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)

Web Reasoning and Rule Systems Conf. 2016,

Doctoral Consortium

Introduction

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

Complex Domains

Incomplete

NoisyRapidly 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.

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

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 QuestionCan we realise an expressive and efficient stream reasoning?

Still unanswered!

Research QuestionCan we realise an expressive and efficient stream reasoning,

using a hierarchical approach?

Cascading Reasoning

Stuckenschmidt, H., Ceri, S., Della Valle, E., & Van Harmelen, F.

(2010). Towards expressive stream reasoning

Cascading Reasoning vs State-of-the-art

C-SPARQL

EP-SPARQL

Information Integration SystemsThe role of II systems is to provide a uniform view of the data in the sources.

Integrated Conceptual Model (ICM)

Mappings

Data Sources

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.

at a glance

Cascading Reasoning VS Information Integration

z Wrappingz Mapping

Research Plan

Research Questions

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?

Q.2, Can we extend the ontological language to include time operators without degenerate into intractability?

Research Questions

Q.3, Can we enable a systematic comparative research approach for stream reasoners?

Research Questions: 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.

Extending mapping language to

Research Questions: Q.1

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.

Extend the ICM language to

Evaluation Plan

A good evaluation

by Nico Matentzoglu

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)

Research Questions

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 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

Experiment Design

for Stream Reasoning

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.

Test Stand Architecture

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.

Comparative Analysis Enabled

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

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)

Questions?Email: riccardo.tommasini@polimi.it Twitter: @rictomm Github: riccardotommasini Web: streamreasoning.org

a hierarchical approach towards efficient and expressive stream reasoning

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