streaming day - an overview of stream reasoning

Streaming Day: an overview of Stream Reasoning

by: Riccardo Tommasini

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Scuola di Ingegneria Industriale e dell’Informazione Computer Science and Engineering

Master Degree Thesis – Riccardo Tommasini

Agenda

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Background

Stream Reasoning

Get in Touch

Heaven✓

SR Example


GiT - Riccardo Tommasini

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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

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• 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

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Daniele Dell’Aglio

Phd

Emanuele Della Valle Advisor

Marco Balduini Phd


Agenda

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Background

Stream Reasoning

Get in Touch

Heaven✓

SR Example


Background - Semantic Web

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It provides a common framework to allow

interoperability applications.

The Semantic Web is a WWW extension.

Semantic Web world involves several

technologies.


Background - RDF

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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 objectpredicate

RDF describes a conceptual model of information in any given domain.


Background - OWL

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• 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

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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

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RICORDARE CAMBIO SEMANTICA!!!!Csparql language extends sparql in every 3 parts of query formsQuery form -> STREAM CLAUSE to create a RDF stream as query resultsDatasert clause -> FROM STREAM clause added to let engine get data from RDF streams specified by URIWhere Clause -> built in timestamp function to retrieve the timestamp of every single triple in the engine


Background - DSMS vs CEP

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QQQQ

Throw

Scratch

Store

StreamStream 1

Stream2

Stream n

…

Complex EventProcessing

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

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Background

Stream Reasoning

Get in Touch

Heaven✓

SR Example


Stream Reasoning (SR)

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Reasoning upon heterogeneous and rapidly changing information flows.

-- S. Ceri, E. Della Valle, F. van Harmelen and H. Stuckenschmidt, 2010


SR - RSP Engine

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RDF Stream Processing Engine


SR - RSP Engine

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heterogeneous data (unbounded) streams


SR - RSP Engine

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data streams integration through RDF data model



SR - RSP Engine

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


T


< ,Q>

SR - RSP Engine

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


continuous querying (Q) answering

T


SR - RSP Engine Execution Semantics

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S2ROperator

Window

R2ROperator

SPARQL

R2SOperator

Rstream,Itream,Dstream

RDF StreamRDF Stream

Engine StreamMappings Mappings



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S2ROperator

Window

R2ROperator

SPARQL

R2SOperator




Stream to Relation



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S2ROperator

Window

R2ROperator

SPARQL

R2SOperator




Stream to Relation

Relation to Relation



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S2ROperator

Window

R2ROperator

SPARQL

R2SOperator




Stream to Relation

Relation to Relation

Relation to Stream


SR - C-SPARQL Engine

Input TripleInferred Triple



RDF Stream

DSMS




RDF Stream

DSMS

active window




RDF Stream

DSMS Reasoner T,Q

active window




RDF Stream

DSMS Reasoner

RDF Stream

T,Q

active window




RDF Stream

DSMS Reasoner

RDF Stream

T,Q

C-SPARQL Query

active window




RDF Stream

DSMS Reasoner

RDF Stream

T,Q

Continuous Query

SPARQL Query

C-SPARQL Query

active window



Agenda

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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

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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 ?personFROM 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

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?x ?room ?person

:RedSensor :RedRoom :Alice

:Bob :RedRoom :Bob

Running Example - Query


Agenda

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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?

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Test Stand

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Evaluate engines with Test Stands

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In Aerospace engineering…

Experimental Environment

Reproducibility, Repeatability, ComparabilityEvaluation of running systems

Heaven - Test Stand


Heaven - Test Stand

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Disk

ResultCollector Streamer RSPEngine

Experiment

Analyser

Start MB StopTestStand

MB





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Test Stand






Method

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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?

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Causalità dei livelli, sarebbe bello poter dire sempre quale engine è migliore





Test Stand

Method

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Test Stand

Baselines

Method

Analysis

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Heaven - Dashboard Example

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Increasing Window

Dim

ension (ms)



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Memory(mb)

Latency(ms)

Increasing Window

Dim

ension (ms)



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Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Increasing Window

Dim

ension (ms)



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Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Increasing Window

Dim

ension (ms)



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Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Increasing Window

Dim

ension (ms)


Heaven - Pattern Identification Example

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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).

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Memory

Naive

cancellare graph lasciare naive


Heaven- Visual Comparison Example

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Agenda

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Semantic Web

Stream Reasoning

Get in Touch

Heaven

✓


Thank You

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Thank You!


Contact

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RiccardoTommasini+

@rictomm

tomma156

[email protected] Tommasini

riccardotommasini

https://github.com/streamreasoning


mailto:[email protected]


Resources

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Streamreasoning.org

StreamReasoning@GitHub

RDF Stream Processors

PhD CEP Course @Polimi

Stream Reasoning Tutorial

C-SPARQL Engine

Quick start availableSource code are released open source under Apache 2.0C-SPARQL Enginehttps://github.com/streamreasoning/CSPARQL-enginehttps://github.com/streamreasoning/CSPARQL-ReadyToGoPack

http://streamreasoning.org/download



http://www.streamreasoning.org/courses/scep2015

http://www.streamreasoning.org/events/sr4ld2014



Resources

37

Streamreasoning.org

StreamReasoning@GitHub

RDF Stream Processors

PhD CEP Course @Polimi

Stream Reasoning Tutorial

Esper

Jena

C-SPARQL Engine

Quick start availableSource code are released open source under Apache 2.0C-SPARQL Enginehttps://github.com/streamreasoning/CSPARQL-enginehttps://github.com/streamreasoning/CSPARQL-ReadyToGoPack




http://www.streamreasoning.org/courses/scep2015

http://www.streamreasoning.org/events/sr4ld2014
