The talk about "Stream Reasoning" for INQUEST -- INnovative QUErying of STreams 2012 -- (http://games.cs.ox.ac.uk/inquest12/) organized in Oxford, United Kingdom, September 25-27 2012. The talks presents a comprehensive view on "Stream Reasoning" -- reasoning on rapidly flowing information. It illustrates the challenges, presents the achievements of the database group of Politecnico di Milano on the topic, reviews the challenges pointing to results and ongoing work in the Semantic Web community and proposes how to go beyond the current Stream Reasoning concept. It particular, it points out that "orders matters" when processing massive data and it proposes to investigate streaming algorithms for automated reasoning that can be applied not only to data streams that are "naturally" ordered (by recency) but to any sortable data source.

1. Stream Reasoning:
State of the Art and Beyond
http://streamreasoning.org
Emanuele Della Valle
DEI - Politecnico di Milano
emanuele.dellavalle@polimi.it
http://emanueledellavalle.org
Emanuele Della Valle - visit http://streamreasoning.org

2. Agenda
• Motivation
• Concept
• Running Example
• Achievements
• Challenges vs. Achievements
• Beyond Stream Reasoning
• Conclusions
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 2

3. Motivation
It‘s a streaming World! [IEEE-IS2009] 1/3
• Oil operations
• Traffic
• Financial markets
• Social networks
• Generate data streams!
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 3

4. Motivation
It‘s a streaming World! [IEEE-IS2009] 2/4
• … 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 ?
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 4

5. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 5

6. 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]
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 6

7. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 7

8. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 8

9. 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
application domains languages
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 9

10. Motivation
Are DSMS/CEP ready to address them?
Typical Requirements DSMS/CEP
• Processing Streams • Continuous semantics
• Large datasets • Scalable processing
• Reactivity • Real-time systems
• Fine-grained information • Powerful query
access languages
• Modeling complex • Rich ontology
application domains languages
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 10

11. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 11

12. Motivation
New Requirements  New Challenges
Typical Requirements Semantic Web
• Processing Streams • Continuous semantics
• Large datasets • Scalable processing
• Reactivity • Real-time systems
• Fine-grained information • Powerful query
access languages
• Modeling complex • Rich ontology
application domains languages
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 12

13. Motivation
New Requirements call for Stream Reasoning
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
application domains languages
Stream Reasoning
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 13

14. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 14

15. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 15

16. Running Example
Social Media Analytics in BOTTARI [JWS2012]
http://streamreasoning.org/demos/bottari
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 16

17. Running Example
Data Model Used in BOTTARI
sr:following sr:follower
sioc:UserAccount sr:TwitterUser
sioc:id(xsd:string) sr:screenName(xsd:string)
twd:post
sr:retweet
sioc:creator_of sioc:has_creator
twd:discuss
sr:reply
sr:Tweet
sioc:Post
sr:messageID(xsd:string)
sioc:content(xsd:string)
sr:messageTimeStamp(xsd:string) sr:talksAboutPositively
sr:talksAbout sr:talksAboutNeutrally
sr:talksAboutNegatively
geo:SpatialThing sr:NamedPlace
geo:lat
(xsd:float)geo:long(xsd
:float)
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 17

18. Running Example
Streaming vs. Background Information
User related background knowledge
data stream
Point of Interest related background knowledge
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 18

19. 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
– 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 19

20. Achievements
Outline
• 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
– 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 20

21. 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.,
(<:Alice :posts :post1 >, 2010-02-12T13:34:41)
(<:post1 :talksAboutPositively :LaScala>, 2010-02-12T13:34:41)
(<:Bob :posts :post2 >, 2010-02-12T13:36:28)
(<:post2 :talksAboutNegatively :Duomo>, 2010-02-12T13:36:28)
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 21

22. Achievements
Outline
• 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
– 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 22

23. MEMO: SPARQL
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 23

24. Achievements
Where C-SPARQL Extends SPARQL
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 24

25. 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 )
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 25

26. Achievements
An Example of C-SPARQL Query
Who are the registration makers? i.e., the users who are likely
Query
opinion
RDF Stream added as
to influence the behavior of other users who ouput format
(for continuous execution) new follow them
REGISTER STREAM OpinionMakers COMPUTED EVERY 5m AS
CONSTRUCT { ?opinionMaker sd:about ?resource }
FROM STREAM
<http://streamingsocialdata.org/interactions>clause
FROM STREAM
[RANGE 30m STEP 5m]
WHERE { WINDOW
?opinionMaker ?opinion ?resource . Builtin to
?follower sioc:follows ?opinionMaker.access
timestamps
?follower ?opinion ?resource.
FILTER ( cs:timestamp(?follower) >
cs:timestamp(?opinionMaker) Aggregates as
in SPARQL 1.1
&& ?opinion != sd:accesses )
}
HAVING ( COUNT(DISTINCT ?follower) > 3 )
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 26

27. Achievements
Outline
• 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
– 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 27

28. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 28

29. Achievements
Efficiency of Evaluation [IEEE-IS2010]
• window based selection of C-SPARQL outperforms
the standard FILTER based selection
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 29

30. Achievements
Outline
• 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
– 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 30

31. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 31

32. Achievements
algebraic optimizations of C-SPARQL [EDBT2010]
• Push of filters and projections
125
100
75
ms
50
25
0
10 100 1000 10000 100000
Window Size
None Static Only Streaming Only Both
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 32

33. Achievements
Outline
• 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
– 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 33

34. Achievements
High Throughputs [JWS2012]
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 34

35. Achievements
Outline
• 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 35

36. 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
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 36

37. 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
discuss
retweet discuss
reply discuss
reply
t1 t3 t5 t8
retweet
discuss t6
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 37

38. 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.
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 38

39. Implementation
Comparative Evaluation on Materialization
• base-line: re-computing the materialization from scratch
• state-of-the-art [Ceri1994,Volz2005]
• our approach [ESWC2010]
10000
1000
ms.
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 theslides
% of the materialization changed when the window window slides
incremental-volz incremental-stream
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 39

40. 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
ms.
10
5
0
forward reasoning
Backward reasoning naive approach incremental-stream
query 5,82 1,61 1,61
materialization 0 15,91 0,28
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 40

41. Achievements
Location Based Social Media Analytics [JWS2012]
Live demonstration at http://streamreasoning.org/demos/bottari
41
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org

42. Achievements
Sensor Networks - Weather phenomenon detection
Live
Oxford, 2012-9-25 demonstration at http://streamreasoning.org/demos/
Emanuele Della Valle - visit http://streamreasoning.org 42

43. 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 :-) work in progress in Potsdam&DERI [9,10]
 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
 Demonstrative applications in Social Media and Sensor Networks :-)
 Development Environment :-) work in progress in UPM [7]
 Benchmarks :-P work in progress in CWI&UPM [8]
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 43

44. Beyond Stream Reasoning [SWJ2012]
Positioning Stream Reasoning
Types of
orders
Natural DSMS
CEP
Stream reasoning
No ordering
Scalable reasoning
Types of
reasoning
No reasoning Data-driven Query-driven Combinations
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 44

45. Beyond Stream Reasoning [SWJ2012]
Architecture of Stream Reasoner
• Continuous reasoning tasks registered over
streams that, in most of the cases, are observed
trough windows window
Registered
input streams streams of answer
Continuous
Reasoning
Tasks
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 45

46. Beyond Stream Reasoning [SWJ2012]
Order-aware Data Management
Types of
orders
Combinations
data management
Order-aware
Expensive to enforce
Cheap to enforce
Natural
Stream reasoning
No ordering
Scalable reasoning
Types of
reasoning
No reasoning Data-driven Query-driven Combinations
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 46

47. Beyond Stream Reasoning [SWJ2012]
Order-aware Data Management
• When treating massive data order matters!
Data as a where we can e.g., order by
sortable entity enforce orderings • sortable literals
easily and logically • popularity
• uncertainty
• trust
Most relevant
streaming answers first
algorithms
• If N is the size of the input, a problem is considered to be
“well- solved” if a streaming algorithm exists which requires at most
O(poly(log(N)) space and time [Henzinger98, Babcock2002]
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 47

48. Beyond Stream Reasoning [SWJ2012]
A first attempt: SPARQL-Rank [ISWC2012]
An extended SPARQL algebra where ORDER is a
first class citizen
FROM TO
Materialize then sort Split and interleave
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 48

49. Beyond Stream Reasoning [SWJ2012]
A first attempt: SPARQL-Rank [ISWC2012]
Performance increase up to 2 orders of magnitude for K<100
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 49

50. Beyond Stream Reasoning [SWJ2012]
A new space of investigation
Types of
orders
Combinations Order-aware
data management
reasoning
Order-aware
Expensive to enforce
Top-k
Cheap to enforce Reasoning
Natural
Stream reasoning
No ordering
Scalable reasoning
Types of
reasoning
No reasoning Data-driven Query-driven Combinations
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 50

51. Conclusions
• The Semantic Web community positively
answered to the call at investigating stream
reasoning
• A number of work in progress are rapidly
developing this field, but we are only at the very
beginning
• It may be the right time to move from naturally
ordered data to other types of ordering by
deepening the study of streaming algorithms for
automatic reasoning
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 51

52. 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
– Sara Magliacane and Alessandro Bozzon who started the
exploration of Order-aware reasoning working on SPARQL-RANK
• People I directly worked with on the topic
– CEFRIEL: Irene Celino, and Danile Dell’Aglio
– Saltlux: Seonho Kim, and Tony Lee
– SIEMENS: Yi Huang, and Volker Tresp
– STI-Innsbruck: prof. Dieter Fensel and Srdjan Komazec
– UO: prof. Ian Horrocks and Markus Krötzsch
– VUA: prof. Frank van Harmelen and Stefan Schlobach
• The broader research community that showed interest in
stream reasoning
52
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org

53. Downloads
• C-SPARQL Engine (no reasoning support)
– A ready to go pack for eclipse
• http://streamreasoning.org/download
– Source code available on request
• SPARQL-Rank Engine (ARQ-Rank)
– Source code and experimental data
• http://sparqlrank.search-computing.org/
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 53

54. 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.
• [JWS2012] 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. IN PRESS.
• [ESWC2010] D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus. Incremental
Reasoning on Streams and Rich Background Knowledge.
ESWC 2010
• [SWJ2012] E. Della Valle, S.Schlobach, M. Krötzsch, A. Bozzon, S. Ceri, I. Horrocks. Order Matters!
Harnessing a World of Orderings for Reasoning over Massive Data. Accepted with minor revision to
SWJ
• [ISWC2012] S. Magliacane, A. Bozzon, E. Della Valle. Efficient Execution of Top-k SPARQL
Queries. ISWC 2012. IN PRESS
54
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org

55. 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] Ying Zhang, Minh-Duc Pham, Oscar Corcho and Jean Paul Calbimonte. SRBench: A
Streaming RDF/SPARQL Benchmark ISWC 2012: IN PRESS
[9] Gebser, M., Sabuncu, O., & Schaub, T. (2011). An incremental answer set programming
based system for finite model computation. AI Commun., 24(2), 195–212.
[10] Gebser, M., Grote, T., Kaminski, R., Obermeier, P., Sabuncu, O., & Schaub, T. (2012). Stream
Reasoning with Answer Set Programming: Preliminary Report. In KR’12, pages 613–617. AAAI
Press.
55
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org

56. References
Background papers
• [Babcock2002] Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, Jennifer
Widom: Models and Issues in Data Stream Systems. PODS 2002: 1-16
• [Ceri1994] Stefano Ceri, Jennifer Widom: Deriving Incremental Production Rules for
Deductive Data. Inf. Syst. 19(6): 467-490 (1994)
• [Cugola2011] Alessandro Margara, Gianpaolo Cugola: Processing flows of information:
from data stream to complex event processing. DEBS 2011: 359-360
• [Henzinger98] Henzinger, M. R. & Raghavan, P. (1998). Computing on data streams.
Systems Research.
• [Volz2005] Raphael Volz, Steffen Staab, Boris Motik: Incrementally Maintaining
Materializations of Ontologies Stored in Logic Databases. J. Data Semantics 2: 1-34
(2005)
56
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org

57. Thank You! Questions?
Much More to Come!
Keep an eye on
http://www.streamreasoning.org
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 57

58. Call for Papers
The International Journal on
Semantic Web and Information Systems
IJSWIS
seeks contributions to a
Special Issue on Stream reasoning
Editors:
Emanuele Della Valle, Stefano Ceri, Frank van Harmelen
Important Dates:
Abstract Submission: 30th September 2012
Submission Deadline: 31th October 2012
Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 58