Stream Reasoning: An Overview of Stream Reasoning and C-SPARQL

Streaming Day:
an overview of Stream Reasoning
by: Riccardo Tommasini
1
Scuola di Ingegneria Industriale e dell’Informazione
Computer Science and Engineering

Master Degree Thesis – Riccardo Tommasini
Agenda
2
Background
Stream
Reasoning
Get
in
Touch
Heaven
✓
SR
Example

GiT - Riccardo Tommasini
3
Master Degree in C.S. @ Politecnico Of Milano
M.D. Thesis on Stream Reasoning
I’ll start my Phd in November 2k15

GiT - Research Topic & Areas of Interest
4
• StreamReasoning @ CEP
• Techniques and Methods for
Stream Reasoners Benchmarking
• RESTfull API
• Software Testing
• Programming Languages
RDF
Stream
Processing
Software
Engineering

GiT - Stream Reasoning Research Group
5
Daniele

Dell’Aglio

Phd
Emanuele

Della
Valle

Advisor
Marco

Balduini

Phd

Agenda
6
Background
Stream
Reasoning
Get
in
Touch
Heaven
✓
SR
Example

Background - Semantic Web
7
It provides a common
framework to allow
interoperability applications.
The Semantic Web is a
WWW extension.
Semantic Web world
involves several
technologies.

Background - RDF
8
Let I, B and L be three pairwise disjoint sets, deﬁned as IRIs, Blank Nodes and
Literals, respectively. A triple
(s, p, o) ∈ (I ∪ B)I(I ∪ B ∪ L) 
is an RDF triple, while a set of RDF triples is called an RDF graph.
subject object
predicate
RDF describes a conceptual model of information in any given domain.

Background - OWL
9
• Web Ontology Language (OWL) is a language for
writing ontologies for the Web
• An Ontology is a a speciﬁcation of a conceptualisation
(Tom Gruber)
• OWL extends RDF allowing to speciﬁc more about
properties and classes
• OWL extends RDF enabling reasoning:
• Check logical correctness of statements
• Infer implied statements w.r.t. a set of inferences rules

Background - SPARQL
10
SPARQL Protocol and RDF Query Language 3 main parts

• CONSTRUCT query: used to provide an RDF graph created directly from the results of the query.

• SELECT query: used to extract a set of variables and their matching values, called set of mappings in the table format.

• Dataset clause -> FROM or FROM Named

• WHERE: provides the graph pattern to match against the data graph.

Background - C-SPARQL
11
RICORDARE CAMBIO SEMANTICA!!!!

Csparql language extends sparql in every 3 parts of query forms

Query form -> STREAM CLAUSE to create a RDF stream as query results

Datasert clause -> FROM STREAM clause added to let engine get data from RDF streams speciﬁed by URI

Where Clause -> built in timestamp function to retrieve the timestamp of every single triple in the engine

Background - DSMS vs CEP
12
Q
Q
Q
Q
Throw
Scratch
Store
Stream
Stream 1
Stream2
Stream n
…
Complex Event
Processing
Engine
Event Observers Event Consumers
Processing Flows of Information: From Data Stream to Complex Event Processing
- Gianpaolo Cugola & Alessandro Margara
Heterogeneous data stream
processing
Data semantic is up to the client
Incoming data are notiﬁcation of
events
Events are semantically evaluate
through rules
Pub/Sub Model
CEP
DSMS
Continuous queries execution

Master Degree Thesis – Riccardo Tommasini 13
Background - Time Based Window
Tumbling

Window
Sliding

Window
Window Dimension ω [ms] Slide Parameter β [ms]

Agenda
14
Background
Stream
Reasoning
Get
in
Touch
Heaven
✓
SR
Example

Stream Reasoning (SR)
15
Reasoning upon heterogeneous and rapidly
changing information flows.
-- S. Ceri, E. Della Valle, F. van Harmelen and H. Stuckenschmidt, 2010

SR - RSP Engine
16
RDF
Stream

Processing

Engine

SR - RSP Engine
16
RDF
Stream

Processing

Engine
heterogeneous data (unbounded) streams

SR - RSP Engine
16
RDF
Stream

Processing

Engine
data streams integration through RDF data model

SR - RSP Engine
16
RDF
Stream

Processing

Engine
continuously infers implied triples w.r.t. ontology T
T

< ,Q>
SR - RSP Engine
16
RDF
Stream

Processing

Engine
continuously infers implied triples w.r.t. ontology T
continuous querying (Q) answering
T

SR - RSP Engine Execution Semantics
17
S2R
Operator
Window
R2R
Operator
SPARQL
R2S
Operator
Rstream,Itream,Dstream
RDF Stream
RDF Stream
Engine Stream
Mappings Mappings

17
S2R
Operator
Window
R2R
Operator
SPARQL
R2S
Operator
RDF Stream
RDF Stream
Engine Stream
Mappings Mappings
Stream
to
Relation

17
S2R
Operator
Window
R2R
Operator
SPARQL
R2S
Operator
RDF Stream
RDF Stream
Engine Stream
Mappings Mappings
Stream
to
Relation
Relation
to
Relation

17
S2R
Operator
Window
R2R
Operator
SPARQL
R2S
Operator
RDF Stream
RDF Stream
Engine Stream
Mappings Mappings
Stream
to
Relation
Relation
to
Relation
Relation
to
Stream

SR - C-SPARQL Engine
Input Triple
Inferred Triple

RDF Stream
DSMS
Input Triple
Inferred Triple

RDF Stream
DSMS
active
window
Input Triple
Inferred Triple

RDF Stream
DSMS Reasoner
T,Q
active
window
Input Triple
Inferred Triple

RDF Stream
DSMS Reasoner
RDF Stream
T,Q
active
window
Input Triple
Inferred Triple

RDF Stream
DSMS Reasoner
RDF Stream
T,Q
C-SPARQL
Query
active
window
Input Triple
Inferred Triple

RDF Stream
DSMS Reasoner
RDF Stream
T,Q
Continuous
Query
SPARQL
Query
C-SPARQL
Query
active
window
Input Triple
Inferred Triple

Agenda
19
Background
Stream
Reasoning
Get
in
Touch
Heaven
✓
SR
Example

BlueRoom RedRoom
is with
Running Example

BlueRoom RedRoom
RedSensor
BlueSensor
is with
Running Example

BlueRoom RedRoom
RedSensor
BlueSensor
R
Alice
R RFID is with
Running Example

BlueRoom RedRoom
RedSensor
BlueSensor
R
Alice
Bob
R RFID is withFoursquare
Running Example

BlueRoom RedRoom
RedSensor
BlueSensor
R
Alice
David
Bob
Carl
Elena
R RFID is withf FacebookFoursquare
Running Example

▪ Four ways to learn who is where
Sensor Room Person Time-stamp
RedSensor RedRoom Alice T1
… … … …
Person ChecksIn Time-stamp
Bob BlueRoom T2
… … …
Person IsIn With Time-stamp
Carl null Bob T2
David RedRoom Elena T3
… … … …
Running Example - Which Data?

Running Example - Data Model
22
Streaming Data Static Data
isWith
isConnectedTo

RDF graph Time-stamp Stream
:RedSensor :observes [ :who :Alice; :where :RedRoom ] . t1 sensors
:Bob :posts [ :who :Bob ; :where :RedRoom] . t2 foursquare
• Data
• Query
REGISTER QUERY whoIsInWhichRoom? AS  
PREFIX : <http://…/sr4ld2014-onto#>  
SELECT ?x ?room ?person 
FROM STREAM <http://…/fs> [RANGE 1m STEP 10s]  
FROM STREAM <http://…/sensors> [RANGE 1m STEP 10s]
WHERE { ?x :observes [ :who ?person ; :where ?room ] .}
• Results at t2+10s
23
?x ?room ?person
:RedSensor :RedRoom :Alice
:Bob :RedRoom :Bob
Running Example - Query

Agenda
24
Background
Stream
Reasoning
Get
in
Touch
Heaven
✓
SR
Example

Heaven - Research Question
My
contributions
are
Can
we
enable

Systematic
Comparative
Research

Approach
of
RSP
Engines,
exploiting
existing
queries,

dataset
and
metrics?
25

My
contributions
are
Can
we
enable

Systematic
Comparative
Research

Approach
of
RSP
Engines,
exploiting
existing
queries,

dataset
and
metrics?
Test
Stand
25

Evaluate
engines
with

Test
Stands
26
In Aerospace engineering…
Experimental Environment
Reproducibility, Repeatability, Comparability
Evaluation of running systems
Heaven - Test Stand

Heaven - Test Stand
27
Disk
ResultCollectorStreamer
RSPEngine
Experiment
Analyser
Start
MB
Stop
TestStand
MB

My
contributions
are
Can
we
enable

Systematic
Comparative
Research

Approach
of
RSP
Engines,
exploiting
existing
queries,

dataset
and
metrics?
28
Test
Stand

My
contributions
are
Can
we
enable

Systematic
Comparative
Research

Approach
of
RSP
Engines,
exploiting
existing
queries,

dataset
and
metrics?
Method
28
Test
Stand

Heaven - Analyser
I
develop
a
layered
investigation
method,
which
tries

answer
different
possible
question
about
RSP
Engine
L0
-‐

How
to
choose
an
engine?
L1
-‐

What
distinguish
an
engine?
L2
-‐

When
choosing
an
engine?
L3
-‐

Why
choosing
this
engine?
29
Causalità dei livelli, sarebbe bello poter dire sempre quale engine è migliore

My
contributions
are
Can
we
enable

Systematic
Comparative
Research

Approach
of
RSP
Engines,
exploiting
existing
queries,

dataset
and
metrics?
Test
Stand
Method
30

My
contributions
are
Can
we
enable

Systematic
Comparative
Research

Approach
of
RSP
Engines,
exploiting
existing
queries,

dataset
and
metrics?
Test
Stand
Baselines
Method
Analysis
30

Heaven - Dashboard Example
31
IncreasingWindowDimension(ms)

31
Memory(mb)
Latency(ms)

31
Memory(mb)
Latency(ms)
Memory(mb)
Latency(ms)

31
Memory(mb)
Latency(ms)
Memory(mb)
Latency(ms)
Memory(mb)
Latency(ms)

31
Memory(mb)
Latency(ms)
Memory(mb)
Latency(ms)
Memory(mb)
Latency(ms)
Memory(mb)
Latency(ms)

Heaven - Pattern Identification Example
32
6.3 SOAK Test Evaluation Results
(a) Graph Naive
Triple Slots
in Number
Window 1 10 100 1000 10000
1
10
100
1000
10000
(b) Graph Incremental
Triple Slots
in Number
Window 1 10 100 1000 10000
1
10
100
1000
10000
Table 6.11 – The ﬁgure shows the representation in the time domain of mem-
ory for GN (a) and GI (b).
117
Memory
Naive
cancellare graph lasciare naive

Heaven- Visual Comparison Example
33

Agenda
34
Semantic
Web
Stream
Reasoning
Get
in
Touch
Heaven
✓

Thank You
35
Thank
You!

Contact
36
RiccardoTommasini+
@rictomm
tomma156
riccardo@knoesis.orgRiccardo Tommasini
riccardotommasini

Resources
37
Streamreasoning.org
StreamReasoning@GitHub
RDF Stream Processors
PhD CEP Course @Polimi
Stream Reasoning Tutorial
C-SPARQL Engine
Quick start available

Source code are released open source under Apache 2.0

C-SPARQL Engine

https://github.com/streamreasoning/CSPARQL-engine

https://github.com/streamreasoning/CSPARQL-ReadyToGoPack

Resources
37
Streamreasoning.org
StreamReasoning@GitHub
RDF Stream Processors
PhD CEP Course @Polimi
Stream Reasoning Tutorial
Esper
Jena
C-SPARQL Engine
Quick start available

Source code are released open source under Apache 2.0

C-SPARQL Engine

https://github.com/streamreasoning/CSPARQL-engine

https://github.com/streamreasoning/CSPARQL-ReadyToGoPack

Stream Reasoning: An Overview of Stream Reasoning and C-SPARQL

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