Emanuele Della Valle - visit http://streamreasoning.org
Stream Reasoning:Stream Reasoning: State of the Art and Beyond State of the Art and Beyond
http://streamreasoning.org http://streamreasoning.org
Emanuele Della Valle DEI - Politecnico di Milano
[email protected]://emanueledellavalle.org
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Agenda
• Motivation
• Concept
• Running Example
• Achievements
• Challenges vs. Achievements
• Beyond Stream Reasoning
• Conclusions
Oxford, 2012-9-25 2
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Motivation
It‘s a streaming World! [IEEE-IS2009] 1/3
• Oil operations
• Traffic
• Financial markets
• Social networks
• Generate data streams!
Oxford, 2012-9-25 3
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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 givenits historical behavior?
• Is public transportation where the people are?
• Can we detect any intra-daycorrelation clusters among stock exchanges?
• Who is driving the discussion about the top 10 emerging topics ?
Oxford, 2012-9-25 4
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Motivation
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
Oxford, 2012-9-25 5
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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 6
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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 streams of answerRegistered Continuous
Query
Oxford, 2012-9-25 7
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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 8
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Motivation
New Requirements New Challenges
Typical Requirements
•Processing Streams
•Large datasets
•Reactivity
•Fine-grained information access
•Modeling complex application domains
•Continuous semantics
• Scalable processing
• Real-time systems
• Powerful query languages
•Rich ontology languages
Oxford, 2012-9-25 9
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Motivation
Are DSMS/CEP ready to address them?
Typical Requirements
•Processing Streams
•Large datasets
•Reactivity
•Fine-grained information access
•Modeling complex application domains
DSMS/CEP
•Continuous semantics
• Scalable processing
• Real-time systems
• Powerful query languages
•Rich ontology languages
Oxford, 2012-9-25 10
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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 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
Oxford, 2012-9-25 11
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Motivation
New Requirements New Challenges
Typical Requirements
•Processing Streams
•Large datasets
•Reactivity
•Fine-grained information access
•Modeling complex application domains
Semantic Web
•Continuous semantics
• Scalable processing
• Real-time systems
• Powerful query languages
•Rich ontology languages
Oxford, 2012-9-25 12
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Motivation
New Requirements call for Stream Reasoning
Typical Requirements
•Processing Streams
•Large datasets
•Reactivity
•Fine-grained information access
•Modeling complex application domains
•Continuous semantics
• Scalable processing
• Real-time systems
• Powerful query languages
•Rich ontology languages
Stream Reasoning
Oxford, 2012-9-25 13
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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 14
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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 15
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Running Example
Social Media Analytics in BOTTARI [JWS2012]
http://streamreasoning.org/demos/bottari
16Oxford, 2012-9-25 Winner of S
emantic Web Challenge 2012
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Running Example
Data Model Used in BOTTARI
17Oxford, 2012-9-25
geo:SpatialThinggeo:lat
(xsd:float)geo:long(xsd:float)
sr:NamedPlace
sioc:creator_ofsioc:creator_of
sr:talksAboutsr:talksAbout
sr:replysr:reply
sr:retweetsr:retweet
sioc:has_creatorsioc:has_creator
sr:talksAboutNeutrallysr:talksAboutNeutrally
sr:talksAboutPositivelysr:talksAboutPositively
sr:followersr:followersr:followingsr:following
twd:posttwd:post
twd:discusstwd:discuss
sr:talksAboutNegativelysr:talksAboutNegatively
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Running Example
Streaming vs. Background Information
18Oxford, 2012-9-25
data stream
User related background knowledge
Point of Interest related background knowledge
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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 19
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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 20
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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 21
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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 22
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MEMO: SPARQLMEMO: SPARQL
Oxford, 2012-9-25 23
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AchievementsAchievements
Where C-SPARQL Extends SPARQLWhere C-SPARQL Extends SPARQL
Oxford, 2012-9-25 24
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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 ASCONSTRUCT { ?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 25
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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 ASCONSTRUCT { ?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 26
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
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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 27
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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 28
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Achievements Efficiency of Evaluation [IEEE-IS2010]
• window based selection of C-SPARQL outperforms the standard FILTER based selection
Oxford, 2012-9-25 29
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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 30
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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 31
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Achievements algebraic optimizations of C-SPARQL [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
Oxford, 2012-9-25 32
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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 33
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Achievements High Throughputs [JWS2012]
Oxford, 2012-9-25 34
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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 35
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AchievementsAchievements
Where’s the Reasoning? 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
t1 t3 t5 t8retweet reply reply
t2 t4 t7
t6
reply reply
retweet
reply
now10 min ago20 min ago30 min ago40 min ago50 min ago
Oxford, 2012-9-25 36
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AchievementsAchievements
Example of C-SPARQL and Reasoning 1/2Example 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 {
:t1 sr:discuss ?tweet
}
:reply rdfs:subPropertyOf :discuss .:retweet rdfs:subPropertyOf :discuss .
t1 t3 t5 t8retweet reply reply
t2 t4 t7
t6
reply reply
retweet
reply
discuss discuss discuss
discuss discuss
discuss
discuss
:discuss a owl:TransitiveProperty .
7!Oxford, 2012-9-25 37
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AchievementsAchievements
Our approach [ESWC2010]
• 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.
Oxford, 2012-9-25 38
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Implementation
Comparative Evaluation on Materialization
• base-line: re-computing the materialization from scratch• state-of-the-art [Ceri1994,Volz2005]• our approach [ESWC2010]
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-streamOxford, 2012-9-25 39
% of the materialization changed when the window slides
Emanuele Della Valle - visit http://streamreasoning.org
forward reasoning naive approach incremental-stream
query 5,82 1,61 1,61materialization 0 15,91 0,28
0
5
10
15
20
ms.
AchievementsAchievements
Comparative Evaluation on Query Answering
• comparison of the average time needed to answera C-SPARQL query using
– backward reasoner– the naive approach of re-computing the materialization– our approach
Backward reasoning
Oxford, 2012-9-25 40
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AchievementsAchievements
Location Based Social Media Analytics [JWS2012]
41Oxford, 2012-9-25
Live demonstration at http://streamreasoning.org/demos/bottari
http://youtu.be/XGOKe_lhSks
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AchievementsAchievements
Sensor Networks - Weather phenomenon detection
42Oxford, 2012-9-25Live demonstration at http://streamreasoning.org/demos/
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Research Challenges vs. AchievementsResearch 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 43
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Beyond Stream Reasoning [SWJ2012]
Positioning Stream Reasoning
44Oxford, 2012-9-25
Scalable reasoningScalable reasoning
Stream reasoningStream reasoning
Types of reasoning
No reasoning Data-driven Query-driven Combinations
No ordering
Natural
Types of orders
DSMS CEP
DSMS CEP
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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
input streams streams of answerRegistered Continuous Reasoning
Tasks
Oxford, 2012-9-25 45
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Beyond Stream Reasoning [SWJ2012]
Order-aware Data Management
46Oxford, 2012-9-25
Scalable reasoningScalable reasoning
Stream reasoningStream reasoning
Types of reasoning
No reasoning Data-driven Query-driven Combinations
No ordering
Natural
Cheap to enforce
Expensive to enforce
Combinations
Types of orders
Ord
er-a
war
e d
ata
man
agem
ent
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Beyond Stream Reasoning [SWJ2012]
Order-aware Data Management
• When treating massive data order matters!
• 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]
47Oxford, 2012-9-25
Data as a sortable entity
streaming algorithms
where we can enforce orderings easily and logically
e.g., order by•sortable literals•popularity•uncertainty•trust
Most relevant answers first
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Beyond Stream Reasoning [SWJ2012]
A first attempt: SPARQL-Rank [ISWC2012]
An extended SPARQL algebra where ORDER is a first class citizen
48Oxford, 2012-9-25
FROM Materialize then sort
TOSplit and interleave
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Beyond Stream Reasoning [SWJ2012]
A first attempt: SPARQL-Rank [ISWC2012]
Performance increase up to 2 orders of magnitude for K<100
49Oxford, 2012-9-25
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Beyond Stream Reasoning [SWJ2012]
A new space of investigation
50Oxford, 2012-9-25
Ord
er-a
war
e d
ata
man
agem
ent
Scalable reasoningScalable reasoning
Stream reasoningStream reasoning
Top-k Reasoning
Top-k Reasoning
Order-aware reasoning
Order-aware reasoning
Types of reasoning
No reasoning Data-driven Query-driven Combinations
No ordering
Natural
Cheap to enforce
Expensive to enforce
Combinations
Types of orders
Emanuele Della Valle - visit http://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.org
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
51Oxford, 2012-9-25
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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
52Oxford, 2012-9-25
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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/
53Oxford, 2012-9-25
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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
54Oxford, 2012-9-25
Emanuele Della Valle - visit http://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.org
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.
55Oxford, 2012-9-25
Emanuele Della Valle - visit http://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.org
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)
56Oxford, 2012-9-25
Emanuele Della Valle - visit http://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.org
Thank You! Questions?
Much More to Come!Keep an eye on
http://www.streamreasoning.org
Oxford, 2012-9-25 57
Emanuele Della Valle - visit http://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.org
Call for Papers
The International Journal on Semantic Web and Information Systems
IJSWIS seeks contributions to a
Special Issue on Stream reasoningEditors:
Emanuele Della Valle, Stefano Ceri, Frank van Harmelen
Important Dates:
Abstract Submission: 30th September 2012
Submission Deadline: 31th October 2012
58Oxford, 2012-9-25