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Reasoning on rapidly chancing information requires: a) semantic models for representing both data streams and continuous querying/reasoning tasks, and b) reasoning algorithms optimised for continuous reactive query-answering. This talk presents applications cases from which Stream Reasoning requirements were elicited, it briefly covers the findings of 5 year of research, it presents an optimised algorithm for Incremental Reasoning on RDF Streams (IMaRS), and offers an outlook on future research opportunities.
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Stream Reasoning http://streamreasoning.org/
It's a Streaming World! Reasoning upon Rapidly Changing InformationEmanuele Della Valleemanuele.dellavalle@polimi.it
Emanuele Della Valle - http://streamreasoning.org/
Agenda
Stream Reasoning in a nutshell
A sample of Stream Reasoning investigation: IMaRS
Conclusions
3
Emanuele Della Valle - http://streamreasoning.org/
Agenda
Stream Reasoning in a nutshell
It's a streaming world
Requirements
State-of-the-art solutions
Stream Reasoning research questions
Example of findings
A sample of Stream Reasoning investigation: IMaRS
Conclusions
4
Emanuele Della Valle - http://streamreasoning.org/
It's a streaming World! 1/2
Off-shore oil operations
Smart Cities
Global Contact Center
Social networks
Generate data streams!
5
Emanuele Della Valle - http://streamreasoning.org/
It's a streaming World! 2/2
What is the expected time to failure when thatturbine's barring starts to vibrate as detected in the last 10 minutes?
Is public transportationwhere the people are?
Who are the best available agents to route all these unexpected contacts about the tariff plan launched yesterday?
Who is driving the discussion about the top 10 emerging topics ?
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)
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Emanuele Della Valle - http://streamreasoning.org/
Requirements 1/8
A system able to answer those queries must be able to
handle massive datasets• A typical oil production platform is equipped
with about 400.000 sensors
• Telecom data is the most pervasive datasource in urban are, in Milano there are1.8 million mobile users
• A global contact centre of a Telecom operator counts 500 millions of clients
• Facebook alone has 1.1 billion of active users
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Emanuele Della Valle - http://streamreasoning.org/
Requirements 2/8
A system able to answer those queries must be able to
process data streams on the fly • The sensors on typical oil production
platform generates 10,000 observationsper minute with peaks of 100,000 o/m
• The mobile users in Milano generates20,000 call/sms/data connectionsper minute with peaks of 80,000 c/m
• A global contact centre receives10,000 contacts per minute withpeaks of 30,000 c/m
• Facebook, as of May 2013, observes3 millions "I like" per minute
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Emanuele Della Valle - http://streamreasoning.org/
Requirements 3/8
A system able to answer those queries must be able to
cope with heterogeneous dataset • The sensors on typical oil production
have been deployed over 10 yearsby 10s of different producers
• Tens of data sources are normallyneeded to make sense of an urbanphenomena
• A global contact centre consists in 100sof offices owned by different subsidiary companies engaged yearly
• Each social network has its owndata model, APIs, …
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Emanuele Della Valle - http://streamreasoning.org/
Requirements 4/8
A system able to answer those queries must be able to
cope with incomplete data • 10s of sensors and networking links
broke down daily
• Coverage is incomplete
• Only standard cases are covered byfully machine processable data records100s of contacts per minute are manage ad-hoc
• Conversations happen outside thesocial networks, too!
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Emanuele Della Valle - http://streamreasoning.org/
Requirements 5/8
A system able to answer those queries must be able to
cope with noisy data • Sensor out-of-operating range
• Faulty sensors
• Agents misunderstand, get tired, …
• Irony, sarcasm, …
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Emanuele Della Valle - http://streamreasoning.org/
Requirements 6/8
A system able to answer those queries must be able to
provide reactive answers • detection of dangerous situations
must occur within minutes
• recommendations to citizens mustbe performed in few seconds
• routing a contact through each step of the decision tree must take less than asecond
• Search autocompleting may needto be updated every few minutes
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Emanuele Della Valle - http://streamreasoning.org/
Requirements 7/8
A system able to answer those queries must be able to
support fine-grained information access • Identify a turbine among thousands
• Locate a bus among thousands
• Contact an agent among thousands
• Identify an opinion maker amongthousands of influencers for a topic
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Emanuele Della Valle - http://streamreasoning.org/
Requirements 8/8
A system able to answer those queries must be able to
integrate complex domain models of • operational and control process
• various city aspects
• contact management, contract types, agent skills, contactor profiles, …
• topics, user profiles, …
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Emanuele Della Valle - http://streamreasoning.org/
Requirements (wrap up)
A system able to answer those queries must be able to
handle massive datasets
process data streams on the fly
cope with heterogeneous dataset
cope with incomplete data
cope with noisy data
provide reactive answers
support fine-grained information access
integrate complex domain models
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Emanuele Della Valle - http://streamreasoning.org/
What are data streams anyway?
Formally: • Data streams are unbounded sequences of time-varying data
elements
Less formally: • an (almost) “continuous” flow of information
Assumption• recent information is more relevant as it describes the
current state of a dynamic system
time
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Emanuele Della Valle - http://streamreasoning.org/
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 in the late '90s
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Emanuele Della Valle - http://streamreasoning.org/
Continuous Semantics
Continuous queries registered over streams thatare observed trough windows
window
input streams streams of answerRegistered Continuous Query
Dynamic System
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Emanuele Della Valle - http://streamreasoning.org/
DSMS/CEP State of the Art
Gianpaolo Cugola, Alessandro Margara: Processingflows of information: From data stream to complex event processing. ACM Comput. Surv. 44(3): 15 (2012)
Content• Type of models compared
– Functional and processing– Deployment and interactions– Data, Time, and Rule– Language
• # of systems surveyed: – Academic: 24 – Industrial: 9– Total: 33
• To learn more:– http://home.dei.polimi.it/margara/papers/survey.pdf
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Emanuele Della Valle - http://streamreasoning.org/
Existing solutions
RequirementDSMS CEP
massive datasets
data streams
heterogeneous dataset
incomplete data
noisy data
reactive answers
fine-grained information access
complex domain models
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Emanuele Della Valle - http://streamreasoning.org/
Given ontology O and query Q, use O to rewrite Qas Q’ so that, for any set of ground facts A contained in multiple databases:• answer(Q,O,A) = answer(Q’,,A)
– The answer of the query Q using the ontology O for any set of ground facts A is equal to answer of a query Q’ without considering the ontology O
Use mapping M to map Q’ to multiple SQL queries to the various databases
Ontology Based Data Access
Rewrite
O
QQ’
MapSQL
M
answerA
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Emanuele Della Valle - http://streamreasoning.org/
Existing solutions
RequirementDSMS CEP
OBDA
massive datasets
data streams
heterogeneous dataset
incomplete data
noisy data
reactive answers
fine-grained information access
complex domain models
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Emanuele Della Valle - http://streamreasoning.org/
Research Question
is it possible to make sense in real time of multiple, heterogeneous, gigantic and inevitably noisy and incomplete data streams in order to support the decision process of extremely large numbers of concurrent user?
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Emanuele Della Valle - http://streamreasoning.org/
Stream Reasoning feasibility (intuition)
Many relevant reasoning methods are not able todeal with high frequency data streams
However, trade-off exists between the complexity of the reasoning method and the frequency of the data stream
ComplexityRaw Stream Processing
Semantic Streams
DL-Lite
DLAbstraction
Selection
Interpretation
Reasoning
Querying
Re-writing
Change Frequency
PTIME
NEXPTIME
104 Hz
1 Hz
Complexity vs. Dynamics
Heiner Stuckenschmidt, Stefano Ceri, Emanuele Della Valle, Frank van Harmelen: Towards Expressive Stream Reasoning. Proceedings of the Dagstuhl Seminar on Semantic Aspects of Sensor Networks, 2010.
AC0
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Emanuele Della Valle - http://streamreasoning.org/
Sub-research questions
Is it possible model at semantic levelheterogeneous data streams, continuous queries, and continuous reasoning tasks?
Does the ordered nature of data streams and the possibility to forget old enough information allow to optimize continuous querying and continuous reasoning tasks so to provide reactive answers to large number of concurrent users without forsaking correctness or completeness?
Can Semantic Web and Machine Learning technologies be jointly employed to cope with the noisy and incomplete nature of data streams?
Are there practical cases where processing data stream at semantic level is the best choice?
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Emanuele Della Valle - http://streamreasoning.org/
FindingsModel data stream at semantic level 1/2
State of the art: RDF• It allows to make statements about resources in the form
of subject-predicate-object expressions– In RDF terminology triples– E.g. @BarakObama posts "Four more years"
• A collection of RDF statements represents a labelled, directed graph– In RDF terminology a graph– E.g., the tweet above by Barak Obama is connected to
- 800,000+ twitter user profiles via retweets- 300,000+ twitter user profiles favorite- …
Contribution: RDF stream
subject predicate object
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Emanuele Della Valle - http://streamreasoning.org/
FindingsModel data stream at semantic level 2/2
State of the art: RDF
Contribution: RDF Stream• Unbound sequence of time-varying triples• each represented by a pair made of an RDF triple and its
timestamp
…
@BarakObama posts "Four more years", 8:16PM 6 Nov 2012
@Alice posts "RT: Four more years", 8:17PM 6 Nov 2012
…
D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus: Querying RDF streams with C-SPARQL. SIGMOD Record 39(1): 20-26 (2010)
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subject predicate object timestamp
Emanuele Della Valle - http://streamreasoning.org/
FindingsAdding continuous semantics to SPARQL
Who are the opinion makers? i.e., the users who arelikely to influence the behavior their followers
REGISTER STREAM OpinionMakers COMPUTED EVERY 5m AS
CONSTRUCT { ?opinionMaker sd:about ?resource }
FROM STREAM <http://…> [RANGE 30m STEP 5m]
WHERE {
?opinionMaker ?opinion ?resource .
?follower sioc:follows ?opinionMaker.
?follower ?opinion ?resource.
FILTER ( cs:timestamp(?follower) > cs:timestamp(?opinionMaker) )
}
HAVING ( COUNT(DISTINCT ?follower) > 3 )28
Emanuele Della Valle - http://streamreasoning.org/
FindingsAdding continuous semantics to SPARQL
Who are the opinion makers? i.e., the users who arelikely to influence the behavior their followers
REGISTER STREAM OpinionMakers COMPUTED EVERY 5m AS
CONSTRUCT { ?opinionMaker sd:about ?resource }
FROM STREAM <http://…> [RANGE 30m STEP 5m]
WHERE {
?opinionMaker ?opinion ?resource .
?follower sioc:follows ?opinionMaker.
?follower ?opinion ?resource.
FILTER ( cs:timestamp(?follower) > cs:timestamp(?opinionMaker) )
}
HAVING ( COUNT(DISTINCT ?follower) > 3 )
Query registration(for continuous execution)
FROM STREAM clause
WINDOW
RDF Stream added as new ouput format
Builtin to access timestamps
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Emanuele Della Valle - http://streamreasoning.org/
FindingsCope with the noisy and incomplete data
Noise is reduced using DSMS techniques
Deductive stream reasoning copes with incompleteness deducing implicit facts
Inductive stream reasoning copes "irrepairable" incompleteness inducing missing facts
D.F. 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 25(6): 32-41 (2010)
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Emanuele Della Valle - http://streamreasoning.org/
FindingsPractical cases
10+ deployments in Sensor Networks,Social media analytics and Cloud Computing, e.g.
BOTTARI
Winner of Semantic Web Challenge 2011
City Data Fusion
Winner of IBM faculty award 2013
M. Balduini, I. Celino, D. Dell’Aglio, E. Della Valle, Y. Huang, T. Lee, S.-H. Kim, V. Tresp: BOTTARI: An augmented reality mobile application to deliver personalized and location-based recommendations by continuous analysis of social media streams. J. Web Sem. 16: 33-41 (2012)
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Emanuele Della Valle - http://streamreasoning.org/
FindingsOptimize for reactive answers
C-SPARQL time window-based selections outperform SPARQL filter-based selections
Incremental Reasoning on RDF streams (IMaRS): new reasoning algorithm optimized for reactive query answering
D.F. Barbieri, D. Braga, S.Ceri, E. Della Valle, M. Grossniklaus: Incremental Reasoning on Streams and Rich Background Knowledge. ESWC (1) 2010: 1-15D. Dell'Aglio, E. Della Valle: Incremental Reasoning on RDF Streams. In A.Harth, K.Hose, R.Schenkel (Eds.) Linked Data Management, CRC Press 2014, ISBN 9781466582408
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Emanuele Della Valle - http://streamreasoning.org/
Agenda
Stream Reasoning in a nutshell
It's a streaming world
Requirements
State-of-the-art solutions
Stream Reasoning research questions
Affirmative answers collected in 5 years of investigations
A sample of Stream Reasoning investigation: IMaRS
Problem statement
State-of-the art solutions
Proposed solution
Experimental results
Conclusions 33
Emanuele Della Valle - http://streamreasoning.org/
The problem
Reasoning upon rapidly changing information
Example Context
Social Media Analytics: a set of methods and technologies for collecting, monitoring, analyzing, summarizing, and visualizing media generated and disseminated in a conversational, and distributed mode among online communities
Real query Which have been the most discussed posts in the last hour?
Simplified version How many posts have discussed A in the last 40 min?
Data
A B C
A, B, and C represent postsA B means B discusses AA C means C indirectly discusses ANOTE: discusses is transitive property
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Emanuele Della Valle - http://streamreasoning.org/
TimeEnter
windowExit
windowExplicitly in
windowInferred in
window # Exp. # Inf. SUM
How many posts have discussed A in the last 40 min?
The problem
A B
A B C A C
A B C
E
A C
A B C
E
A C
A C
E
A C
A B C
E
A C
CE
10:00 AB
10:10 BC
10:20 AE
10:30 EC
10:40 AB
10:50 BC
11:00 AE
1 1
1 1 2
2 1 3
2 1 3
1 1 2
1 1 2
0 0 0
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Emanuele Della Valle - http://streamreasoning.org/
Is it an hard problem?
In the database community it is known as the incremental view maintenance problem
The state of the art solution is the DRed algorithm• Overestimation of deletion: Overestimates deletions by
computing all direct consequences of a deletion.
• Rederivation: Prunes those estimated deletions for which alternative derivations (via some other facts in the program) exist.
• Insertion: Adds the new derivations that are consequences of insertions
Deletion are twice as expensive as insertions
A B C
E
A C
A B C
E
A C
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Emanuele Della Valle - http://streamreasoning.org/ 37
Is it an hard problem?
1000
100
10
0
rematerialize the view after each update
0% 2% 4% 6% 8% 10% 12% 14%
% of deletions w.r.t the size of the DB
ms
Break-even point
In a normal DB incremental view maintenance is convenient in most of the cases:• Lot of inserts, few deletions• Small % of deletions w.r.t.
the size of the DB
incremental view maintenance
Emanuele Della Valle - http://streamreasoning.org/ 38
Is it an hard problem?
1000
100
10
0
rematerialize the view after each update
0% 2% 4% 6% 8% 10% 12% 14%
% of deletions w.r.t the content of the window
ms
Break-even point
In a streaming setting • The number of inserts are equals
to the number of deletions• the data exiting the windows
causes large % of deletions w.r.t. the size of the window
incremental view maintenance
Emanuele Della Valle - http://streamreasoning.org/ 39
What is the target behaviour?
1000
100
10
0
rematerialize the view after each update
incremental view maintenance
0% 2% 4% 6% 8% 10% 12% 14%
ms
Break-even point
Target behaviour
% of deletions w.r.t the content of the window
Emanuele Della Valle - http://streamreasoning.org/
Observation
DRed works with random insertions and deletions
In a streaming setting, when a triple enters the window, given the size of the window, the reasoner knows already when it will be deleted!
E.g., • if the window is 40 minutes
long, and, • it is 10:00, the triple(s)
entering now• will exit on 10:40.
Conclusion• deletions are predictable
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Emanuele Della Valle - http://streamreasoning.org/
Optimized Stream Reasoning (IMaRS)
Idea: • add an expiration time to each triple and • use an hash table to index triples by their expiration time
The algorithm1. deletes expired triples 2. Adds the new derivations that are consequences of insertions
annotating each inferred triple with an expiration time (the min of those of the triple it is derived from), and
3. when multiple derivations occur, for each multiple derivation, it keeps the max expiration time.
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Emanuele Della Valle - http://streamreasoning.org/
TimeEnter
windowExit
windowExplicitly in
windowInferred in
window # Exp. # Inf. SUM
How many posts have discussed A in the last 40 min?
The problem
A B
A B C A C
10:00 AB
10:10 BC
1 1
1 1 2
10:40
10:40 10:50 10:40
The min of the two expiration times
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Emanuele Della Valle - http://streamreasoning.org/
TimeEnter
windowExit
windowExplicitly in
windowInferred in
window # Exp. # Inf. SUM
How many posts have discussed A in the last 40 min?
The problem
A B
A B C A C
A B C
E
A C
10:00 AB
10:10 BC
10:20 AE
1 1
1 1 2
2 1 3
10:40
10:40 10:50 10:40
10:40 10:50 10:4011:00
AC remains unchanged
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Emanuele Della Valle - http://streamreasoning.org/
TimeEnter
windowExit
windowExplicitly in
windowInferred in
window # Exp. # Inf. SUM
How many posts have discussed A in the last 40 min?
The problem
A B
A B C A C
A B C
E
A C
A B C
E
A C
10:00 AB
10:10 BC
10:20 AE
10:30 EC
1 1
1 1 2
2 1 3
2 1 3
10:40
10:40 10:50 10:40
10:40 10:50 10:4011:00
10:40 10:50 11:0011:00 11:10
AC is inferred with a longer lasting expiration time, the max of the expiration time is used
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Emanuele Della Valle - http://streamreasoning.org/
TimeEnter
windowExit
windowExplicitly in
windowInferred in
window # Exp. # Inf. SUM
How many posts have discussed A in the last 40 min?
The problem
A B
A B C A C
A B C
E
A C
A B C
E
A C
A B C
E
A C
10:00 AB
10:10 BC
10:20 AE
10:30 EC
10:40 AB
1 1
1 1 2
2 1 3
2 1 3
2 1 3
10:40
10:40 10:50 10:40
10:40 10:50 10:4011:00
10:40 10:50 11:0011:00 11:10
10:50 11:0011:00 11:10
This deletion causes no effect
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Emanuele Della Valle - http://streamreasoning.org/
TimeEnter
windowExit
windowExplicitly in
windowInferred in
window # Exp. # Inf. SUM
How many posts have discussed A in the last 40 min?
The problem
A B
A B C A C
A B C
E
A C
A B C
E
A C
A C
E
A C
A B C
E
A C
10:00 AB
10:10 BC
10:20 AE
10:30 EC
10:40 AB
10:50 BC
1 1
1 1 2
2 1 3
2 1 3
2 1 3
2 1 3
10:40
10:40 10:50 10:40
10:40 10:50 10:4011:00
10:40 10:50 11:0011:00 11:10
10:50 11:0011:00 11:10
11:0011:00 11:10
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This deletion causes no effect
Emanuele Della Valle - http://streamreasoning.org/
TimeEnter
windowExit
windowExplicitly in
windowInferred in
window # Exp. # Inf. SUM
How many posts have discussed A in the last 40 min?
The problem
A B
A B C A C
A B C
E
A C
A B C
E
A C
A C
E
A C
A B C
E
A C
CE
10:00 AB
10:10 BC
10:20 AE
10:30 EC
10:40 AB
10:50 BC
11:00 AE
1 1
1 1 2
2 1 3
2 1 3
2 1 3
2 1 3
0 0 0
10:40
10:40 10:50 10:40
10:40 10:50 10:4011:00
10:40 10:50 11:0011:00 11:10
10:50 11:0011:00 11:10
11:0011:00 11:10
Deletion of inferred triples is just a look up
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Emanuele Della Valle - http://streamreasoning.org/
Experimental results
Re-materialize after each window slide
Use DRed
IMaRS
% of deletions w.r.t. the content of the window
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Emanuele Della Valle - http://streamreasoning.org/
Implication of the experimental results
comparison of the average time needed to answera C-SPARQL query, when 2% of the content exits the window each time it slides, using • A backward reasoner on the window content• DRed + standard SPARQL on the materialization• IMaRS + standard SPARQL on the materialization
Backward DRed +SPARQL IMaRS + SPARQL
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Emanuele Della Valle - http://streamreasoning.org/
Agenda
Stream Reasoning in a nutshell
It's a streaming world
Requirements
State-of-the-art solutions
Stream Reasoning research questions
Affirmative answers collected in 5 years of investigations
A sample of Stream Reasoning investigation: IMaRS
Problem statement
State-of-the art solutions
Proposed solution
Experimental results
Conclusions 50
Emanuele Della Valle - http://streamreasoning.org/ 51
Scholarly impact #
of c
itatio
ns
year
SR Vision
C-SPARQL
IMaRS
Inductive SR
BOTTARI
Emanuele Della Valle - http://streamreasoning.org/
Recent WorkBenchmarks are needed
Stream Reasoning benchmarks are appearing• SRBench Ying Zhang, Minh-Duc Pham, Óscar Corcho, Jean-
Paul Calbimonte– http://www.w3.org/wiki/SRBench
• LSBench: Danh Le Phuoc, Minh Dao-Tran, Minh-Duc Pham, Peter A. Boncz, Thomas Eiter, Michael Fink– http://code.google.com/p/lsbench/
What shall we evaluate?• PeCK methodology: Sys Properties -> Challenges -> KPI
How?• Maximise throughput is OK, but do not forsake correctness!
52
T. Scharrenbach, J. Urbani, A. Margara, E. Della Valle, A. Bernstein: Seven Commandments for Benchmarking Semantic Flow Processing Systems. ESWC 2013: 305-319
D. Dell'Aglio, J.-P. Calbimonte, M. Balduini, Ó. Corcho, E. Della Valle: On Correctness in RDF Stream Processor Benchmarking. ISWC (2) 2013: 326-342
Emanuele Della Valle - http://streamreasoning.org/
Recent WorkSupporting industrial take up
W3C's RDF stream processing Community Group • http://www.w3.org/community/rsp/ • Goals
– collect use cases– cense approaches– define RDF stream models– define SPARQL extension for RDF stream processing– define RESTful interfaces
• 52 members from 24 organization (6 industries)• Biweekly phone calls since September 2013
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Emanuele Della Valle - http://streamreasoning.org/
Next stepGeneralizing from time to any ordering
Observation: order reflects recency, relevance, …
Indexes
Recency
Relevance …
Combinations
Typ
es o
f o
rder
s
No Yes
Traditional solutions
DSMS/CEP
Top-k Q/A
Continuous top-k Q/A
Scalable reasoning
Stream reasoning
Order-aware reasoning
Top-k Reasoning
Reasoning
Emanuele Della Valle, Stefan Schlobach, Markus Krötzsch, Alessandro Bozzon, Stefano Ceri, Ian Horrocks: Order matters! Harnessing a world of orderings for reasoning over massive data. Semantic Web 4(2): 219-231 (2013)
Sara Magliacane, Alessandro Bozzon, Emanuele Della Valle: Efficient Execution of Top-K SPARQL Queries. International Semantic Web Conference (1) 2012: 344-360
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Emanuele Della Valle - http://streamreasoning.org/
Credits
Politecnico di Milano’s colleagues Vision: Prof. S. Ceri C-SPARQL: M. Balduini, D. Barbieri, D. Braga, S. Ceri and M.
Grossniklaus Benchmarking: M. Balduini, D. Dell'Aglio, and A. Margara SPARQL-RANK: S. Magliacane and A. Bozzon People I directly worked with on the topic CEFRIEL: I. Celino Saltlux: T. Lee and S.-H. Kim SIEMENS: prof. V. Tresp and Y. Huang U-Innsbruck: prof. D. Fensel U-Oxford: prof. I. Horrocks and M. Krötzsch UP-Madrid: Ó. Corcho and J.-P. Calbimonte U-Mannheim: prof. H. Stuckenschmidt U-Zurich: prof. A. Bernstein and T. Scharrenbach VA-Amsterdam: prof. F. van Harmelen, S. Schlobach and J. UrbaniThe broader research community that showed interest in stream reasoning
55
Stream Reasoning http://streamreasoning.org/
Thank you for paying attention!
Any question?Emanuele Della Valle
emanuele.dellavalle@polimi.it
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