stream reasoning: where we got so far. oxford 2010.1.18

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

Stream ReasoningStream ReasoningWhere We Got So FarWhere We Got So Far

Oxford - 2010.1.18Oxford - 2010.1.18

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

• Running Example

• Background

• Concept

• Achievements

• Retrospective and Conclusions

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Motivation

It‘s a streaming World! [IEEE-IS2009]

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• Sensor networks, …

• traffic engineering, …

• social networking, …

• financial markets, …

• generate streams!


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.

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Running Example

Examples of Questions Users are Asking

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

• Which book is my friend likely to read next?

• What impact have I been creating with my tweets in the last day?

• …

• <query> … <time dimension> ?

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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 user

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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

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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

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Background

Stream Processing

• Continuous queries registered over streams that are observed trough windows

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window

input stream stream of answerRegistered Continuous

Query

9


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/

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Background

Can the Semantic Web process data stream?

• The Semantic Web, the Web of Data is doing fine– RDF, RDF Schema, SPARQL, OWL, RIF– 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

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Concept

Stream Reasoning [IEEE-IS2010]

• 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

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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– Notions of soundness and completeness– Efficiency– Scalability

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

• Distributed and parallel processing– Streams are parallel in nature

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Achievements

Explored Continuous Semantics for SeWeb

• We investigated– Architecture of a 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

– Effective inductive stream reasoning (joint work with Siemens - Munich)

• See paper in IEEE IS special issue on Social Media Analytics

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Achievements

Architecture (IEEE-IS2010)

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

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Legenddata stream C-SPARQL query

RDF stream SPARQL with Probability

RDF graph

SelectorDSMS .

AbstracterDSMS

DeductiveReasonerWindow

AbstracterLong-Term

Matrix

AbstracterHype

Matrix

InductiveReasoner

InductiveReasoner

C

CC

C

P

P

P Soci

al M

edia

Ana

lytic

s


Achievements

RDF Stream [WWW2009,EDBT2010,IJSC2010]

• 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 :likes :TheLordOfTheRings >, 2010-02-12T13:36:28)

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

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Achievements C-SPARQL [WWW2009,EDBT2010,IJSC2010]

• 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

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Achievements An 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 )

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Achievements An 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 )

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Query registration(for continuous

execution)

FROM STREAM clause

WINDOW

RDF Stream added as new ouput format

Builtin to access

timestamps

Aggregates as in SPARQL

1.1


Achievements Efficiency of Evaluation 1/3 [IEEE-IS2010]

• Evaluation of Window-based Selection

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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.

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Achievements Efficiency of Evaluation 3/3 [EDBT2010]

• Push of filters and projections

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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

What impact have I been creating with my tweets in the last hour? Is it positive or negative? Let’s count them …REGISTER QUERY CountPositiveAndNegativeReactions AS

PREFIX : <http://ex.org/twitterImpactMining#>

SELECT ?t count(?pos) count(?neg)

FROM STREAM <http://ex.org/discussions.trdf>

[RANGE 30m STEP 30s]

WHERE {

?t a :MonitoredTweet .

{ ?pos :discuss ?t ;

:ProduceReaction [ a :PositiveReaction ] .

} UNION {

?neg :discuss ?t ;

:ProduceReaction [ a :NegativeReaction ] .

}

} GROUP BY ?t

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:discuss a owl:TransitiveProperty .:reply rdfs:subPropertyOf :discuss .

:retweet rdfs:subPropertyOf :discuss .


Achievements Example of C-SPARQL and Reasoning 2/2

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t1 t1-1 t1-2 t1-3retweet reply retweet

discuss

discuss

discuss discuss discuss

discuss

Monitored Positive Negative


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.

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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 +

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Achievements

our approach [ESWC2010] 2/2

• The algorithm1. deletes all triples (asserted or inferred) that have just

expired

2. computes the entailments derived by the inserts,

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

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

• learn more– http://www.slideshare.net/emanueledellavalle/incremental-

reasoning-on-streams-andrich-background-knowledge

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Achievements

Comparative Evaluation 1/2 [ESWC2010] • Hypothesis

– Background knowledge do not change and it is fully 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 materialization changed when the window slides

• .

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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


forward reasoning naive approach incremental-stream

query 5,82 1,61 1,61materialization 0 15,91 0,28

0

5

10

15

20

ms.

Achievements

Comparative Evaluation 2/2

• Comparison of the average time needed to answer a C-SPARQL query using

– a forward reasoner, – the naive approach of re-computing the materialization– our approach

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Retrospective and Conclusions

Wrap Up• RDF Streams

– Notion defined• 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

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Retrospective and Conclusions

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

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References• 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)

• 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

[IJSC2010] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: C-SPARQL: a Continuous Query Language for RDF Data Streams. Int. J. Semantic Computing 4(1): 3-25 (2010)

[IEEE-IS2010] Davide Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Yi Huang, Volker Tresp, Achim Rettinger, Hendrik Wermser, "Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics," IEEE Intelligent Systems, 30 Aug. 2010.

• 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)

• Background work[Ceri1994] Stefano Ceri, Jennifer Widom: Deriving Incremental Production Rules for Deductive

Data. Inf. Syst. 19(6): 467-490 (1994)[Volz2005] Raphael Volz, Steffen Staab, Boris Motik: Incrementally Maintaining

Materializations of Ontologies Stored in Logic Databases. J. Data Semantics 2: 1-34 (2005)

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For more information visit http://www.larkc.eu/

Thank You! Questions?

33

Much More to Come!Keep an eye on

http://www.streamreasoning.org

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stream reasoning: where we got so far. oxford 2010.1.18

Education

web streams

datastream systems

semantic streams

sense of streams

querying rdf streams

nature oxford

parallel processing

stream extensions