A Hierarchical approach towards Efficient and Expressive Stream Reasoning

RR - 2016 - Aberdeen - Riccardo Tommasini (Politecnico di Milano)
A Hierarchical approach towards Efficient
and Expressive Stream Reasoning
Riccardo Tommasini (Ph.D Student at Politecnico di Milano, DEIB )
Advisor: Emanuele Della Valle (Assistant Professor at Politecnico di Milano, DEIB)
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Web Reasoning and Rule Systems Conf. 2016,
Doctoral Consortium

Introduction
2

RR - 2016 - Aberdeen - Riccardo Tommasini (Politecnico di Milano) 3

Complex Domains
Incomplete
Vast
Noisy
Rapidly Changing
Reactive
Time Aware
Heterogeneous

Stream Reasoning
Supports complex domains decision making 
in real-time (reactively).
I.e., making sense of 
vast and heterogeneous, 
noisy and incomplete 
streams of data.
5
Vision

Stream Processing and Reasoning
Data Stream Management Systems (DSMS) e.g., Esper, Flink
Complex Event Processing Engines (CEP) e.g., Drools Fusion, Esper. 
RDF Stream Processing (RSP) e.g., C-SPARQL, CQELS, SKB. 
Rule Based Systems e.g., (RBS) EP-SPARQL, Sparkwave. 
Ontology Based Data Access (OBDA) e.g., Morphstream, STARQL. 
Incremental Maintenance of Ontology Materialisation (IMOM), e.g,
RDFox, TrOWL
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State-of-the-art

SR DSMS CEP RSP RBS OBDA IMOM
Vast x x x
Heterogeneous x x x x x
Noisy x x
Incomplete x x x x
Stream x x x
Time-Aware x x x
Complex Domains x x x
Approaches VS Challenges

Research Question
Can we realise an expressive and efficient stream reasoning?

Research Question
Can we realise an expressive and efficient stream reasoning?
Still unanswered!

Research Question
Can we realise an expressive and efficient stream reasoning,
using a hierarchical approach?

Cascading Reasoning
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Stuckenschmidt, H., Ceri, S., Della Valle, E., & Van Harmelen, F.
(2010). Towards expressive stream reasoning

Cascading Reasoning vs State-of-the-art
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C-SPARQL
EP-SPARQL
trOWL
ESPER

Information
Integration Systems
The role of II systems is to
provide a uniform view of
the data in the sources.
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Integrated Conceptual
Model (ICM)
Mappings
Data 
Sources
Query
Wrappers

Information Integration Systems
Integrated Conceptual Model (ICM), i.e., a common
vocabulary, formally defined, that enables query answering.
Mapping, i.e., (typically) FOL statements that establish
links between ICM and data sources.
Wrapper, i.e., interfaces to reinterpret the data source
into a data model that enables the mapping.
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at a glance

Cascading Reasoning VS Information Integration
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z
ICM
z
Wrapping
z
Mapping

Research Plan
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Research Questions
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Q.1, Can we extend the mapping language to include time-
related operators (e.g. windows) and engines operational
semantics?
Q.2, Can we extend the ontological language to include time
operators without degenerate into intractability?
Q.3, Can we enable a systematic comparative research
approach for stream reasoners?

semantics?
operators without degenerate into intractability?
Research Questions
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approach for stream reasoners?

Research Questions: Q.1
19
Q.1
relates with rewriting and interpretation

Q.1 Research Plan
(i) include the continuous semantics to enable continuous
querying over virtual RDF Stream data sources;
(ii) include time aware operators, e.g. windows, to enable
rewriting over continuous query languages e.g. EPL;
(iii) enable the description of stream processors execution
semantics.
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Extending mapping language to

Research Questions: Q.1
21
Q.2
relates with reasoning and abstraction

Q.2 Research Plan
(i) identify meaningful OWL 2 DL fragments for Stream
Reasoning.
(ii) consider temporal extension of DLs that do not
degenerate to intractability.
(ii) exploit time-related operators typical of complex event
processing or event calculus to provide rule based reasoning.
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Extend the ICM language to

Evaluation Plan
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A good
evaluation
by Nico Matentzoglu
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Stream Reasoning Benchmarking
Mostly related to RDF Stream Processing
Focused on query answering
Limited Entailment (RDFS subsets)
Lack of expressive benchmarks
Lack of shared approaches
No absolute winner (RSP)
25

Research Questions
semantics?
operators without degenerate into intractability?
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approach for stream reasoners benchmarking?

Benchmark Principles
The goal of a domain specific benchmark is to foster
technological progress by guaranteeing a fair
assessment.
Jim Gray, The Benchmark Handbook  
for Database and Transaction Systems, 1993
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Experiment
Design
for Stream Reasoning
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is the engine used as subject in the
experiment;
is an ontology and any data not subject
to change during the experiment.
is the description of the input data
streams:
is the set of continuous queries
registered into the engine
is the set of key performance
indicators (KPIs) to collect.
The result of the execution of an
experiment is a Report that captures
the engine dynamics.
E
T
Q
D
K
R

Test Stand Architecture
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RSP Baselines
The minimal meaningful
approaches to realise an
RSP engine
Pipeline of DSMS and a reasoner;
Support reasoning under the ρDF
entailment regime;
Data can flows from the DSMS to the
reasoner via snapshots (i.e. Figure 2-A)
or differences ( Figure 2-B);
They exploit absolute time, i.e. their
internal clock can be externally
controlled.
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Comparative Analysis Enabled
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Comparative Analysis Enabled
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Achievements and Future Works
Conclusion

Lessons Learned
- Stream Reasoning benchmarking requires further
investigations
- RSP research is mature (active w3c group), but still its
role can be further investigated
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Achievements
- Publication: Heaven: a framework for systematic
comparative research approach for RSP engines (ESWC 2016)
- Promising work for semantic Complex Event Processing
- First steps towards a “naïve” implementation of cascading
reasoning (collaboration with UGENT)
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Questions?
Email: riccardo.tommasini@polimi.it 
Twitter: @rictomm
Github: riccardotommasini
Web: streamreasoning.org
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A Hierarchical approach towards Efficient and Expressive Stream Reasoning

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