•For more information visit http://wiki.larkc.eu/UrbanComputing
Stream ReasoningStream ReasoningWhere We Got So FarWhere We Got So Far
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
• Background
• Concept
• Running Example
• Achievements
• Retrospective and Conclusions
2NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Motivation
It‘s a streaming World! [IEEE-IS2009]
3NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
• Sensor networks, …
• traffic engineering, …
• social networking, …
• financial markets, …
• generate streams!
Emanuele Della Valle - visit http://streamreasoning.org
Motivation
Questions People are Asking
4NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
• Given this brand of turbine, what is the expected time to failure when the barring starts to vibrate as now detected?
• Is a traffic jam going to happen in this highway? And is then convenient to reallocate travelers based upon the forecast?”
• Who are the opinion makers? i.e., the users who are likely to influence the behavior of other users who follow them
• In the financial context, can we detect any intraday correlation clusters among stock exchange?
Emanuele Della Valle - visit http://streamreasoning.org
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 users
5NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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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
6NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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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, DL– 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
7NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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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
8NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Background
Stream Processing
• Continuous queries registered over streams that are observed trough windows
NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
window
input stream stream of answerRegistered Continuous
Query
9
Emanuele Della Valle - visit http://streamreasoning.org
Background
Key Optimization in Stream Processing
• When a continuous query is registered, generate a query execution plan
– New plan merged with existing plans
– Global scheduler for plan execution maximizing experience gathered with previous queries.
10NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Q1 Q2
State4⋈State3
Stream1 Stream2
Stream3
State1 State2⋈
SchedulerScheduler
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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/
11NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Concept
Stream Reasoning [IEEE-IS2010,Dagstuhl2010]
• 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
12NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
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
• Active Logic? Step Logic? Temporal Logic? None of them?– Notions of soundness and completeness
• How to define these on windows?– Efficient incremental updates of deductive closures? – How to combine streams and background knowledge?
• Dealing with incomplete & noisy data– Even more so than on the current Web of Data
• Distributed and parallel processing– Streams are parallel in nature
13NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Concept
Engineering challenges
• Software architectures
• Integration with existing systems
• Optimization and scalability
• Real-time computations
14NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Concept
Evaluation and Success Criteria
15NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
• An early attempt
-- S. Ceri, E. Della Valle, F. van Harmelen, Ralf Möller and H. Stuckenschmidt, 2009
Efficiency Criterion Querying Answering Complex InferenceNr. of streams 10s of streams 10s of streamsSpeed per stream(assertions/second a/t)
1000s a/t 10s a/t
Nr. of registered queries
100s of queries 100s of queries
Response Latency 10s of milliseconds 1000s of milliseconds
Emanuele Della Valle - visit http://streamreasoning.org
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.
NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010LDOW2010 @ WWW 2010, Raleigh, North Carolina, April 27th, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Running Example
Example of Questions Users are Asking
• What are the hottest topics under discussion on Twitter?
• Which topics have my close friends discussed in the last hour?
• Who is discussing about Italian food in northern Italy right now?
• Which movie is my friend likely to watch next?
• Which Tuscany red wine should I recommend to one of my friends?
17NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Running Example
Real Social Media Stream Data: Glue
18NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Running Example
Glue Data Model as an Ontology
19NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
URLrdfs:labelskos:subjectowl:sameAs
ObjectResource links
describes
URLfoaf:name
User
sioc:follows
foaf:knowsaccesses
likes
dislikes
data stream
background knowledge
URLrdfs:label
Emanuele Della Valle - visit http://streamreasoning.org
Achievements Stream Reasoner Inputs and Outputs
20NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
[…]
Stream Reasoner
[…]
Legenda
stream
Slowly evolving graph
variable binding
registered query orreasoning tasks
Emanuele Della Valle - visit http://streamreasoning.org
Achievements
Explored Continuous Semantics for SeWeb
• We gave up one-time semantics in Semantic Web and explored the benefits provided by continuous semantics when dealing with streams
• We investigated– Architecture of a Simple 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
21NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Achievements
Architecture of our Simple Stream Reasoner
• Based on the LarKC conceptual frameworkhttp://www.larkc.eu
22NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Select Select AbstractAbstract ReasonReason
Streamed Input Window Content RDF Streams
Answ
ers
Stre
ams
Window
RDF Graphs
Emanuele Della Valle - visit http://streamreasoning.org
Achievements
RDF Stream [WWW2009,EDBT2010]
• 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 :accesses :TheLordOfTheRings >, 2010-02-12T13:36:28)
(<:Alice :dislikes :Twilight >, 2010-02-12T13:36:28)
(<:Bob :accesses :Chianti >, 2010-02-12T13:37:18)
23NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Achievements Windows on RDF Streams
• RDF streams are intrinsically infinite
• A window extracts the last triples
• The extraction can be– physical
• a given number of triples
– logical • a variable number of triples which occur during a given
time interval
24NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Achievements Example Window of RDF Stream [SDOW2009]
• A real source of social semantic data streams– Source: the Social Network Glue http://getglue.com – RDF obtained applying GRDDL to XML results of
invocation of http://api.getglue.com/v2/glue/recent Glue REST service
– Check out the current content of the windowhttp://c-sparql.cefriel.it/sdow-demo/RDFstream.html
25NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Achievements C-SPARQL [WWW2009,EDBT2010]
• 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
26NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Achievements An Simple Example of C-SPARQL Query
What have my closest friends been visiting in the last hour?
REGISTER QUERY WhatMyFriendsVisitedInTheLastHour AS
PREFIX sioc: <http://rdfs.org/sioc/ns#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX glue: <http://c-sparql.cefriel.it/sdow-demo/>
SELECT DISTINCT ?friend ?topic
FROM <http://c-sparql.cefriel.it/sdow-demo/glueusers.rdf>
FROM STREAM <http://c-sparql.cefriel.it/sdow-demo/interactions.trdf>
[ RANGE 60m STEP 5m ]
WHERE { glue:id1 foaf:knows ?friend .
?post sioc:has_creator ?friend .
?post rdf:type sioc:Post .
?post sioc:topic ?topic . }
27NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Achievements An Simple Example of C-SPARQL Query
What have my closest friends been visiting in the last hour?
REGISTER QUERY WhatMyFriendsVisitedInTheLastHour AS
PREFIX sioc: <http://rdfs.org/sioc/ns#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX glue: <http://c-sparql.cefriel.it/sdow-demo/>
SELECT DISTINCT ?friend ?topic
FROM <http://c-sparql.cefriel.it/sdow-demo/glueusers.rdf>
FROM STREAM <http://c-sparql.cefriel.it/sdow-demo/interactions.trdf>
[ RANGE 60m STEP 5m ]
WHERE { glue:id1 foaf:knows ?friend .
?post sioc:has_creator ?friend .
?post rdf:type sioc:Post .
?post sioc:topic ?topic . }
28NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Triples from a graph
Combined with triples from a
stream
Query registration(for continuous
execution)
FROM STREAM clause
WINDOW
Emanuele Della Valle - visit http://streamreasoning.org
Achievements An Advance 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 )
29NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Achievements C-SPARQL at Work [SDOW2009]
30NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
http://c-sparql.cefriel.it/sdow-demo/C-SPARQLquery.html
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Achievements Efficiency of Evaluation 1/3 [SDOW2009]
• Evaluation of Window-based Selection
31NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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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.
32NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Achievements Efficiency of Evaluation 3/3 [EDBT2010]
• Push of filters and projections
33NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
0
25
50
75
100
125
10 100 1000 10000 100000
ms
Window Size
None Static Only Streaming Only Both
Emanuele Della Valle - visit http://streamreasoning.org
Achievements Example of C-SPARQL and Reasoning 1/2
Who are the movie opinion makers ?
REGISTER STREAM MovieOpinionMakers COMPUTED EVERY 5m AS
CONSTRUCT { ?opinionMaker sd:about ?resource }
FROM STREAM <http://streamingsocialdata.org/interactions> [RANGE 30m STEP 5m]
WHERE {
?opinionMaker ?opinion ?resource .
?opinionMaker a sd:UserOnlyInterestInMovies .
?follower sioc:follows ?opinionMaker.
?follower ?opinion ?resource.
FILTER ( cs:timestamp(?follower) >
cs:timestamp(?opinionMaker)
&& ?opinion != sd:accesses )
}
HAVING ( COUNT(DISTINCT ?follower) > 3 )
34NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Achievements Example of C-SPARQL and Reasoning 2/2
• If we define sd:UserOnlyInterestInMovies rdfs:subClassOf sd:User;
rdfs:subClassOf [
a owl:Restriction;
owl:onProperty sd:likes;
owl:allValuesFrom yago:Movie;
] .
• if the current window contains the following triples, ( <:Giulia sd:likes :Avatar >, 2010-02-12T13:18:05)
( <:John sd:likes :StarWars >, 2010-02-12T13:36:23)
( <:John sd:likes :WutheringHeights >, 2010-02-12T13:38:07)
( <:Giulia sd:likes :AliceInWonderland >, 2010-02-12T13:42:07)
• Giulia is an instance of the class UserOnlyInterestInMovies (i.e., she liked only movies), while John is not (i.e., he liked a movies and a book).
– NOTE: we are using OWL2-RL + Negation As Failure
35NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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Achievements Incremental Reasoning: State-of-the-Art
• Incremental Maintenance of Materialized Views– Stefano Ceri, Jennifer Widom: Deriving Incremental Production Rules
for Deductive Data. Inf. Syst. 19(6): 467-490 (1994)– HA Kuno, EA Rundensteiner: Incremental Maintenance of
Materialized Object-Oriented Views in MultiView: Strategies and Performance Evaluation. TDKE 1998
– Raphael Volz, Steffen Staab, Boris Motik: Incrementally Maintaining Materializations of Ontologies Stored in Logic Databases. J. Data Semantics 2: 1-34 (2005)
• Incremental Rule-based Reasoning– F Fabret, M Regnier, E Simon: An Adaptive Algorithm for Incremental
Evaluation of Production Rules in Databases. VLDB 1993– B. Berster: Extending the RETE Algorithm for Event
Management.TIME’02• Incremental DL Reasoning
– Cuenca-Grau et al : History Matters: Incremental Ontology Reasoning Using Modules. ISWC 2007.
– Parsia et al: Towards incremental reasoning through updates in OWL-DL. - Reasoning on the Web-Workshop at WWW-2006
36NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
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.
37NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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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 +
38NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Achievements
our approach [ESWC2010] 2/2
• The algorithm1. computes the entailments derived by the inserts,
2. annotates each entailed triple with a expiration time, and
3. eliminates from the current state all copies of derived triples except the one with the highest timestamp.
• NOTE: if you like to learn more come to my presentation on 1.6.2010 in Mobility & Sensor Network I session
– http://www.slideshare.net/emanueledellavalle/incremental-reasoning-on-streams-andrich-background-knowledge
39NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Achievements
Comparative Evaluation [ESWC2010] • Hypothesis
– Background knowledge do not change and it is 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 triples updated
• .
40NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
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
Emanuele Della Valle - visit http://streamreasoning.org
Retrospective and Conclusions
Wrap Up• RDF Streams
– Notion defined – Examples of RDF streams can be easily created
• e.g., http://c-sparql.cefriel.it/sdow-demo/RDFstream.html • 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
41NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
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
42NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Retrospective and Conclusions
Achievements vs. Engineering challenges• Software architectures
– C-SPARQL engine :-| only very preliminary results• Integration with existing systems
– :-) C-SPARQL prototype based on esper• Optimization
– :-) EDBT 2010 and ESWC 2010 paper, but still a lot of investigation is possible
• Scalability– :-D for Query Answering in C-SPARQL under simple RDF
entailment regime– :-| for Query Answering in C-SPARQL under OWL-RL entailment
regime– :-P for Complex Reasoning
• Real-time computations– :-P
43NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Emanuele Della Valle - visit http://streamreasoning.org
Retrospective and Conclusions
A Key Problem to Investigate [Dagstuhl2010]
• Reasoning on Data streams has a huge potential impact
• Current Reasoning methods are not suited to work on data with a high change frequency.
• A promising approach?
-- H. Stuckenschmidt, S. Ceri, E. Della Valle and F. van Harmelen, 2010
44NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
Raw Stream Processing
RDF Streams
Logic Programs
DL
Complexity
Reasoning
Querying
Rewriting
Abstraction
Selection
Interpretation
Change FrequencyPTIME
2NEXPTIME
104 Hz
1 Hz
Dynamics and Scale vs. Complexity
Evolving Knowlege
Emanuele Della Valle - visit http://streamreasoning.org
References (selection)• 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) bibtex
• 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 bibtex
[SDOW2009] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri and Emanuele Della Valle and Michael Grossniklaus, Continuous Queries and Real-time Analysis of Social Semantic Data with C-SPARQL, in SDoW 2009 Colocated with ISWC 2009. bibtex
• 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)
[Dagstuhl2010] Heiner Stuckenschmidt, Stefano Ceri, Emanuele Della Valle and Frank van Harmelen. Towards Expressive Stream Reasoning. Proceedings of the Dagstuhl Seminar on Semantic Aspects of Sensor Networks, 2010.
45NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
For more information visit http://www.larkc.eu/
Thank You! Questions?
46
Much More to Come!Keep an eye on
http://www.streamreasoning.org
46NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010