Stream Reasoning: Where We Got So Far

•For more information visit http://wiki.larkc.eu/UrbanComputing

Stream ReasoningStream ReasoningWhere We Got So FarWhere We Got So Far

http://streamreasoning.org http://streamreasoning.org

Emanuele Della Valle DEI - Politecnico di Milano

[email protected]://emanueledellavalle.org

Joint work with:Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, and Michael Grossniklaus

Emanuele Della Valle - visit http://streamreasoning.org

Agenda

• Motivation

• Background

• Concept

• Running Example

• Achievements

• Retrospective and Conclusions

2NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010


Motivation

It‘s a streaming World! [IEEE-IS2009]


• Sensor networks, …

• traffic engineering, …

• social networking, …

• financial markets, …

• generate streams!


Motivation

Questions People are Asking


• Given this brand of turbine, what is the expected time to failure when the barring starts to vibrate as now detected?

• Is a traffic jam going to happen in this highway? And is then convenient to reallocate travelers based upon the forecast?”

• Who are the opinion makers? i.e., the users who are likely to influence the behavior of other users who follow them

• In the financial context, can we detect any intraday correlation clusters among stock exchange?


Motivation

Problem Statement

• Making sense – in real time – of gigantic and inevitably noisy data streams – in order to support the decision process of extremely

large numbers of concurrent users



Background

What are data streams anyway?

• Formally: – Data streams are unbounded sequences of time-

varying data elements

• Less formally: – an (almost) “continuous” flow of information – with the recent information being more relevant as it

describes the current state of a dynamic system

time



Background

Can the Semantic Web process data stream?

• The Semantic Web, the Web of Data is doing fine– RDF, RDF Schema, SPARQL, OWL, DL– well understood theory, – rapid increase in scalability

• BUT it pretends that the world is staticor at best a low change rateboth in change-volume and change-frequency

– ontology versioning– belief revision– time stamps on named graphs

• It sticks to the traditional one-time semantics



Background

Continuous Semantics

• Processing data streams in the space of one-time semantics is difficult because of the very nature of the underlying data

• Innovative* assumption: continuous semantics! – streams can be consumed on the fly rather than being

stored forever and– queries are registered and continuously produce

answers

* This innovation arose in DB community in ’90s



Background

Stream Processing

• Continuous queries registered over streams that are observed trough windows

NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010

window

input stream stream of answerRegistered Continuous

Query

9


Background

Key Optimization in Stream Processing

• When a continuous query is registered, generate a query execution plan

– New plan merged with existing plans

– Global scheduler for plan execution maximizing experience gathered with previous queries.


Q1 Q2

State4⋈State3

Stream1 Stream2

Stream3

State1 State2⋈

SchedulerScheduler


Background

Data Stream Management Systems (DSMS)• Research Prototypes

– Amazon/Cougar (Cornell) – sensors– Aurora (Brown/MIT) – sensor monitoring, dataflow– Gigascope: AT&T Labs – Network Monitoring– Hancock (AT&T) – Telecom streams– Niagara (OGI/Wisconsin) – Internet DBs & XML– OpenCQ (Georgia) – triggers, view maintenance– Stream (Stanford) – general-purpose DSMS– Stream Mill (UCLA) - power & extensibility– Tapestry (Xerox) – publish/subscribe filtering– Telegraph (Berkeley) – adaptive engine for sensors– Tribeca (Bellcore) – network monitoring

• High-tech startups– Streambase, Coral8, Apama, Truviso

• Major DBMS vendors are all adding stream extensions as well– Oracle http://www.oracle.com/technology/products/dataint/htdocs/streams_fo.html

– DB2 http://www.eweek.com/c/a/Database/IBM-DB2-Turns-25-and-Prepares-for-New-Life/



Concept

Stream Reasoning [IEEE-IS2010,Dagstuhl2010]

• Idea origination– Can continuous semantics be ported to reasoning?– This is an unexplored yet high impact research

area!

• Stream Reasoning– Logical reasoning in real time on gigantic and

inevitably noisy data streams in order to support the decision process of extremely large numbers of concurrent users.

-- S. Ceri, E. Della Valle, F. van Harmelen and H. Stuckenschmidt, 2010

• Note: making sense of streams necessarily requires processing them against rich background knowledge



Concept

Research Challenges

• Relation with data-stream systems– Just as RDF relates to data-base systems?

• Query languages for semantic streams– Just as SPARQL for RDF but with continuous semantics?

• Reasoning on Streams– Formal representations for stream reasoning

• Active Logic? Step Logic? Temporal Logic? None of them?– Notions of soundness and completeness

• How to define these on windows?– Efficient incremental updates of deductive closures? – How to combine streams and background knowledge?

• Dealing with incomplete & noisy data– Even more so than on the current Web of Data

• Distributed and parallel processing– Streams are parallel in nature



Concept

Engineering challenges

• Software architectures

• Integration with existing systems

• Optimization and scalability

• Real-time computations



Concept

Evaluation and Success Criteria


• An early attempt

-- S. Ceri, E. Della Valle, F. van Harmelen, Ralf Möller and H. Stuckenschmidt, 2009

Efficiency Criterion Querying Answering Complex InferenceNr. of streams 10s of streams 10s of streamsSpeed per stream(assertions/second a/t)

1000s a/t 10s a/t

Nr. of registered queries

100s of queries 100s of queries

Response Latency 10s of milliseconds 1000s of milliseconds


Running Example

Real-Time Streams on the Web

• Streams are appearing more and more often on the Web in sites that distribute and present information in real-time streams.

• Checkout http://activitystrea.ms/ for a standard API

• E.g.

NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010LDOW2010 @ WWW 2010, Raleigh, North Carolina, April 27th, 2010


Running Example

Example of Questions Users are Asking

• What are the hottest topics under discussion on Twitter?

• Which topics have my close friends discussed in the last hour?

• Who is discussing about Italian food in northern Italy right now?

• Which movie is my friend likely to watch next?

• Which Tuscany red wine should I recommend to one of my friends?



Running Example

Real Social Media Stream Data: Glue



Running Example

Glue Data Model as an Ontology


URLrdfs:labelskos:subjectowl:sameAs

ObjectResource links

describes

URLfoaf:name

User

sioc:follows

foaf:knowsaccesses

likes

dislikes

data stream

background knowledge

URLrdfs:label


Achievements Stream Reasoner Inputs and Outputs


[…]

Stream Reasoner

[…]

Legenda

stream

Slowly evolving graph

variable binding

registered query orreasoning tasks


Achievements

Explored Continuous Semantics for SeWeb

• We gave up one-time semantics in Semantic Web and explored the benefits provided by continuous semantics when dealing with streams

• We investigated– Architecture of a Simple Stream Reasoner– RDF streams

• the natural extension of the RDF data model to the new continuous scenario and

– Continuous SPARQL (or simply C-SPARQL) • the extension of SPARQL for querying RDF streams.

– Efficient incremental updates of deductive closures

• specifically considering the nature of data streams



Achievements

Architecture of our Simple Stream Reasoner

• Based on the LarKC conceptual frameworkhttp://www.larkc.eu


Select Select AbstractAbstract ReasonReason

Streamed Input Window Content RDF Streams

Answ

ers

Stre

ams

Window

RDF Graphs


Achievements

RDF Stream [WWW2009,EDBT2010]

• RDF Stream Data Type– Ordered sequence of pairs, where each pair is made

of an RDF triple and its timestamp t(< triple >, t)

• E.g.,(<:Giulia :likes :Twilight >, 2010-02-12T13:34:41)

(<:John :accesses :TheLordOfTheRings >, 2010-02-12T13:36:28)

(<:Alice :dislikes :Twilight >, 2010-02-12T13:36:28)

(<:Bob :accesses :Chianti >, 2010-02-12T13:37:18)



Achievements Windows on RDF Streams

• RDF streams are intrinsically infinite

• A window extracts the last triples

• The extraction can be– physical

• a given number of triples

– logical • a variable number of triples which occur during a given

time interval



Achievements Example Window of RDF Stream [SDOW2009]

• A real source of social semantic data streams– Source: the Social Network Glue http://getglue.com – RDF obtained applying GRDDL to XML results of

invocation of http://api.getglue.com/v2/glue/recent Glue REST service

– Check out the current content of the windowhttp://c-sparql.cefriel.it/sdow-demo/RDFstream.html



Achievements C-SPARQL [WWW2009,EDBT2010]

• We specificied of C-SPARQL syntax– Incrementally, from existing specifications

• Including windows, grouping, aggregates, timestamping

• We gave the formal semantics of C-SPARQL – Query registration, handling overloads– Order of evaluation, pattern matching over time, …

• We investigated efficiency of evaluation – Defining a suitable algebra– Applying optimizations – Efficient materialization of inferred data from streams



Achievements An Simple Example of C-SPARQL Query

What have my closest friends been visiting in the last hour?

REGISTER QUERY WhatMyFriendsVisitedInTheLastHour AS

PREFIX sioc: <http://rdfs.org/sioc/ns#>

PREFIX foaf: <http://xmlns.com/foaf/0.1/>

PREFIX glue: <http://c-sparql.cefriel.it/sdow-demo/>

SELECT DISTINCT ?friend ?topic

FROM <http://c-sparql.cefriel.it/sdow-demo/glueusers.rdf>

FROM STREAM <http://c-sparql.cefriel.it/sdow-demo/interactions.trdf>

[ RANGE 60m STEP 5m ]

WHERE { glue:id1 foaf:knows ?friend .

?post sioc:has_creator ?friend .

?post rdf:type sioc:Post .

?post sioc:topic ?topic . }



Achievements An Simple Example of C-SPARQL Query

What have my closest friends been visiting in the last hour?

REGISTER QUERY WhatMyFriendsVisitedInTheLastHour AS

PREFIX sioc: <http://rdfs.org/sioc/ns#>

PREFIX foaf: <http://xmlns.com/foaf/0.1/>

PREFIX glue: <http://c-sparql.cefriel.it/sdow-demo/>

SELECT DISTINCT ?friend ?topic

FROM <http://c-sparql.cefriel.it/sdow-demo/glueusers.rdf>

FROM STREAM <http://c-sparql.cefriel.it/sdow-demo/interactions.trdf>

[ RANGE 60m STEP 5m ]

WHERE { glue:id1 foaf:knows ?friend .

?post sioc:has_creator ?friend .

?post rdf:type sioc:Post .

?post sioc:topic ?topic . }


Triples from a graph

Combined with triples from a

stream

Query registration(for continuous

execution)

FROM STREAM clause

WINDOW


Achievements An Advance Example of C-SPARQL Query

Who are the opinion makers? i.e., the users who are likely to influence the behavior of other users who follow them

REGISTER STREAM OpinionMakers COMPUTED EVERY 5m AS

CONSTRUCT { ?opinionMaker sd:about ?resource }

FROM STREAM <http://streamingsocialdata.org/interactions> [RANGE 30m STEP 5m]

WHERE {

?opinionMaker ?opinion ?resource .

?follower sioc:follows ?opinionMaker.

?follower ?opinion ?resource.

FILTER ( cs:timestamp(?follower) > cs:timestamp(?opinionMaker)

&& ?opinion != sd:accesses )

}

HAVING ( COUNT(DISTINCT ?follower) > 3 )



Achievements C-SPARQL at Work [SDOW2009]


http://c-sparql.cefriel.it/sdow-demo/C-SPARQLquery.html


Achievements Efficiency of Evaluation 1/3 [SDOW2009]

• Evaluation of Window-based Selection



Achievements Efficiency of Evaluation 2/3 [EDBT2010]

• Several transformations can be applied to algebraic representation of C-SPARQL

• some recalling well known results from classical relational optimization

– push of FILTERs and projections

• some being more specific to the domain of streams.– push of aggregates.



Achievements Efficiency of Evaluation 3/3 [EDBT2010]

• Push of filters and projections


0

25

50

75

100

125

10 100 1000 10000 100000

ms

Window Size

None Static Only Streaming Only Both


Achievements Example of C-SPARQL and Reasoning 1/2

Who are the movie opinion makers ?

REGISTER STREAM MovieOpinionMakers COMPUTED EVERY 5m AS

CONSTRUCT { ?opinionMaker sd:about ?resource }

FROM STREAM <http://streamingsocialdata.org/interactions> [RANGE 30m STEP 5m]

WHERE {

?opinionMaker ?opinion ?resource .

?opinionMaker a sd:UserOnlyInterestInMovies .

?follower sioc:follows ?opinionMaker.

?follower ?opinion ?resource.

FILTER ( cs:timestamp(?follower) >

cs:timestamp(?opinionMaker)

&& ?opinion != sd:accesses )

}

HAVING ( COUNT(DISTINCT ?follower) > 3 )



Achievements Example of C-SPARQL and Reasoning 2/2

• If we define sd:UserOnlyInterestInMovies rdfs:subClassOf sd:User;

rdfs:subClassOf [

a owl:Restriction;

owl:onProperty sd:likes;

owl:allValuesFrom yago:Movie;

] .

• if the current window contains the following triples, ( <:Giulia sd:likes :Avatar >, 2010-02-12T13:18:05)

( <:John sd:likes :StarWars >, 2010-02-12T13:36:23)

( <:John sd:likes :WutheringHeights >, 2010-02-12T13:38:07)

( <:Giulia sd:likes :AliceInWonderland >, 2010-02-12T13:42:07)

• Giulia is an instance of the class UserOnlyInterestInMovies (i.e., she liked only movies), while John is not (i.e., he liked a movies and a book).

– NOTE: we are using OWL2-RL + Negation As Failure



Achievements Incremental Reasoning: State-of-the-Art

• Incremental Maintenance of Materialized Views– Stefano Ceri, Jennifer Widom: Deriving Incremental Production Rules

for Deductive Data. Inf. Syst. 19(6): 467-490 (1994)– HA Kuno, EA Rundensteiner: Incremental Maintenance of

Materialized Object-Oriented Views in MultiView: Strategies and Performance Evaluation. TDKE 1998

– Raphael Volz, Steffen Staab, Boris Motik: Incrementally Maintaining Materializations of Ontologies Stored in Logic Databases. J. Data Semantics 2: 1-34 (2005)

• Incremental Rule-based Reasoning– F Fabret, M Regnier, E Simon: An Adaptive Algorithm for Incremental

Evaluation of Production Rules in Databases. VLDB 1993– B. Berster: Extending the RETE Algorithm for Event

Management.TIME’02• Incremental DL Reasoning

– Cuenca-Grau et al : History Matters: Incremental Ontology Reasoning Using Modules. ISWC 2007.

– Parsia et al: Towards incremental reasoning through updates in OWL-DL. - Reasoning on the Web-Workshop at WWW-2006



Achievements

State-of-the-Art Approach [Ceri1994,Volz2005]

1. Overestimation of deletion: Overestimates deletions by computing all direct consequences of a deletion.

2. Rederivation: Prunes those estimated deletions for which alternative derivations (via some other facts in the program) exist.

3. Insertion: Adds the new derivations that are consequences of insertions to extensional predicates.



Achievements

our approach [ESWC2010] 1/2

• Assuption– Insertions and deletions are triples respectively

entering and exiting the window– The window size is known

• Therefore– The time when each triple will expire is known and

determined by the window size• E.g. if the window is 10s long a triple entering at time t will

exit at time t+10s

– Note: all knowledge can be annotated with an expiration time

• i.e., background knowledge is annotated with +



Achievements

our approach [ESWC2010] 2/2

• The algorithm1. computes the entailments derived by the inserts,

2. annotates each entailed triple with a expiration time, and

3. eliminates from the current state all copies of derived triples except the one with the highest timestamp.

• NOTE: if you like to learn more come to my presentation on 1.6.2010 in Mobility & Sensor Network I session

– http://www.slideshare.net/emanueledellavalle/incremental-reasoning-on-streams-andrich-background-knowledge



Achievements

Comparative Evaluation [ESWC2010] • Hypothesis

– Background knowledge do not change and it is materialized– Changes only take place in the window

• An experiment comparing the time required to compute a new materialization using

– Re-computing from scratch (i.e.,1250 ms in our setting)– State of the art incremental approach [Volz, 2005]– Our approach

• Results at increasing % of the triples updated

• .


10

100

1000

10000

0,0% 2,0% 4,0% 6,0% 8,0% 10,0% 12,0% 14,0% 16,0% 18,0% 20,0%

ms.

% of the materialization changed when the window slides

incremental-volz incremental-stream


Retrospective and Conclusions

Wrap Up• RDF Streams

– Notion defined – Examples of RDF streams can be easily created

• e.g., http://c-sparql.cefriel.it/sdow-demo/RDFstream.html • C-SPARQL

– Syntax and semantics defined as a SPARQL extension– Engine designed– Engine implemented based on the decision to keep stream

management and query evaluation separated• Experiments with C-SPARQL under simple RDF entailment regimes

– window based selection of C-SPARQL outperforms the standard FILTER based selection

– having formally defined C-SPARQL semantics algebraic optimizations are possible

• Experiment with C-SPARQL under OWL-RL entailment regimes– efficient incremental updates of deductive closures investigated – our approach outperform state-of-the-art when updates comes as

stream




Achievements vs. Research Challenges• Relation with data-stream systems

– Notion of RDF stream :-|• Query languages for semantic streams

– C-SPARQL :-D• Reasoning on Streams

– Formal representations for stream reasoning• :-P

– Notions of soundness and completeness• :-P

– Efficient incremental updates of deductive closures• ESWC 2010 paper :-) ... but much more work is needed!

– How to combine streams and background knowledge• ESWC 2010 paper :-| ... but a lot needs to be studied ...

• Dealing with incomplete & noisy data– :-P

• Distributed and parallel processing– :-P




Achievements vs. Engineering challenges• Software architectures

– C-SPARQL engine :-| only very preliminary results• Integration with existing systems

– :-) C-SPARQL prototype based on esper• Optimization

– :-) EDBT 2010 and ESWC 2010 paper, but still a lot of investigation is possible

• Scalability– :-D for Query Answering in C-SPARQL under simple RDF

entailment regime– :-| for Query Answering in C-SPARQL under OWL-RL entailment

regime– :-P for Complex Reasoning

• Real-time computations– :-P




A Key Problem to Investigate [Dagstuhl2010]

• Reasoning on Data streams has a huge potential impact

• Current Reasoning methods are not suited to work on data with a high change frequency.

• A promising approach?

-- H. Stuckenschmidt, S. Ceri, E. Della Valle and F. van Harmelen, 2010


Raw Stream Processing

RDF Streams

Logic Programs

DL

Complexity

Reasoning

Querying

Rewriting

Abstraction

Selection

Interpretation

Change FrequencyPTIME

2NEXPTIME

104 Hz

1 Hz

Dynamics and Scale vs. Complexity

Evolving Knowlege


References (selection)• Vision

[IEEE-IS2009] Emanuele Della Valle, Stefano Ceri, Frank van Harmelen, Dieter Fensel It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009) bibtex

• Continuous SPARQL (C-SPARQL) [EDBT2010] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri and

Michael Grossniklaus. An Execution Environment for C-SPARQL Queries. EDBT 2010

[WWW2009] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: C-SPARQL: SPARQL for continuous querying. WWW 2009: 1061-1062 bibtex

[SDOW2009] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri and Emanuele Della Valle and Michael Grossniklaus, Continuous Queries and Real-time Analysis of Social Semantic Data with C-SPARQL, in SDoW 2009 Colocated with ISWC 2009. bibtex

• Stream Reasoning[ESWC2010] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri,

Emanuele Della Valle, Michael Grossniklaus. Incremental Reasoning on Streams and Rich Background Knowledge. In. 7th Extended Semantic Web Conference (ESWC 2010)

[Dagstuhl2010] Heiner Stuckenschmidt, Stefano Ceri, Emanuele Della Valle and Frank van Harmelen. Towards Expressive Stream Reasoning. Proceedings of the Dagstuhl Seminar on Semantic Aspects of Sensor Networks, 2010.


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Thank You! Questions?

46

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Education

Stream Reasoning: Where We Got So Far