on unified stream reasoning

Daniele Dell’Aglio

On Unified Stream Reasoning

Advisor: Prof. Emanuele Della Valle

Milano, 15/07/2016. Ph.D. Viva

Problem setting

Real time integration of dynamic data from heterogeneous

sources

– Traffic Prediction

Problem setting

sources

– Social media analytics

Problem setting

sources

– Social media analytics

– Personalised services

Information Flow Processing

In literature there are two different main approaches to

process streams

Data Stream Management Systems (DSMSs)

• Roots in DBMS research

• Aggregations, filters and joins

process streams

Complex Event Processors (CEPs)

• Roots in Distributed Systems (Publish/Subscribe)

• Search of relevant patterns in the stream

• Non-equi-join on the timestamps (after, before, etc.)

process streams

Complex Event Processors (CEPs)

• Roots in Distributed Systems (Publish/Subscribe)

• Search of relevant patterns in the stream

• Non-equi-join on the timestamps (after, before, etc.)

Current systems implements feature of both of them

• EPL (e.g. Esper, ORACLE CEP)

Stream Reasoning

Information Flow

Processing

Semantic Technologies

Stream Reasoning

Inference over streams of data

Real-time processing of

highly dynamic data

Integration of heterogeneous data and access through

query answering

The SR research so far

STARQL

DynamiTE

StreamRuleStarCITY

SPARKWAVE

StreamQR

The problem (1)

t3 6 91

{:alice :isIn :hall}

{:bob :isIn :hall}

{:alice :isIn :kitchen}

{:bob :isIn :kitchen}

e1 e2 e3 e4S

The problem (1)

Where are Alice and Bob,

when they are together?

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

The problem (1)

Let’s consider a tumbling

window W of size 5

Let’s execute the

experiment 4 times

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

The problem (1)

Execution 1° answer 2° answer

1 :hall [6] :kitchen [11]

4 - [7] - [12]

Let’s consider a tumbling

window W of size 5

Let’s execute the

experiment 4 times

t3 6 91

{:bob :isIn :hall}

Which is the correct answer?

e1 e2 e3 e4S

The problem (2)

4 - [7] - [12]

CSPARQL

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

The problem (2)

4 - [7] - [12]

2 No answers

4 No answers

CSPARQL CQELS

Which system behaves in the correct way?

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

Research Question 1

In the context of continuous query answering over RDF

streams, how can the behaviour of existing systems be

captured, compared and contrast when deductive reasoning

processes are not involved?

Research Question 1

In the context of continuous query answering over RDF

streams, how can the behaviour of existing systems be

captured, compared and contrast when deductive reasoning

processes are not involved?

Why do we need it?

• Comparison and contrast

• Study RDF Stream Processing related problems

• Standard RSP query language

Background data

Streams

Applications

SPARQL

RSEP-QL

A reference model that formally defines the semantics of

RDF Stream Processing engines

Background data

Streams

Applications

RSP-QL

SPARQL

RSEP-QL

Background data

Streams

Applications

RSP-QL BGP evaluation over background

BGP evaluation over streams

Model to express continous queries

SPARQL

RSEP-QL

Background data

Streams

RSEP-QL

Applications

RSP-QL BGP evaluation over background

BGP evaluation over streams

Event Pattern detection operators

Model to express continous queries

SPARQL

RSEP-QL

Q (E, DS, QF)

RSEP-QLFrom SPARQL to RSEP-QL

Query Interface

Q (E, DS, QF)

Data layer

RDF graphs

Query Interface

Q (E, DS, QF)

Evaluator

Data layer

RDF graphs

Query Interface

Q (E, DS, QF)

Evaluator

Data layer

Result Formatter

Ans(Q)RDF graphs

Query Interface

Q (E, DS, QF)

Evaluator

Data layer

Result Formatter

Ans(Q)RDF graphs

RDF graphsRDF streams

Query Interface

Q (E, SDS, QF)

Q (E, DS, QF)

Evaluator

Data layer

Result Formatter

Ans(Q)RDF graphs

QFContinuous EvaluatorET

Query Interface

Q (E, SDS, QF)

Q (E, SDS, ET, QF)

Ans(Q,t)

Q (E, DS, QF)

Evaluator

Data layer

Result Formatter

Ans(Q)RDF graphs

QFContinuous EvaluatorET

Query Interface

Q (E, SDS, QF)

Q (E, SDS, ET, QF)

Q (SE, SDS, ET, QF)

Ans(Q,t)

S4 S5 S6

RSEP-QL: DatasetTime-based Sliding Window: 𝕎(ω,β,t0)

Sequence of timestamped graphs (stream items)

𝕎(2,1,1)

S4 S5 S6

𝕎(2,1,1)

S4 S5 S6

𝕎(2,1,1)

S4 S5 S6

𝕎(2,1,1)

S4 S5 S6

𝕎(2,1,1)

widthslide

S4 S5 S6

𝕎(2,1,1)

widthslide

S4 S5 S6

𝕃(2)

S4 S5 S6

RSEP-QL: DatasetLandmark window: 𝕃(t0)

𝕃(2)

S4 S5 S6

𝕃(2)

S4 S5 S6

𝕃(2)

S4 S5 S6

RSEP-QL: Dataset From SPARQL to RSEP-QL dataset

R={RDF graph}

SPARQL dataset

R={RDF graph}

SPARQL dataset

S9 S10

R={RDF graph}

SPARQL dataset

S9 S10

𝕎(S)

T⊆ ℕ R={RDF graph}

SPARQL dataset

GInstantaneous GraphG(t1) RTime-Varying Graph

G: T R

S9 S10

𝕎(S)

SPARQL dataset

Instantaneous GraphG(t1) RTime-Varying Graph

G: T R

S9 S10

𝕎(S)

SPARQL dataset

Instantaneous GraphG(t1) RTime-Varying Graph

G: T R

RESP-QL dataset

S9 S10

𝕎(S)

RSEP-QL: Operators

Stream Processing operators (RSP-QL)

• SPARQL operators

• WINDOW to specify that the active element is a window (similar to

GRAPH)

• RStream, IStream, DStream to create the output stream

RSEP-QL: Operators

Stream Processing operators (RSP-QL)

• SPARQL operators

• WINDOW to specify that the active element is a window (similar to

GRAPH)

• RStream, IStream, DStream to create the output stream

Event Processing operators (RSEP-QL)

• EVENT w P (Basic Event Pattern)

• E1 SEQ E2

• FIRST E1, LAST E1

• MATCH to describe an event pattern

• ...

RSEP-QL: Evaluation SemanticsStream Processing Evaluation Semantics

The SPARQL evaluation function is defined as

⟦𝑃⟧𝐷𝑆(𝐺)

The RSEP-QL evaluation function extends the SPARQL one

by introducing the evaluation time instant

⟪𝑃⟫𝑆𝐷𝑆(𝐴)𝑡

The RSEP-QL evaluation function extends the SPARQL one

by introducing the evaluation time instant

⟪𝑃⟫𝑆𝐷𝑆(𝐴)𝑡

SPARQL operators are straight extended to the new

evaluation function

Example: JOIN

⟦𝐽𝑂𝐼𝑁(𝑃1, 𝑃2)⟧𝐷𝑆(𝐺) = ⟦𝑃1⟧𝐷𝑆 𝐺 ⨝⟦𝑃2⟧𝐷𝑆 𝐺⟪𝐽𝑂𝐼𝑁(𝑃1, 𝑃2)⟫𝑆𝐷𝑆 𝐴

𝑡 = ⟪𝑃1⟫𝑆𝐷𝑆 𝐴𝑡 ⨝⟪𝑃2⟫𝑆𝐷𝑆 𝐴

The main difference is on the BGP evaluation:

⟪𝐵𝐺𝑃⟫𝑆𝐷𝑆(𝐴)𝑡

=⟦𝐵𝐺𝑃⟧𝑆𝐷𝑆(𝐴,𝑡)

SDS(A,t) is:

SDS(G,t)= SDS(G(t)) if A is a time-varying graph G

SDS(A,t) is:

SDS(G,t)= SDS(G(t)) if A is a time-varying graph G

SDS(𝕎(S),t)=SDS(m(𝕎(S,t))) if A is from a sliding window 𝕎

SDS(𝕃(S),t)=SDS(m(𝕃(S,t))) if A is from a landmark window 𝕃

where m denotes a merge function

m(𝕎(S,t))= 𝑑𝑖,𝑡𝑖 ∈𝕎(S,t)

𝑑𝑖

• takes as input a window content i.e. a sequence of timestamped

RDF graphs

• produces an RDF graph

RSEP-QL: Evaluation SemanticsEvent Processing Evaluation semantics

A new evaluation function ⦅⋅⦆ 𝑜,𝑐𝑡

• t is the evaluation time instant (as in ⟪⋅⟫𝑆𝐷𝑆(𝐴)𝑡 ),

• 𝑜, 𝑐 is an additional window to identify the portion of the data

on which the event may happen

Event pattern evaluation produces event mappings 𝜇, 𝑡1, 𝑡2• 𝜇 is a solution mapping

• 𝑡1 and 𝑡2 denote the time inverval justifying 𝜇

Event pattern evaluation produces event mappings 𝜇, 𝑡1, 𝑡2• 𝜇 is a solution mapping

• 𝑡1 and 𝑡2 denote the time inverval justifying 𝜇

Selection and consumption policies to determine the data

involved in the evaluation

RSEP-QL: Evaluation SemanticsEvaluation semantics - Example

⦅FIRST EVENT 𝑤1 𝑃1 SEQ EVERY EVENT 𝑤2 𝑃2⦆ 9,16𝑡

S9 S10

FIRST EVENT 𝑤1 𝑃1

EVERY EVENT 𝑤2 𝑃2

t10 12 14 1611 13 15

S9 S10

t10 12 14 1611 13 15

S9 S10

t10 12 14 1611 13 15

S9 S10

t10 12 14 1611 13 15

S9 S10

S1 S10

t10 12 14 1611 13 15

S9 S10

S1 S10

t10 12 14 1611 13 15

S9 S10

S1 S10

t10 12 14 1611 13 15

S9 S10

S1 S10

S1 S12

t10 12 14 1611 13 15

RSEP-QL: Evaluation SemanticsMATCH graph pattern

Event patterns are enclosed in MATCH graph patterns

• Event mappings exist only in the context of event patterns

• The evaluation of a MATCH graph pattern produces a bag of solution

mappings

⟪𝑀𝐴𝑇𝐶𝐻 𝐸⟫𝑆𝐷𝑆 𝐴𝑡 = {𝜇| 𝜇, 𝑡1, 𝑡2 ∈ ⦅𝐸⦆ 0,𝑡

𝑡 }

It is possible to combine the MATCH graph pattern with

other SPARQL graph patterns

RSEP-QL: Evaluation SemanticsContinuous evaluation

For each evaluation time t ∈ ET: ⟪𝑆𝐸⟫𝑆𝐷𝑆(𝐴)𝑡

• The continuous evaluation is a sequence of instantaneous

evaluations

It is not always possible to compute ET a priori

evaluations

• Can be infinite

• Can be data dependent

evaluations

• Can be infinite

• Can be data dependent

ET is expressed through a Report Policy

• A set of conditions to one or more window operators in SDS

• Initially defined in SECRET for Stream Processing engines

Report Policy examples:

• P Periodic: the window reports only at regular intervals

• WC Window Close: the window reports if the active window

closes

• CC Content Change: the window reports if the content changes.

Research Question 2

Is it possible to add deductive reasoning features to RSEP-QL?

Research Question 2

Applications

SPARQL

Ontology

Research Question 2

Applications

SPARQL

Ontology

SPARQL Entailment

Regimes

Research Question 2

Streams OntologyBackground

RSEP-QL

Applications

RSP-QL

SPARQL

Ontology

SPARQL Entailment

Regimes

Research Question 2

Streams OntologyBackground

RSEP-QL Entailment

Regimes

RSEP-QL

Applications

RSP-QL

SPARQL

Ontology

SPARQL Entailment

Regimes

RSEP-QL: Reasoning

Stream Processing

At which point of the continuous query answering process

should the inference process be taken into account?

Event ProcessingWindow merge

Window operator

Stream S34 5

RDF Graph

RDF Stream

Window

RSEP-QL: Reasoning

Stream Processing

should the inference process be taken into account?Direct application

on SPARQLentailment regimes

Window operator

Stream S

Graph-level entailment

RDF Graph

RDF Stream

Window

RSEP-QL: Reasoning

Stream Processing

After applying the window operator

Direct application on SPARQL

entailment regimes

Window operator

Stream S

Window-level entailment

RDF Graph

RDF Stream

Window

RSEP-QL: Reasoning

Stream Processing

A larger, landmark window to include

relevant information from the past

After applying the window operator

Direct application on SPARQL

entailment regimes

Window operator

Stream S

Stream-level entailment

RDF Graph

RDF Stream

Window

RSEP-QL: ReasoningWindow-level and stream-level entailment regimes

How can DL ontology be extended to capture the window

content?

• Ontology stream – a sequence of time-stamped ABoxes 𝑆𝑜𝑐 𝑖

w.r.t a static TBox

content?

• Ontology stream – a sequence of time-stamped ABoxes 𝑆𝑜𝑐 𝑖

w.r.t a static TBox

• Introduction of so-far closure, as the closure of the i-th

snapshot w.r.t. the Tbox and the previus snapshots

• The so-far closure of 𝑆𝑜𝑐 𝑖 is done w.r.t. the TBox and the

snapshots 𝑆𝑜𝑐 𝑜 … 𝑆𝑜

𝑐 𝑖 − 1

• The so-far closure of the ontology stream is obtained by so-far

closing all the ABox snapshots

Event Processing

RSEP-QL: ReasoningResults

1. Theorem: in the context of

Stream Processing operations,

graph- and window-level

entailments have the same

behaviour as far as no

inconsistency is entailed

Stream Processing

Window merge

Window operator

Stream S

Graph-level ent.

Event Processing

RSEP-QL: ReasoningResults

1. Theorem: in the context of

Stream Processing operations,

graph- and window-level

entailments have the same

behaviour as far as no

inconsistency is entailed

2. Theorem: The stream-level

entailment brings to a larger

set of answers w.r.t. graph-

and window- entailment ones.

Stream Processing

Window operator

Stream S

Graph-level ent.

4 - [7] - [12]

RSEP-QL in actionCorrectness assessment

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

Window 1° answer 2° answer

t0=0 :hall [5] :kitchen [10]

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

t0=2 - [7] - [12]

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

t0=2 - [7] - [12]

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

2 No answers

4 No answers

CSPARQL CQELS

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

4 - [7] - [12]

2 No answers

4 No answers

CSPARQL CQELS

Window-close vs content-change

Empty relation notification (yes|no)

t3 6 91

{:bob :isIn :hall}

e1 e2 e3 e4S

CSR Bench

CSR Bench is an extension of the SRbench benchmark

that focuses on correctness

A test suite

• LinkedSensorData as dataset

• 8 parametric queries to tests the RSP engines in different

conditions

An oracle (an automatic correctness validator)

• Based on RSP-QL

CSR BenchExperimental Results

All the three systems that we

considered in our experiments showed

wrong behaviours

The defects we identified are related to:

•The window operator

• Initialization

• Slide parameter

• Window contents

•Timestamps of the stream items

• Internal timestamp management

stream

What’s next?

An RSEP-QL query language

• W3C RSP CG ongoing activities

What’s next?

Implementations

What’s next?

Implementations

• Yet another RSP engine

What’s next?

Implementations

• Stream Reasoner composition

• RSEP-QL as model to capture the behaviour of SRs

• RSEP-QL queries to define tasks to be executed over one or

more SR engines

What’s next?

Implementations

• Stream Reasoner composition

• RSEP-QL as model to capture the behaviour of SRs

• RSEP-QL queries to define tasks to be executed over one or

more SR engines

Stream-level entailment scalable techniques

• Initial work on stream without inference

• Permanent storage of portions of data (raw or inferred)

What’s next?

Reasoning over streams and continuous query answering

over streams are still two different worlds

• Key to show why RDF Stream Processing is useful w.r.t.

DSMS and CEP

• Few initial attempts to put them together

What’s next?

Reasoning over streams and continuous query answering

over streams are still two different worlds

• Key to show why RDF Stream Processing is useful w.r.t.

DSMS and CEP

• Few initial attempts to put them together

Streams in the Web are getting popular – applications want

more and more sophisticated features

• Different timestamps, out-of-orders

• Noise (inductive reasoning)

Conclusions

RSEP-QL captures the evaluation semantics of existing

RSP engines

Conclusions

RSP engines

RSEP-QL can determine which are the correct answers of

an RSP engine, given the input data and query

• At the basis of CSR Bench

Conclusions

RSP engines

RSEP-QL can determine which are the correct answers of

an RSP engine, given the input data and query

• At the basis of CSR Bench

The dynamics introduced in the continuous query evaluation

process have not been totally understood

• Not fully captured by existing models

• RSEP-QL captures those dynamics

Thank you! Questions?

On Unified Stream Reasoning

Advisor: Prof. Emanuele Della Valle

on unified stream reasoning

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