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Reactive Processing of RDF
Streams of Events
Jean-Paul Calbimonte and Karl Aberer
LSIR EPFL
DeRiVE 2015. Extended Semantic Web Conference ESWC 2015
Portoroz, 31.05.2015
@jpcik
Why Events?
2
They seem to be fun
Countless physical and logical
types of situations
Events on the Web
3
what
where
when
Event context
Time
Location
Agents/actors
Interactions
Semantics of Events
4
http://purl.org/NET/c4dm/event.owl#
Event ontology
Storyline ontology
http://purl.org/ontology/storyline/
Schema.org
Why Streams?
Internet of Things
Sensor Networks
Mobile Networks
Smart Devices
Participatory Sensing
Transportation
Financial Data
Social Media
Urban Planning
Health Monitoring
Marketing
“It’s a
streaming
world!”[1]
5 Della Valle, et al : It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems
Why Streams?
Web standards
Data discovery
Data sharing
Web queries
GoWeb
Semantics
Vocabularies
Data Harvesting
Data linking
Matching
Integration
Ontologies
Expressivity
Inference
Rule processing
Knowledge basesReasoning
Query languages
Query answering
Efficient processing
Query Federation
Processing
6
Raw Streams to Semantics
7
Raw observations
Patterns
Semantics Upstairs | Standing | Downstairs
Example: Activity recognition
TimeSeriesSymbolsOntologies
Example: SSN Ontology
8
Data items
9
• With data item we can refer to:
1. A triple
2. A graph
<:alice :isWith :bob>
<:alice :posts :p>
<:p :who :bob>
<:p :where :redRoom>
:graph1
RDF stream model
10
• A commonly adopted RDF stream model
• A RDF triple is an event
• Application time: point-based
<:alice :isWith :bob>:[1]
<:alice :isWith :carl>:[3]
<:bob :isWith :diana>:[6]
...
e1 e2 e3 e4S
t3 6 91
:alice :isWith :bob
:alice :isWith :carl
:bob :isWith :diana
:diana :isWith :carl
RDF Stream Processing
11
RSP
Engine
RDF
graphs
input RDF streams streams of results
background
knowledge
continuous queries
streamproducers
RSP
Engine
producer
subscribe
notify
cont. query consumer
push results
subscribe
streamconsumers
continuous queries
RSP Implementations
RDF Stream…
Gi
Gi+1
Gi+2
…
Gi+n
…
unboundedsequence
Gi {(s1,p1,o1),
(s2,p2,o2),…} [ti]
1+ triples
implicit/explicit
timestamp/interval
public class SensorsStreamer extends RdfStream implements Runnable {
public void run() {
..
while(true){
...
RdfQuadruple q=new RdfQuadruple(subject,predicate,object,
System.currentTimeMillis());
this.put(q);
}
}
}
How do I code this?
something
to run on a
thread
timestamped
triple
the stream is
“observable”
Data structure, execution
and callbacks are mixed
12
Observer pattern
Tightly coupled listener
Reactive Systems
Event-Driven
Jonas Boner. Go Reactive: Event-Driven, Scalable, Resilient & Responsive Systems. 2013.
Events:
reactto
ScalableLoad:
ResilientFailure:
ResponsiveUsers:
13
RSP Producer & Consumer
14
Proce
ssor
faster producers >> slower processor/consumer
Prod
ucer
Prod
ucer
Prod
ucer
Prod
ucer
RDF streams Cons
umer
Cons
umer
Cons
umer
unresponsive
overload
Overload of the processor/receiver
Unresponsiveness in stream processor
Actor Model
15
Actor
1
Actor
2
m
No shared mutable state
Avoid blocking operators
Lightweight objects
Loose coupling
communicate
through messages
mailbox
state
behavior
non-blocking response
send: fire-forget
Implementations: e.g. Akka for Java/Scala
Producer
Actor Processor
Actor
asynchronous messages
non-blocking response
stream of triples
Consumer
Actor
stream of results
Prod
ucer
Prod
ucer
Prod
ucer
Cons
umer Cons
umer
Cons
umer
Cons
umer
RDF Stream
object DemoStreams {
...
def streamTriples={
Iterator.from(1) map{i=>
...
new Triple(subject,predicate,object)
}
}
Data structure
Infinite triple
iterator
Execution
val f=Future(DemoStreams.streamTriples)
f.map{a=>a.foreach{triple=>
//do something
}}
Asynchronous
iteration
Message passing
f.map{a=>a.foreach{triple=>
someSink ! triple
}}
send triple to
actor
Immutable RDF stream
 avoid shared mutable state
 avoid concurrent writes
 unbounded sequence
Ideas using akka actors
Futures
 non blocking composition
 concurrent computations
 work with not-yet-computed results
Actors
 message-based
 share-nothing async
 distributable
16
Dynamic Push-Pull
17
Producer
Consumer
m
data flow
demand flow
Push when consumer is faster
Pull when producer is faster
Dynamically switch modes
Communication is dynamic
depending on demand vs supply
Producer Consumer
m
m
m m
m
m
m
m
m
m
push
Evaluation: throughput
18
Basic dynamic pull push
On top of CQELS
Limitations of Thread model
Not yet fully async
Reactive RSP workflows
19
Morph
Streams
CSPARQL
s
Etalis
TrOWL
s
s CQELS
Dyna
mite
s
Minimal agreements: standards, serialization, interfaces
Formal models for RSPs and reasoning
Working prototypes/systems!
Event-driven message passing
Async communication
Immutable streams
Transparent Remoting
Parallel and distributed
Supervised Failure Handling
Responsive processing
Reactive RSPs
Muchas gracias!
Jean-Paul Calbimonte
LSIR EPFL
@jpcik
The RSP Community
Research work
Many Papers
PhD Thesis
Datasets
Prototypes
Benchmarks
RDF Streams
Stream Reasoning
Complex Event Processing
Stream Query Processing
Stream Compression
Semantic Sensor Web
Manytopics
Tonsofwork
http://www.w3.org/community/rsp
W3C RSP Community Group
Effort to our work on RDF stream processing
discuss
standardize
combine
formalize
evangelize
21
The RSP Community
22
RDF Stream
23
… other issues:
Graph implementation?
Timestamps: application vs system?
Serialization?
 Loose coupling
 Immutable data streams
 Asynchronous message passing
 Well defined input/output
⑥ Guarantee data safety and availability
Restart,
Suspend,
Stop,
Escalate, etc
24
Parent
Actor
1
Automatic supervision
Isolate failures
Manage local failures
Supervision strategies:
All-for-One
One-for-one
Death watch handling
Supervision
hierarchy
Supervision
Actor
2
Actor
3
Actor
4
X
Actors everywhere
25
Actor
1
Actor
2
m No difference in
one core
many cores
many servers
Actor
3
Actor
4
Transparent Remoting
Locality optimization
Define Routing policies
Define actor clusters
m
m
Existing ‘map reduce’ for streams: Storm, S4, Spark, Akka Streams
Create workflows of Stream Processors?

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Streams of RDF Events Derive2015

  • 1. Reactive Processing of RDF Streams of Events Jean-Paul Calbimonte and Karl Aberer LSIR EPFL DeRiVE 2015. Extended Semantic Web Conference ESWC 2015 Portoroz, 31.05.2015 @jpcik
  • 2. Why Events? 2 They seem to be fun Countless physical and logical types of situations
  • 3. Events on the Web 3 what where when Event context Time Location Agents/actors Interactions
  • 4. Semantics of Events 4 http://purl.org/NET/c4dm/event.owl# Event ontology Storyline ontology http://purl.org/ontology/storyline/ Schema.org
  • 5. Why Streams? Internet of Things Sensor Networks Mobile Networks Smart Devices Participatory Sensing Transportation Financial Data Social Media Urban Planning Health Monitoring Marketing “It’s a streaming world!”[1] 5 Della Valle, et al : It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems
  • 6. Why Streams? Web standards Data discovery Data sharing Web queries GoWeb Semantics Vocabularies Data Harvesting Data linking Matching Integration Ontologies Expressivity Inference Rule processing Knowledge basesReasoning Query languages Query answering Efficient processing Query Federation Processing 6
  • 7. Raw Streams to Semantics 7 Raw observations Patterns Semantics Upstairs | Standing | Downstairs Example: Activity recognition TimeSeriesSymbolsOntologies
  • 9. Data items 9 • With data item we can refer to: 1. A triple 2. A graph <:alice :isWith :bob> <:alice :posts :p> <:p :who :bob> <:p :where :redRoom> :graph1
  • 10. RDF stream model 10 • A commonly adopted RDF stream model • A RDF triple is an event • Application time: point-based <:alice :isWith :bob>:[1] <:alice :isWith :carl>:[3] <:bob :isWith :diana>:[6] ... e1 e2 e3 e4S t3 6 91 :alice :isWith :bob :alice :isWith :carl :bob :isWith :diana :diana :isWith :carl
  • 11. RDF Stream Processing 11 RSP Engine RDF graphs input RDF streams streams of results background knowledge continuous queries streamproducers RSP Engine producer subscribe notify cont. query consumer push results subscribe streamconsumers continuous queries RSP Implementations
  • 12. RDF Stream… Gi Gi+1 Gi+2 … Gi+n … unboundedsequence Gi {(s1,p1,o1), (s2,p2,o2),…} [ti] 1+ triples implicit/explicit timestamp/interval public class SensorsStreamer extends RdfStream implements Runnable { public void run() { .. while(true){ ... RdfQuadruple q=new RdfQuadruple(subject,predicate,object, System.currentTimeMillis()); this.put(q); } } } How do I code this? something to run on a thread timestamped triple the stream is “observable” Data structure, execution and callbacks are mixed 12 Observer pattern Tightly coupled listener
  • 13. Reactive Systems Event-Driven Jonas Boner. Go Reactive: Event-Driven, Scalable, Resilient & Responsive Systems. 2013. Events: reactto ScalableLoad: ResilientFailure: ResponsiveUsers: 13
  • 14. RSP Producer & Consumer 14 Proce ssor faster producers >> slower processor/consumer Prod ucer Prod ucer Prod ucer Prod ucer RDF streams Cons umer Cons umer Cons umer unresponsive overload Overload of the processor/receiver Unresponsiveness in stream processor
  • 15. Actor Model 15 Actor 1 Actor 2 m No shared mutable state Avoid blocking operators Lightweight objects Loose coupling communicate through messages mailbox state behavior non-blocking response send: fire-forget Implementations: e.g. Akka for Java/Scala Producer Actor Processor Actor asynchronous messages non-blocking response stream of triples Consumer Actor stream of results Prod ucer Prod ucer Prod ucer Cons umer Cons umer Cons umer Cons umer
  • 16. RDF Stream object DemoStreams { ... def streamTriples={ Iterator.from(1) map{i=> ... new Triple(subject,predicate,object) } } Data structure Infinite triple iterator Execution val f=Future(DemoStreams.streamTriples) f.map{a=>a.foreach{triple=> //do something }} Asynchronous iteration Message passing f.map{a=>a.foreach{triple=> someSink ! triple }} send triple to actor Immutable RDF stream  avoid shared mutable state  avoid concurrent writes  unbounded sequence Ideas using akka actors Futures  non blocking composition  concurrent computations  work with not-yet-computed results Actors  message-based  share-nothing async  distributable 16
  • 17. Dynamic Push-Pull 17 Producer Consumer m data flow demand flow Push when consumer is faster Pull when producer is faster Dynamically switch modes Communication is dynamic depending on demand vs supply Producer Consumer m m m m m m m m m m push
  • 18. Evaluation: throughput 18 Basic dynamic pull push On top of CQELS Limitations of Thread model Not yet fully async
  • 19. Reactive RSP workflows 19 Morph Streams CSPARQL s Etalis TrOWL s s CQELS Dyna mite s Minimal agreements: standards, serialization, interfaces Formal models for RSPs and reasoning Working prototypes/systems! Event-driven message passing Async communication Immutable streams Transparent Remoting Parallel and distributed Supervised Failure Handling Responsive processing Reactive RSPs
  • 21. The RSP Community Research work Many Papers PhD Thesis Datasets Prototypes Benchmarks RDF Streams Stream Reasoning Complex Event Processing Stream Query Processing Stream Compression Semantic Sensor Web Manytopics Tonsofwork http://www.w3.org/community/rsp W3C RSP Community Group Effort to our work on RDF stream processing discuss standardize combine formalize evangelize 21
  • 23. RDF Stream 23 … other issues: Graph implementation? Timestamps: application vs system? Serialization?  Loose coupling  Immutable data streams  Asynchronous message passing  Well defined input/output
  • 24. ⑥ Guarantee data safety and availability Restart, Suspend, Stop, Escalate, etc 24 Parent Actor 1 Automatic supervision Isolate failures Manage local failures Supervision strategies: All-for-One One-for-one Death watch handling Supervision hierarchy Supervision Actor 2 Actor 3 Actor 4 X
  • 25. Actors everywhere 25 Actor 1 Actor 2 m No difference in one core many cores many servers Actor 3 Actor 4 Transparent Remoting Locality optimization Define Routing policies Define actor clusters m m Existing ‘map reduce’ for streams: Storm, S4, Spark, Akka Streams Create workflows of Stream Processors?