Challenges, Approaches, and Solutions in Stream Reasoning

Challenges, Approaches,
and Solutions in
Stream Reasoning
http://streamreasoning.org
Emanuele Della Valle
DEI - Politecnico di Milano
emanuele.dellavalle@polimi.it
http://emanueledellavalle.org
Emanuele Della Valle - visit http://streamreasoning.org

Agenda
• Motivation
• Concept
• Achievements
• Applications
• Conclusions

Stavanger, 2012-5-9 Emanuele Della Valle - visit http://streamreasoning.org 2

Motivation
It‘s a streaming World! [IEEE-IS2009] 1/3
• Oil operations

• Traffic

• Financial markets

• Social networks

• Generate data streams!


Motivation
• … and want to analyse
data streams in real time
• In a well in progress to drown,
how long time do I have given
its historical behavior?
• Is public transportation
where the people are?
• Can we detect any intra-day
correlation clusters among
stock exchanges?
• Who is driving the discussion
about the top 10 emerging topics ?


Motivation
• e.g., Real Time Rome (mobile network data streams)
Normal day Exceptional day

[source: http://senseable.mit.edu/realtimerome/ ]
afternoon
evening


Motivation
• e.g., Pulse of the Nation (social media streams)

[source: http://www.ccs.neu.edu/home/amislove/twittermood/ ]
12:00

23:00

happier


Motivation
What are data streams anyway?
• Formally:
– Data streams are unbounded sequences of time-
varying data elements

time

• 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


Motivation
The continuous nature of streams
• The nature of streams requires a
paradigmatic change*
– from persistent data
• to be stored and queried on demand
• a.k.a. one time semantics
– to transient data
• to be consumed on the fly by continuous queries
• a.k.a. continuous semantics

*
This paradigmatic change first arose in DB community [Henzinger98]


Motivation – The continuous nature of streams
Continuous Semantics
• Continuous queries registered over streams that,
in most of the cases, are observed trough windows
window

input streams Registered streams of answer
Continuous
Query


Motivation – The continuous nature of streams
Tools exists [Cugola2011]
• Types
– Data Stream Management Systems
– Complex Event Processors
• 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
– IBM InfoSphere Stream
– Microsoft streaminsight
– Oracle CEP


Motivation
New Requirements  New Challenges

Typical Requirements
•Processing Streams •Continuous semantics
•Large datasets • Scalable processing
•Reactivity • Real-time systems
•Fine-grained information • Powerful query
access languages
•Modeling complex •Rich ontology languages
application domains


Motivation
Are DSMS/CEP ready to address them?

Typical Requirements DSMS/CEP
access languages
application domains


Motivation
Is Semantic Web ready to address them?
• 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 static
or at best a low change rate
both in change-volume and change-frequency
– ontology versioning
– belief revision
– time stamps on named graphs
• It sticks to the traditional one-time semantics


Motivation
New Requirements  New Challenges

Typical Requirements Semantic Web
access languages
application domains


Motivation
New Requirements call for Stream Reasoning

Typical Requirements
access languages
application domains

Stream Reasoning


Concept
Stream Reasoning Definition [IEEE-IS2010]
• Making sense
– in real time
– of multiple, heterogeneous, gigantic and inevitably
noisy data streams
– in order to support the decision process of
extremely large numbers of concurrent user

• Note: making sense of streams necessarily requires processing them
against rich background knowledge, an unsolved problem in
database

Concept
Research Challenges
• Relation with DSMSs and CEPs
– Just as RDF relates to data-base systems?
• Data types and query languages for semantic streams
– Just RDF and SPARQL but with continuous semantics?
• Reasoning on Streams
– Theory
– Efficiency
– Scalability
• Dealing with incomplete & noisy data
– Even more than on the current Web of Data
• Distributed and parallel processing
– Streams are parallel in nature, …
• Engineering Stream Reasoning Applications
– Development Environment
– Integration with other technologies
– Benchmarks

Achievements
• RDF Streams
– Notion defined
• C-SPARQL
– Syntax and semantics defined as a SPARQL extension
– Engine designed and implemented
• Experiments with C-SPARQL under simple RDF entailment
regimes
– window based selection of C-SPARQL outperforms the standard
FILTER based selection
– algebraic optimizations of C-SPARQL queries are possible
– Complex event can be detected using a network of C-SPARQL
queries at high throughputs
• Experiment with C-SPARQL under RDFS++ entailment
regimes
– efficient incremental updates of deductive closures investigated
– our approach outperform state-of-the-art when updates comes as
stream
• Streaming Linked Data Framework prototyped

Achievements
Outline
• RDF Streams
– Notion defined
• C-SPARQL
regimes
regimes
stream

Memo: Sensor Network Ontology

•20 Stavanger, 2012-5-9 Emanuele Della Valle - visit http://streamreasoning.org

Memo: Sensor Network Ontology
[ ] streaming part [ ] static part


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

 Timestamps are not required to be unique, they must be non-
decreasing
• E.g.,
(< :s1 ssn:generatedObservation :o1 >, 2010-02-12T13:34:41)
(< :o1 a weather:SnowfallObservation >, 2010-02-12T13:34:41)
(< :s1 om-owl:generatedObservation :o2 >, 2010-02-12T13:36:28)
(< :o2 a weather:WindSpeedObservation >, 2010-02-12T13:36:28)
(< :o2 ssn:result :a1 >, 2010-02-12T13:36:28)
(< :a1 ssn:floatValue "35.4”^^xsd:float >, 2010-02-12T13:36:28)


Achievements
Outline
• RDF Streams
– Notion defined
• C-SPARQL
regimes
regimes
stream

MEMO: SPARQL


Achievements
Where C-SPARQL Extends SPARQL


Achievements
An Example of C-SPARQL Query
Which are the sensors that have observing winds above 50 mph in the
last half an hour? Which is the observed average wind?

REGISTER STREAM AvgWindSpeed AS
CONSTRUCT { ?sens w:avgWindSpeed ?avgWindSpeed }
FROM STREAM <.../streams/ssnmeteostream> [RANGE 1h STEP 1h]
WHERE {
SELECT ?sens (AVG(?v) as ?avgWindSpeed)
WHERE {
?sens om-owl:generatedObservation ?o .
?o a weather:WindSpeedObservation .
?o om-owl:result ?r .
?r om-owl:floatValue ?v . }
GROUP BY ?sens
HAVING (?avgWindSpeed > 5)
}


Achievements
An Example of C-SPARQL Query
Which are the sensors that have observing winds above 50 mph in the
Query registration
RDF Stream added as
last half (for hour? Which is the observed average wind? format
an continuous new ouput
execution)

REGISTER STREAM AvgWindSpeed AS FROM STREAM clause
CONSTRUCT { ?sens w:avgWindSpeed ?avgWindSpeed }
FROM STREAM <.../streams/ssnmeteostream> [RANGE 1h STEP 1h]
WHERE {
SELECT ?sens (AVG(?v) as ?avgWindSpeed)
WINDOW
WHERE {
?sens om-owl:generatedObservation ?o .
?o a weather:WindSpeedObservation .
?o om-owl:result ?r . SPARQ 1.1 features
•Sub-queries
?r om-owl:floatValue ?v . } •aggregates
GROUP BY ?sens
HAVING (?avgWindSpeed > 5)
}


Achievements
Outline
• RDF Streams
– Notion defined
• C-SPARQL
regimes
regimes
stream

Achievements
FROM STREAM Clause - Types of Window
• physical: a given number of triples
• logical: a variable number of triples which occur during a
given time interval (e.g., 1 hour)
– Sliding: they are progressively advanced of
a given STEP (e.g., 5 minutes)

– Tumbling: they are advanced of exactly their time interval


Achievements
Efficiency of Evaluation [IEEE-IS2010]
• window based selection of C-SPARQL outperforms
the standard FILTER based selection


Achievements
Outline
• RDF Streams
– Notion defined
• C-SPARQL
regimes
regimes
stream

Achievements
Algebraic optimizations of C-SPARQL [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
algebraic optimizations of C-SPARQL [EDBT2010]
• Push of filters and projections
125

100

75
m
s

50

25

0
10 100 1000 10000 100000
Window Size
None Static Only Streaming Only Both


Achievements
Outline
• RDF Streams
– Notion defined
• C-SPARQL
regimes
regimes
stream

Achievements
Complex Event Detection as stream compositions
• e.g., continuous detection of blizzards by analyzing
multiple streams of data generated by weather
sensors spread across a continental area

Blizzard: a severe snowstorm with high winds
and low visibility lasting at least three hours


Achievements

Q [1 HOUR]
[TUMBLING]
Count Snow Fall

Q [1 HOUR]
[TUMBLING] Q [3 HOURS]
[STEP 1 HOUR]
Linked Sensor Data AVG Wind Speed Blizzard

Q [1 HOUR]
[TUMBLING]
AVG Temp
Adapter
LEGEND

C-SPARQL
Query


Achievements
snowfall + strong winds + low temp  blizzards
Q [1 HOUR]
[1 HOUR] Q [1 HOUR]
[TUMBLING] Q [TUMBLING] [TUMBLING] Q [1 HOUR]
Count Snow Fall [TUMBLING]
Count Snow Fall Count Snow Fall
Count Snow Fall

[1 HOUR] Q [3 HOURS]
Q [TUMBLING] [STEP 1 HOUR] Q [1 HOUR] Q [3 HOURS] Q [1 HOUR] Q [3 HOURS]
[1 HOUR] [3 HOURS]
[TUMBLING] [STEP 1 HOUR] [TUMBLING] [STEP 1 HOUR] Q [TUMBLING] Q [STEP 1 HOUR]
Linked Sensor Data AVG Wind Speed Most Liked POIs Linked Sensor Data
Linked Sensor Data AVG Wind Speed Most Liked POIs AVG Wind Speed Most Liked POIs
Linked Sensor Data AVG Wind Speed Most Liked POIs

[1 HOUR]
Q [TUMBLING] Q [1 HOUR] Q [1 HOUR]
[1 HOUR]
[TUMBLING] [TUMBLING] Q
AVG Temp [TUMBLING]
AVG Temp AVG Temp
AVG Temp

Live demonstration at http://streamreasoning.org/demos/blizzard-detection


Achievements
High Throughputs [JWS2012a]


Achievements
Outline
• RDF Streams
– Notion defined
• C-SPARQL
regimes
regimes
stream

Achievements
Where’s the Reasoning?
 Example: can we measure the the impact of a tweet?
 Twitter allows two traceable ways of discussing a tweet:
 reply: a user reply to a tweet of another user (it always retweet the
original tweet)
 retweet: a user propagates to his/her followers an interesting
tweet
 For example
reply reply
reply t2 t4 t7

retweet reply reply
t1 t3 t5 t8

retweet t6
50 min ago 40 min ago 30 min ago 20 min ago 10 min ago now

Achievements
Example of C-SPARQL and Reasoning 1/2
What impact have I been creating with my tweets in the last hour?
Let’s count them …
REGISTER STREAM OpinionSpreading COMPUTED EVERY 30s AS
SELECT ?tweet (count(?tweet) AS ?impact
FROM STREAM <http://ex.org> [RANGE 60m STEP 10m]
WHERE { :reply rdfs:subPropertyOf :discuss .
:retweet rdfs:subPropertyOf :discuss .
:t1 sr:discuss ?tweet :discuss a owl:TransitiveProperty .
}

discuss
reply discuss
reply
discuss
reply t2 t4 t7

7!
discuss
retweet discuss
reply discuss
reply
t1 t3 t5 t8

retweet
discuss t6


Achievements
Our approach [ESWC2010]
• The algorithm
1. 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.


Implementation
Comparative Evaluation on Materialization
• base-line: re-computing the materialization from scratch
• state-of-the-art [Ceri1994,Volz2005]
• our approach [ESWC2010]
10000

1000
m
s
.

100

10
0,0% 2,0% 4,0% 6,0% 8,0% 10,0% 12,0% 14,0% 16,0% 18,0% 20,0%
% of the materialization changed when the slides
% of the materialization changed when the window window slides
incremental-volz incremental-stream

Achievements
Comparative Evaluation on Query Answering
• comparison of the average time needed to answer
a C-SPARQL query using
– backward reasoner
– the naive approach of re-computing the materialization
– our approach
20

15

10
m
s
.

5

0
forward reasoning
Backward reasoning naive approach incremental-stream
query 5,82 1,61 1,61
materialization 0 15,91 0,28


Achievements
Outline
• RDF Streams
– Notion defined
• C-SPARQL
regimes
regimes
stream

Achievements
Streaming Linked Data Framework [JWS2012a]
Features
•Accessing raw data
stream from C-
SPARQL
•Publishing streams
and C-SPARQL query
results as Linked Data
•Connecting C-
SPARQL queries in a
network
•Recoding and
replaying portions of
stream
•Supporting fast
prototyping of
applications


Applications
Location Based Social Media Analytics [JWS2012b]


Applications
Location Based Social Media Analytics [JWS2012b]

Sk s
O K e_lh
b e/XG
yo utu.
://
http

Live demonstration at http://streamreasoning.org/demos/bottari


Applications
Blizzard Detection [JWS2012a]


Applications
Blizzard Detection [JWS2012a]

Live demonstration at http://streamreasoning.org/demos/blizzard-detection


Applications
Hurricane Detection [JWS2012a]


Applications
Hurricane Detection [JWS2012a]

Live demonstration at http://streamreasoning.org/demos/hurricane-detection


You can try C-SPARQL out!
 Working prototype available for download in a
“ready to go pack”
http://streamreasoning.org/download
 The Streaming Linked Data Framework will be
soon released too, ask me directly for a pre-
release version.


Conclusions
Research Challenges vs. Achievements
 Relation with DSMSs and CEPs
 Notion of RDF stream :-| alternative solutions can be investigated
 Data types and query languages for semantic streams
 C-SPARQL :-D work in progress in FZI&AIFB [1,2] DERI [3], UPM [4]
 Reasoning on Streams
 Theory :-(
 Efficiency :-) work in progress in ISTI-Innsbruck [5]
 Scalability :-| work in progress in IBM&VUA [6]
 Dealing with incomplete & noisy data
 Even more than on the current Web of Data :-( some initial joint work
with SIEMENS only [IEEE-IS2010]
 Distributed and parallel processing
 Streams are parallel in nature, … :-| work in progress in IBM&VUA [6]
 Engineering Stream Reasoning Applications
 Development Environment :-) work in progress in UPM [7]
 Integration with other technologies :-)
 Benchmarks :-P work in progress in Planet Data [8]


Credits
 Politecnico di Milano’s colleagues
 Prof. Stefano Ceri who had the initial intuition about the value of
introducing data streams to the semantic Web community
 Marco Balduini, Davide Barbieri, Daniele Braga, Stefano Ceri and
Michael Grossniklaus who helped concieving the
C-SPARQL Engine and the Streaming Linked Data Framework
 once again to Davide Barbieri who engineered most of the C-
SPARQL Engine as part of his PhD
 once again to Marco Balduini who engineered most of Streaming
Linked Data Framework as part of his M.Sc. Thesis
 Politecnico di Milano’s Master students that assisted in the
design and development of the prototypes
 Mirko Bratomi, and Marco Regaldo
 Colleagues that in helped in concieving, designing, and
prototyping the applications
 CEFRIEL: Irene Celino, and Danile Dell’Aglio
 SIEMENS: Yi Huang, and Volker Tresp
 Saltlux: Seonho Kim, and Tony Lee


References
My papers
 [IEEE-IS2009] E. Della Valle, S. Ceri, F. van Harmelen, D. Fensel
It's a Streaming World! Reasoning upon Rapidly Changing Information.
IEEE Intelligent Systems 24(6): 83-89 (2009)
 [EDBT2010] D.F. Barbieri, D.Braga, S. Ceri and M. Grossniklaus.
An Execution Environment for C-SPARQL Queries. EDBT 2010
 [WWW2009] D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus:
C-SPARQL: SPARQL for continuous querying. WWW 2009: 1061-1062
 [SIGMODRec2010] D.F. Barbieri, D.Braga, S. Ceri and M. Grossniklaus. :
Querying RDF streams with C-SPARQL. SIGMOD Record 39(1): 20-26 (2010)
 [IEEE-IS2010] D. Barbieri, D. Braga, S. Ceri, E. Della Valle, Y. Huang, V. Tresp,
A.Rettinger, H. Wermser: Deductive and Inductive Stream Reasoning for
Semantic Social Media Analytics IEEE Intelligent Systems, 30 Aug. 2010.
 [JWS2012a] E. Della Valle, M. Balduini: SLD: a Framework for Streaming
Linked Data. JWS. 2012 Under Review
 [JWS2012b] M. Balduini; I.Celino; E. Della Valle; D.Dell'Aglio; Y. Huang; T. Lee;
S. Kim; V. Tresp: BOTTARI: an Augmented Reality Mobile Application to deliver
Personalized and Location-based Recommendations by Continuous Analysis of
Social Media Streams. JWS. 2012. to appear.
 [ESWC2010] D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus.
Incremental Reasoning on Streams and Rich Background Knowledge.
ESWC 2010

Emanuele Della Valle - visit http://streamreasoning.org 56
Stavanger, 2012-5-9

References
Other groups’ papers
[1] Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic: EP-SPARQL: a
unified language for event processing and stream reasoning. WWW 2011: 635-644
[2] Danh Le Phuoc, Minh Dao-Tran, Josiane Xavier Parreira, Manfred Hauswirth: A Native
and Adaptive Approach for Unified Processing of Linked Streams and Linked Data.
International Semantic Web Conference (1) 2011: 370-388
[3] D. Anicic, S. Rudolph, P. Fodor, N. Stojanovic: Real-Time Complex Event Recognition
and Reasoning-a Logic Programming Approach. Applied Artificial Intelligence 26(1-2):
6-57 (2012)
[4] Jean-Paul Calbimonte, Óscar Corcho, Alasdair J. G. Gray: Enabling Ontology-Based
Access to Streaming Data Sources. ISWC (1) 2010: 96-111
[5] S. Komazec and D. Cerri: Towards Efficient Schema-Enhanced Pattern Matching over
RDF Data Streams. First International Workshop on Ordering and Reasoning
(OrdRing2011)
[6] Jesper Hoeksema, Spyros Kotoulas: High-performance Distributed Stream Reasoning
using S4. First International Workshop on Ordering and Reasoning (OrdRing2011)
[7] A.J.G. Gray, R.Garcia-Castro, K.Kyzirakos, M.Karpathiotakis, J.Calbimonte, K.R.Page,
J.Sadler, A.Frazer, I.Galpin, A.A.A. Fernandes, N.W. Paton, O.Corcho, M.Koubarakis,
D.De Roure, K. Martinez, A. Gómez-Pérez: A Semantically Enabled Service
Architecture for Mashups over Streaming and Stored Data. ESWC (2) 2011: 300-314
[8] PlanetData. D1.2 Benchmarking RDF Storage Engines.
http://wiki.planet-data.eu/web/D1.2

Stavanger, 2012-5-9

References
Background papers
 [Henzinger98] Henzinger, M. R. & Raghavan, P. (1998). Computing on
data streams. Systems Research.
 [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)
 [Cugola2011] Alessandro Margara, Gianpaolo Cugola:
Processing flows of information: from data stream to complex event proces
. DEBS 2011: 359-360

Stavanger, 2012-5-9

Thank You! Questions?

Much More to Come!
Keep an eye on
http://www.streamreasoning.org


Challenges, Approaches, and Solutions in Stream Reasoning

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