- owl : sampl i ngTi me sens- obs: I nst ant _2005_10_16_9_35_00 .

sens- obs: Measur eDat a_Wi ndSpeed_3CLO3_2005_10_16_9_35_00a om- owl : Measur eDat a ;om- owl : f l oat Val ue " 17. 0" ^̂ xsd: f l oat ;om- owl : uom weat her : mi l esPer Hour .

sens- obs: I nst ant _2005_10_16_9_35_00a owl - t i me: I nst ant ;owl - t i me: i nXSDDat eTi me " 16- 10- 2005T09: 35: 00^̂ ht t p: / / www. w3. org/ 2001/ XMLSchema#dat eTi me".

…


* Harshal Patni, Cory Henson, and Amit Sheth. Linked Sensor Data. In Proceedings of 2010 International Symposium on

Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010.



MotivationInternet of Things

• Internet of Things– Refers to the networked interconnection of everyday objects*.

– If everything in our daily life could be just identified situations like lost parcels, theft,running out of stock, etc. would be things of past.

• As simple idea but difficult to achieve– It’s a non-deterministic, event-driven, bottom-up and open network with self-organizing

entities.

– Meaning of an event depends on a context of the event itself.

• Makes it necessary for the Internet of Things to be also a Semantic Web**


Conner, Margery (May 27, 2010). Sensors empower the "Internet of Things". pp. 32–38. ISSN 0012-7515

**http://www.i-o-t.org/post/3questionstoPhilippeGAUTIERbyDavidFayon



MotivationInternet of Things

• Central Nervous System for the Earth (HP Labs initiative)

• A research and development program to build a planetwide sensing

network, using billions of "tiny, cheap, tough and exquisitely sensitive“.

• Sensors detect vibrations, motion, light, temperature, barometric, .

• Possible use-cases*:

– arnng a ou s ruc ura s rans or wea er con ons,– Monitor traffic, weather and road conditions,

– Tracking hospital equipment,

– Sniffing out pesticides and pathogens in food, etc.


* http://www.hpl.hp.com/news/2009/oct-dec/cense.html



MotivationOther examples

• Social networking feeds

– Average user creates 90 pieces of content each month**

– Micro blogging site Twitter is seeing 90 million tweets per day*

• Twitter is planning to add structured annotations to a tweet***

" J ust saw Avatar and i t was amazi ng“"annot at i ons“: [

{' movi e' :{

‘ t i t l e’ : ' Avat ar ' ,' ur l ' : ' ht t p: / / www. r ot t ent omat oes. com/ m/ avat ar / ' ,' ' ' '. . . ,

' text ' : ' Avatar ‘}

}]


Twitter's co-founder Ev Williams on September 14th, 2010, http://techcrunch.com/2010/09/14/twitter-event** Facebook statistics, Retrieved on March 7th, 2011 at http://www.facebook.com/press/info.php?statistics*** Twitter Annotations Overview, http://dev.twitter.com/pages/annotations_overview



And so what?

Motivation




MotivationThe join points between the examples

• Data is delivered in the form of stream

A stream is a sequence of data of undetermined length*.

• Inner data structures are exhibiting dimensional characteristics of thenotion of event

– Temporal dimension (creation data stamp),

– Geospatial dimension (coordinates where the data is created),

– Informational dimension (payload), etc.

,collect, analyze and interpret event streams.


*

http://www.cafeaulait.org/course/week10/02.html



MotivationEvent Processing Market Players (March 4th, 2011)


The CEP Market in 2011. Retrieved from http://tibcoblogs.com/cep/2011/03/04/the-cep-market-in-2011 on March 7th, 2011



MotivationReported Customers of Event Processing Solutions


Retreived fromhttp://www.slideshare.net/swadpasc/semantic-complex-event-processing-at-sem-tech-2010on J uly 30th, 2010



MotivationCurrent Event Processing Approaches

• Automated event processing has been applied so far in many areas:– Medical monitoring, luggage management, personal banking system alerts, fraud

detection, emergency control systems, online-trading systems, road-tolling systems,etc…

• Reasons of using event processing are:– Real-time operational behavior,

– Observation capabilities,

– Immediate information dissemination,

– Active diagnostics,

– Predictive processing,

– Anal tics,

– Different paradigm for solving problems• Instead of designing and crafting an information system to accommodate every possible

domain-specific usage scenario, the system composed out of “intelligent objects” shouldgovern itself towards achieving local and global goals.




MotivationEvent processing on the Web

• Some of the problems regarding Event Processing on the Web:– roper treatment o t e noton o tme an tme epen ent event reatons

• Can we expect synchronous time across the globe?– Context-based event interpretation

• An event interpretation depends on the context in which the event has been produced.

• Context must be conveyed together with the event.

– Events interoperability

• IoT-based solutions may be segregated.

• Heterogeneity between the solutions may hinder the possibility to integrate different events.

• Reconciliation of event heterogeneities is necessary.

– Bridging the gap between event and stream processing

• How can we apply event processing techniques over RDF streams?

• Identifying event dimensions in raw data streams

– Scalable event lifecycle• Stream of events may be enormous (sensor readings, social networking activities, etc).

• Activities such as publishing, searching, detecting, transforming, filtering and consumingusable and significant events must be automatized.




Motivation

Event Processing on the Web must rely on the Semantic.







Technical SolutionOutline

• Basic Concepts– The notion of event, event processing, and event processing networks

• Event Processing on the Semantic Web– re overvew o t e current approac es

– Event-driven Transaction Logic Inference System (ETALIS)




Basic Concepts *

Technical olution


*Opher Etzion and Peter Niblett. Event Processing in Action. Manning Publications Co., 2010



Technical SolutionBasic Concepts – What is an event?

• What is an event?– An event is an thin that ha ens.*

– An event is an occurrence within a particular system or domain; it is something that

has happened, or is contemplated as having happened in that domain. **

• Side effects of the definition:– The system in which event occurred may be any system external to observer.

– An event needs not to correspond to actual occurrences.

• Examples of events:

– Temperature sensor readings, airplane landing, Twitter updates, etc.


*K. Many Chandy, W. Roy Schulte. Event Processing: Designing IT Systems for Agile Companies, McGraw-Hill Osborne Media, 2009

**Opher Etzion and Peter Niblett. Event Processing in Action. Manning Publications Co., 2010



Technical SolutionBasic Concepts – What are event dimensions?

Different facets of an event*


*Ramesh J ain, "EventWeb: Developing a Human-Centered Computing System," Computer, pp. 42-50, February, 2008



Technical SolutionBasic Concepts – Event Type

• An event type is a specification for a set of event objects that have thesame semantic intent and same structure *.





Technical SolutionBasic Concepts – Event Processing

• What is Event Processing?– Event processing consists in processing many events happening across all the layers

of an organization, identifying the most meaningful events within the event cloud,analyzing their impact, and taking subsequent action in real time.*

– Operations that you can perform on events, in particular operations that take a set of

one or more events as input and generate further events from them as output. **


*http://en.wikipedia.org/wiki/Complex_event_processing

**Opher Etzion and Peter Niblett. Event Processing in Action. Manning Publications Co., 2010



Technical SolutionBasic Concepts – Elements in the event processing environment

• Event Producer

• Event Channel– An event channel is a processing element that receives events from one or

more source processing elements, makes routing decisions, and sends theinput events unchanged to one or more target processing elements inaccordance to the decisions*.

• Global State– Usage of stateful data may be needed in event processing:

• Historical event logs used for later processing, retraction, etc.

• Reference data used for enrichment,

• State of external entities (e.g., workflow state),

• Global variables (enabling non-event-based communication inside of the network)

• Context– A context is a named specification of conditions that groups together eventinstances for the purpose of processing them together.





Technical SolutionBasic Concepts – Event Processing Agent (EPA)

• What is Event Processing Agent ? • According to the targeted– An Event Processing Agent (EPA) is a

software module which processesevents.

functionality EPA may be classified

as follows:




Technical SolutionBasic Concepts – Event Processing Network

• What is Event Processing Network?– , ,

processing agents and global states which are connected by a collection of channels*.

– The definition is abstract and platform independent (at the level of PlatformIndependent Model - PIM).


*

Opher Etzion and Peter Niblett. Event Processing in Action. Manning Publications Co., 2010



Technical SolutionBasic Concepts – Detecting Event Patterns

• A Pattern is a function that takes a collection of input event instancesand produces a matching set that consists of zero or more of those

* .

• Event pattern categories and types– as c event patterns

• Logical Operator Patterns (all, any, absence).

• Threshold Patterns (count, value max, value min, value average).

• Relative Patterns (relative max, relative min).• o a a erns aways, some mes .

– Dimensional patterns

• Temporal Patterns (sequence, increasing, decreasing, non increasing, non decreasing,stable).

, , , , ,

relative average).• Spatiotemporal Patterns (moving in constant direction, stationary, moving towards).


.

28

*




Technical SolutionBasic Concepts - Languages

• Survey* of the current solutions reveals the following styles:– Stream-oriented style

• Languages used to describe queries are based on SQL and relational algebra

• Streams of events are broken into the windows (temporal context) upon which queries are performed• Example languages are SPADE, Aleri, CQL, but also C-SPARQL.

– u e-or en e s y e

• Production rules

– IF condition THEN action (forward chaining).

– Rooted in expert systems.

• Active rules

– Event-Condition-Action (ECA) rules.

– Rooted in Active Databases.

– Example is Rulecore.

• Logic programming rules

– Based on logic assertions.

– Rooted in the deductive databases.

– Example is ETALIS.

– Imperative style•


.

29

*




Event Processing on the Semantic Web

Technical Solution




Technical SolutionEvent Processing on the Semantic Web - Related Work

• Event-Condition-Action Rules on the Semantic Web– Xcerpt and XChange (University of Munich)

• Xcerpt provides the possibility to query any kind of XML data while XChange enablespropagation of changes on the Web and event-based communication between Web nodes.

• Event processing is defined in terms of rules.

• XChange provides composite event queries supporting temporal ranges and eventcompositions.

• Recent work is laying down formal foundation for event queries and rules.

– ven - rven ransac on ogc n erence ys ems arsru e

• ETALIS is a logic-based complex event processing engine.

• Relies on the decomposition of composite event specifications into intermediate patternswhich are asserted by declarative rules and executed in the backward-chaining fashion.

– ECA-LP and ECA-RuleML (Technical University of Munich)

• Unifies derivation rules, reaction rules and other rule types into a framework grounded in LogicProgramming.


.

31



Technical SolutionEvent Processing on the Semantic WebRelated Work

• Event-Condition-Action Rules on the Semantic Web (cont’d)– RDF-based Event-Condition-Action Language (RDFTL) (University of London)

• RDFTL is an ECA rule language operating over RDF graphs which provides reactive behaviorover RDF data stored in RDF repositories.

• Uses path-based query sublanguage in order to select targeted part of RDF graph.

• Path-based queries are used in RDFTL ECA to construct resources, insert/delete arcs, etc.

– Modular Active Rules for the Semantic Web (MARS) (Göttingen University)

• Web as an active infrastructure of autonomous systems with the primary focus on reactivityan evouton.

• Defines ECA rule ontology as a joining point for different event, condition and actionlanguages.

• Extends behavior of an OWL node with the ECA-based reactive behavior.

– C-SPARQL (Politecnico di Milano)• Combines data streams and background knowledge with reasoning to develop the Stream

Reasoning vision which intertwines four steps of the LarkC pluggable framework.

• -


, ,streams and aggregated functions.

32



Event Processing on the Semantic Web

Technical Solution – ETALIS *


*Figures and examples are taken from Darko Anicic, Sebastian Rudolph, Paul Fodor, Nenad Stojanovic. Stream Reasoning and Complex

Event Processing in ETALIS. Under review at journal Semantic Web – Interoperability, Usability, Applicability (ISSN: 1570-0844).



Technical SolutionETALIS – Introduction

• ETALIS features

– Effective detection of complex events over streaming data

– Evaluation of background knowledge on-the-fly

–

• ETALIS follows a completely deductive rule-based paradigm– Inclusion of background knowledge is straightforward




Technical SolutionETALIS – Conceptual Architecture




Technical SolutionETALIS – ETALIS Language for Events (ELE)

• A rule-based language to construct complex event patters– Emphasis is on temporal composition operators




Technical SolutionETALIS – ETALIS Language for Events (ELE) – Example

Background knowledge

t r af f i cEvent ( …)t r af f i cEvent ( …)t r af f i cEvent ( …)t r af f i cEvent ( …) bot t l eneckAr ea( a)t r af f i cEvent ( …)

t r af f i cEvent ( …)t r af f i cEvent ( …)t r af f i cEvent ( …)t r af f i cEvent ( …)

bot t l eneckAr ea( b)

…


Event detection rule

…



Technical SolutionETALIS – Internal processing

• Event Driven Backward Chaining(EDBC) rules

– Executable rules (written in Prolog)

• Event-driven rules– A rule is evaluated when an event

matching the rule’s head occurs.

– A firin rule inserts a oal into the memory.

– Goal denotes (partial) happening of an event – it shows the current stateof ro ress towards matchin an

event pattern.

• The process of splitting complex


rues n o e nary rues scalled binarization.

38



Technical SolutionETALIS – Stream Reasoning with EP-SPARQL

• Specifying complex event patterns and consuming streams andbackground knowledge represented as RDF

• Event processing SPARQL (EP-SPARQL)– Extends SPARQL with

• Binary operators SEQ, EQUALS, OPTIONALSEQ and EQUALSOPTIONAL.

– The operators are acting as joins depending on how the constituents are temporally interrelated

• Functions getDURATION(), getSTARTTIME(), getENDTIME()

• ETALIS implements a reasoning procedure to support subclassinference




Technical SolutionETALIS – Stream Reasoning with EP-SPARQL - Example

Background knowledge

?r oad = …


Event detection rule



What am I doing now for my PhD?

Possible extensions




ProblemsEvent Processing on the Web

• Some of the problems regarding Event Processing on the Web:– roper treatment o t e noton o tme an tme epen ent event reatons

• Can we expect synchronous time across the globe?

– Context-based event interpretation

• An event interpretation depends on the context in which the event has been produced.

• Context must be conveyed together with the event.

– Events interoperability

• IoT-based solutions may be segregated.

• Heterogeneity between the solutions may hinder the possibility to integrate different events.• Reconciliation of event heterogeneities is necessary.

– Bridging the gap between event and stream processing

• How can we apply event processing techniques over RDF streams?

• Identifying event dimensions in raw data streams

– Scalable event lifecycle

• Stream of events may be enormous (sensor readings, social networking activities, etc).

• Activities such as publishing, searching, detecting, transforming, filtering and consumingusable and significant events must be automatized.


P d S l ti



Proposed SolutionSemantically Enhanced Event Processing Networks (SEPN)

/ * SEPN Agent f unct i onal i t y decl arati on */

SEPADecl : : = Pr ef i xDecl *

I nTerDecl

Out TerDecl

Fi l terDecl ?

Mat cher Decl ?

Deri ver Decl ?

/ * I nput and out put t ermi nal s decl arati on */

I nTerDecl : : = ' I NPUT TERMI NAL'

( Ter m_I d Event_ Type_I d) +

OutTerDecl : : = ' OUTPUT TERMI NAL'

( Ter m_I d Event_ Type_I d) +

/ * F i l ter f unct i on decl arat i on */

Fi l t er Decl : : = ' FI LTER' I nput _Ter m* Out put _Ter m?

Fi l ter_Expr+

Fi l t er _Expr : : = Wher eCl ause

/ * Matcher f unct i on decl arati on */

Matcher Decl : : = ' MATCHER' I nput_Term* Output _Term?

Mat cher_Expr

Matcher_ Expr : : = Wher eCl ause

/ * Der i ver f uncti on decl ar at i on */

Deri ver Decl : : = ' DERI VER' I nput _Ter m* Out put _Ter m

Gl _Stat e_I d? Deri ver _ExprGl _Stat e_I d : : = ' GLOBAL STATE' I RI _REF

Der i ver _Expr : : = ' DERI VE' Const r uctTri pl es

' FROM' WhereCl ause

I n ut Ter m : : = ' I NPUT' Ter m I d


_ _

Out put _Ter m : : = ' OUTPUT' Ter m_I d

Term_I d : : = I RI _REF

Event _Type_I d : : = I RI _REF

I RI _REF : : = ' <' ( [ <̂>"{}| ^̀ \ ] - [ #x00- #x20] ) * ' >'

43

Srdjan Komazec and Davide Cerri. Enhancing Event Processing Networks with Semantics to Enable Self-

Managed SEE Federations. 3rd International Workshop on Monitoring, Adaptation and Beyond (MONA+)colocated with collocated with ECOWS, Ayia Napa, Cyprus, 2010.

P d S l ti



Proposed SolutionGraph Pattern Detection

• Rete Algorithm– Pattern matching algorithm for implementing production rule systems.

– Desi ned b Dr Charles L. For of Carne ie Mellon Universit 1974 .

– The basis for many popular expert system shells, including CLIPS, J ess, Drools,

BizTalk, Rules Engine and Soar.


Proposed Sol tion



Proposed SolutionGraph Pattern Detection – An Example

@pr ef i x weat her: <ht t p: / / knoesi s . wr i ght . edu/ ssw/ ont / weat her . owl #> .

@pref i x r df : <ht t p: / / www. w3. org/ 1999/ 02/ 22- rdf - synt ax- ns#> .

@pr ef i x sensor : <ht t p: / / knoesi s. wr i ght . edu/ ssw/ ont / sensor - obser vat i on. owl #> .

@pr ef i x t i me: <ht t p: / / www. w3. org/ 2006/ t i me#>.

?x r df : t ype weat her : Wi ndObser vat i on .

?x sensor : r esul t ?y .

.

?y r df : t ype sensor : Measur eDat a .

?y sensor : f l oat Val ue ?f l oat Val ue .

?z r df : t ype t i me: I nst ant .

?z t i me: i nXSDDat eTi me ?i nst ant .

rdf : t ype =weather : Wi ndObservat i on

x

?y ?zrdf: t ype =sensor : MeasureDat a

rdf : type =t i me: I nst ant

sensor : r esul t sensor : sampl i ngTi me

?floatValue ?instant

sensor : f l oat Val ue t i me: i nXSDDat eTi me


Proposed Solution



Proposed SolutionGraph Pattern Detection – Alpha and Beta Memory Structures

Root Node

Join Node

ALPHA MEMORY BETA MEMORY

PREDICATE

rdf:typeweather:Wind

Observation

PREDICATE

sensor:result

AlphaNode

AlphaNode

Beta Node

Join Node

Beta Node

PREDICATE

sensor:samplin

gTime

OBJECT

AlphaNodeJoin Node

Beta Node

Detection Root Node

rdf:typesensor:Measur

eData

PREDICATE

sensor:floatVal

ue

AlphaNode

AlphaNode

Join Node

Beta Node

Join Node

Node

PREDICATErdf:type

OBJECTtime:instant

PREDICATE

time:inXSDTim

AlphaNode

AlphaNode

Beta Node

Join Node


eData

Join Node

Beta No e



Further extensions

• Graph pattern detection extensions– Introduction of RDFS entailment rules

• Rewriting of the graph patterns– Negated conditions

• Testin the absence of items in a workin memor

• Introduction of negation nodes in the form of ‘negation-as-failure’

– Garbage collection (GC)

• Avoiding GC pauses during which the knowledge base (KB) grows

–

• Comprehensive event pattern specification– Support for all event pattern categories and types

– Bridging the gap between raw data streams and event streams

– Optimization which exploits characteristics of RDF





C l i



Conclusions

• Event processing represents a particular paradigm in the field of datastream processing where the streamed data exhibits specific structural

dimensionality - temporal, geo-spatial, causal, etc.

• Event processing systems are composed out of a set elementsexhibiting different behavior (produces, consumers, agents, channels,global states and contexts).

• Semantic Web technologies are becoming a requirement when itcomes to Event processing on the Web.

• Semantic Web technologies tend to provide a fruitful foundation toaddress the challenges of Event processing on the Web.





References



References

• Books– .

Distributed Enterprise Systems. Addison-Wesley, 2002

– Opher Etzion and Peter Niblett. Event Processing in Action. Manning Publications Co., 2010

•– Complex Event Processing: Applications, products, research, and developments in event

processing by David Luckham @ http://www.complexevents.com

– Complex Event Processing (CEP) Blog by TIBCO @ http://tibcoblogs.com/cep

– . .

• Technical Societies– Event Processing Technical Society http://www.ep-ts.com

• Major annual events– ACM International Conference on Distributed Event-Based Systems

–


51

References



References

• Wikipedia links– Complex event processing http://en.wikipedia.org/wiki/Complex_event_processing

– Event-driven architecture http://en.wikipedia.org/wiki/Event-driven_architecture

– Pattern matching http://en.wikipedia.org/wiki/Pattern_matching

– Internet of Things http://en.wikipedia.org/wiki/Internet of Things _ _


Questions?



Questions?


Event Processing on the Web

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