EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning

EP-SPARQL: A Uniﬁed Language for Event
Processing and Stream Reasoning

Darko Anicic1 Paul Fodor2 Sebastian Rudolph3
Nenad Stojanovic1

1 FZI Research Center for Information Technology, Karlsruhe, Germany
2 State University of New York at Stony Brook, USA
3 Karlsruhe Institute of Technology, Karlsruhe, Germany

WWWW 2011, Hyderabad, India

Introduction
EP-SPARQL Motivation
Experimental Results Related Work
Conclusion

Introduction

Real-time data appear increasingly today everywhere.
How to effectively process this data?
Financial sector
to find dealing opportunities across available assets
to detect fraud and enable real-time surveillance
to monitor operational risks
Traffic control systems
to observe traffic-update events and (re)plan the traffic
to route the traffic and optimise paths
Sensor networks
to process sensor data and detect real-time observations
e.g., weather observations like tsunamis, hurricanes etc.

D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011

Introduction
Conclusion

Supporting Real-Time Information Processing

An event is something that occurs, happens or changes
the current state of affairs.
To detect more complex dynamic matters, (simpler) events
are combined into complex events.

Event Processing
deals with the task of processing events with the goal of
identifying meaningful situations, using event operators as well
as temporal and semantic relationships.


Introduction
Conclusion

Supporting Real-Time Knowledge Processing

Current EP systems provide on-the-ﬂy analysis of data
streams, but fall short of combining events with higher-level
background knowledge.
Background knowledge describes the context or domain in
which events are interpreted.
Reasoning techniques are necessary for handling
background knowledge as events occur!

Stream Reasoning
deals with the task of conjunctively reasoning over streaming
data and background knowledge.


Introduction
Conclusion

Toward Real-Time Semantic Web

Rapidly changing data
represented as events

handles

Event Processing
(EP)


Introduction
Conclusion


Rapidly changing data Static or slowly evolving
represented as events background knowledge

handles

handles
Event Processing Semantic Web technologies
(EP) including


Introduction
Conclusion



handles

handles
(EP) including

EP SPARQL EP-SPARQL


Introduction
Conclusion



handles

handles
(EP) including

EP SPARQL EP-SPARQL
• Temporal relatedness
• Semantic relatedness
• Stream reasoning


Introduction
Conclusion

Related Work

Streaming Databases
SASE [1]
ZStream [10]
CEDR [6]
TelegraphCQ [5]
Temporal (and Spatial) RDF
Introducing time into RDF [8]
SPARQL-ST [11], Temporal SPARQL [12], stSPARQL [9],
and T-SPARQL [7]
Stream Reasoning
C-SPARQL [3]
Streaming Knowledge Bases [13]
Streaming SPARQL [4]
Incremental reasoning [2]

Introduction
Syntax
EP-SPARQL
Semantics
Experimental Results
Execution Model
Conclusion

Syntax of the Language

EP-SPARQL
– extends SPARQL to enable event-based processing that
takes into account temporal situatedness of triple assertions.
– syntactical and semantic downward-compatibility to plain
SPARQL.

1 Operators: FILTER , AND, UNION , OPTIONAL , SEQ , EQUALS ,
OPTIONALSEQ , and EQUALSOPTIONAL ;
2 getDURATION() yields a literal of type xsd:duration
giving the time interval associated to the graph pattern;
3 getSTARTTIME() and getENDTIME() retrieve the time
stamps of type xsd:dateTime of the start and end of the
interval;

Introduction
Syntax
EP-SPARQL
Semantics
Execution Model
Conclusion

Informal Semantics

RDF stream – a set of triple occurrences s, p, o , tα , tω where
s, p, o is an RDF triple and tα , tω are the start and end of the
interval.
FILTER – restricts variable bindings to those µ, tα , tω for
which the ﬁlter expression evaluates to true;
AND – joins µ, tα , tω and µ , tα , tω . The joined tuple has
timestamp tα = min(tα , tα ), tω = max(tω , tω );
UNION – forms the disjunction of µ, tα , tω and µ , tα , tω ;
OPTIONAL – matches µ, tα , tω optionally with µ , tα , tω
when the ﬁlter expression evaluates to true;


Introduction
Syntax
EP-SPARQL
Semantics
Execution Model
Conclusion

Informal Semantics (cont’d)

SEQ – joins µ, tα , tω and µ , tα , tω only if µ , tα , tω
occurs strictly after µ, tα , tω ;
EQUALS – joins µ, tα , tω and µ , tα , tω if they occur
simultaneously;
OPTIONALSEQ and EQUALSOPTIONAL are
temporal-sensitive variants of OPTIONAL;
CONSTRUCT – generates the stream enriched by triples
from possibly iterative CONSTRUCT rules. SELECT-queries
get evaluated not against the pure input stream but against
the enriched generated stream.


Introduction
Syntax
EP-SPARQL
Semantics
Execution Model
Conclusion

Example I

Continuously search for companies having a larger than 20%
stock price increase in less than 15 days without having
acquired another company during that period.

SELECT ?company WHERE
{ ?company hasStockprice ?price1 }
SEQ { { ?company hasAcquired ?othercompany }
OPTIONALSEQ
{ ?company hasStockPrice ?price2 } }
FILTER ( ?price2 > ?price1 * 1.2 &&
!BOUND(?othercompany) &&
getDURATION() < "P15D"ˆˆxsd:duration)


Introduction
Syntax
EP-SPARQL
Semantics
Execution Model
Conclusion

Example II

Detect slow trafﬁc and its cause which could be used to
automatically modify a speed limit on a certain roads.
SELECT ?road ?speed WHERE
{ ?road tr: slowTrafficDue ?observ }
SEQ {{ ?road tr: slowTrafficDue ?observ }
{ ?observ rdfs:subClassOf tr:SlowTraffic }
{ ?observ wt:speed ?speed }}
FILTER ( getDURATION() < "P1H"ˆˆxsd:duration)

Observ_1 Observ_2
rdf:type tr:GhostDriver ; rdf:type tr:IceConditions ;
wt:speed "50"ˆˆxsd:int . wt:speed "40"ˆˆxsd:int .

tr:GhostDriver rdfs:subClassOf tr:SlowTraffic.
tr:IceConditions rdfs:subClassOf tr:BadWeather.


Introduction
Syntax
EP-SPARQL
Semantics
Execution Model
Conclusion

Sequence Operator
{ ?comp hasStockPrice ?pr1 }
SEQ { ?comp hasStockPrice ?pr2 }
SEQ { ?comp hasStockPrice ?pr3 }
s, p, o , ti , tj represented as triple(s, p, o, Ti , Tj ), and τ represents s, p, o.

triple(τi , T1 , T4 ) ← triple(τ1 , T1 , T2 ) SEQ triple(τ2 , T3 , T4 ).
triple(τ, T1 , T6 ) ← triple(τi , T1 , T4 ) SEQ triple(τ3 , T5 , T6 ).

Rule transformation – Incremental computation (Prolog syntax)
triple(τ1 , T1 , T2 ) :-
assert goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τi , , )) .

triple(τ2 , T3 , T4 ) : −
goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τ, , )),
T2 < T3 ,
retract goal(triple(τ2 , , ), triple(τ1 , T1 , T2 ), triple(τi , , )) ,
triple(τi , T1 , T4 ).


Introduction
Syntax
EP-SPARQL
Semantics
Execution Model
Conclusion

Other Operators
...
FILTER ( ?price2 < ?price1 * 0.7 &&
?price3 > ?price1 * 1.05)

FILTER – Rule transformation
condition(Price1, Price2, Price3) : −
P1 is (Price1 ∗ 0.7), P1 >Price2,
P2 is (Price1 ∗ 0.5), Price3>P2 .

EQUALS – Rule transformation
equals(TI1 , TI2 ) : −
TI1 = [TI1 S, TI1 E], validTimeInterval(TI1 ),
TI2 = [TI2 S, TI2 E], validTimeInterval(TI2 ),
TI1 S = TI2 S, TI1 E = TI2 E.

validTimeInterval(TI) ←
TI = [TI S, TI E], TI S@ < TI E.


Introduction
Test 1: Event Processing
EP-SPARQL
Test 2: Stream Reasoning
Test 3: Example applications
Conclusion

Event Processing

Test pattern: monitoring
Throughput vs. Window size Throughput vs. Window size (Sum over Sequence)

Esper 3.3.0 EALIS-YAP

Throughput (1000 x Events/Sec)
ETALIS-YAP Esper

10000
the average stock price 30

of a company X with
Throughput (Event/Sec)

25
9000

8000 CONSTRUCT queries 20

15
7000

6000
Intel Core Quad CPU 10

5000 Q9400 2,66GHz, 8GB of 5

0
4000 RAM, Ubuntu 9.10
1 3 10
100 500 1000 50000

Count window size
Time window size (Sec)
ETALIS on SWI Prolog
5.6.64 and YAP Prolog Figure: Aggregation over count
5.1.3 vs. Esper 3.3.0 sliding window


Introduction
EP-SPARQL
Conclusion

Stream Reasoning

!"#$"%$&'()*+,$-,#$.+/,"0(01$2+*/3

%&'()*+,'-

$!!!

Test pattern: infer over

.+/,"0(01$2+*/3$(0$5,
#"!!
streaming triples
whether the subject of a #!!!

triple is an instance of "!!

the class of concern,
!
"!!! #!!!! #"!!! $!!!!
or any of its 40,080 !456+'$"%$&'()*+,
subclasses.
Figure: Delay caused by stream
reasoning


Introduction
EP-SPARQL
Conclusion

Example application I

No. of Locations vs. Consumed Memory No. of Locations vs. Consumed Time

1 Visitor 10 Visitors 1 Visitor 10 Visitors

2000
Goods Delivery System 1500
1295

Consumed time in ms
in the city of Milan;
Consumed Memory in kB

1503 1076
1500
1000 889
RDF knowledge base to 1039

1000 810
represent locations and 500
500
trafﬁc links;
204
86 119 165 31 94 109 130
35 3
0
0
The system “listens” to
5 10 15 20 5 10 15 20

Number of locations Number of locations
trafﬁc-update events and
reschedule paths. Figure: Delay caused by
processing


Introduction
EP-SPARQL
Conclusion

Example application II

Pressure difference
Threshold
0.08

A tsunami detection 0.06

system; 0.04

Real buoy sensor data

Pressure
0.02

continuously processed; 0

GeoNames to provide -0.02

geographical places -0.04
Time

within a certain radius
from the sensor location. Figure: Tsunami detection
histogram


Introduction
Conclusion
EP-SPARQL
Example
Semantics
Conclusion

Conclusion

1 Addressing dynamics and notiﬁcation on the Web has
become an important area of research;
2 The challenge is to get advantage of real-time data, and
recognise important situations in a timely fashion;
3 EP-SPARQL – a new language for Event Processing and
Stream Reasoning;
4 Future work: complete implementation, more expressive
formalisms, and adaptive optimizations.


Introduction
Conclusion
EP-SPARQL
Example
Semantics
Conclusion

Thank you! Questions...

ETALIS is open source:
http://code.google.com/p/etalis

On-line demo:
http://etalis.fzi.de

Contact:
Darko.Anicic@fzi.de


Introduction
Conclusion
EP-SPARQL
Example
Semantics
Conclusion

Jagrati Agrawal, Yanlei Diao, Daniel Gyllstrom, and Neil
Immerman.
Efﬁcient pattern matching over event streams.
In Proceedings of the 28th ACM SIGMOD Conference,
pages 147–160, 2008.
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri,
Emanuele Della Valle, and Michael Grossniklaus.
Incremental reasoning on streams and rich background
knowledge.
In Proceedings of the 7th Extended Semantic Web
Conference (ESWC’10), pages 1–15, 2010.
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri,
and Michael Grossniklaus.
An execution environment for C-SPARQL queries.


Introduction
Conclusion
EP-SPARQL
Example
Semantics
Conclusion

In Proceedings of the 13th International Conference on
Extending Database Technology (EDBT’10), pages
441–452, 2010.
Andre Bolles, Marco Grawunder, and Jonas Jacobi.
Streaming SPARQL - extending SPARQL to process data
streams.
In Proceedings of the 5th European Semantic Web
Sirish Chandrasekaran, Owen Cooper, Amol Deshpande,
Michael J. Franklin, Joseph M. Hellerstein, Wei Hong,
Sailesh Krishnamurthy, Samuel Madden, Vijayshankar
Raman, Frederick Reiss, and Mehul A. Shah.
Telegraphcq: Continuous dataﬂow processing for an
uncertain world.


Introduction
Conclusion
EP-SPARQL
Example
Semantics
Conclusion

In Proceedings of the 1st Biennial Conference on
Innovative Data Systems Research (CIDR’03), 2003.
Mitch Cherniack, Hari Balakrishnan, Magdalena
Balazinska, Donald Carney, Ugur Cetintemel, Ying Xing,
¸
and Stanley B. Zdonik.
Scalable distributed stream processing.
In Proceedings of the 1st Biennial Conference on
Innovative Data Systems Research (CIDR’03), 2003.
Fabio Grandi.
T-SPARQL: a TSQL2-like temporal query language for
RDF.
In International Workshop on on Querying Graph
Structured Data, pages 21–30, 2010.
Claudio Gutierrez, Carlos A. Hurtado, and Alejandro A.
Vaisman.

Introduction
Conclusion
EP-SPARQL
Example
Semantics
Conclusion

Introducing time into rdf.
The IEEE Transactions on Knowledge and Data
Engineering, 19(2):207–218, 2007.
Manolis Koubarakis and Kostis Kyzirakos.
Modeling and querying metadata in the semantic sensor
web: The model stRDF and the query language stSPARQL.

In Proceedings of the 7th Extended Semantic Web
Yuan Mei and Samuel Madden.
Zstream: a cost-based query processor for adaptively
detecting composite events.
In Proceedings of the 29th ACM SIGMOD Conference,
pages 193–206, 2009.
Matthew Perry, Amit P. Sheth, and Prateek Jain.

Introduction
Conclusion
EP-SPARQL
Example
Semantics
Conclusion

SPARQLST: Extending SPARQL to support spatiotemporal
queries.
In Technical Report. KNOESIS-TR-2009-01, 2008.
Jonas Tappolet and Abraham Bernstein.
Applied temporal RDF: Efﬁcient temporal querying of RDF
data with SPARQL.
In Proceedings of the 6th European Semantic Web
Onkar Walavalkar, Anupam Joshi, Tim Finin, and Yelena
Yesha.
Streaming knowledge bases.
In International Workshop on Scalable Semantic Web
Knowledge Base Systems, 2008.


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