streaming day - an overview of stream reasoning

Streaming Day: an overview of Stream Reasoning

by: Riccardo Tommasini

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

Master Degree Thesis – Riccardo Tommasini

Agenda

Background

Stream Reasoning

Get in Touch

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SR Example

GiT - Riccardo Tommasini

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

• 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

Daniele Dell’Aglio

Emanuele Della Valle Advisor

Marco Balduini Phd

Agenda

Background

Stream Reasoning

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SR Example

Background - Semantic Web

It provides a common framework to allow

interoperability applications.

The Semantic Web is a WWW extension.

Semantic Web world involves several

technologies.

Background - RDF

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

• 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

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

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

Scratch

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

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]

Tumbling Window

Sliding Window

Tumbling Window

Sliding Window

Tumbling Window

Sliding Window

Agenda

Background

Stream Reasoning

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SR Example

Stream Reasoning (SR)

Reasoning upon heterogeneous and rapidly changing information flows.

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

SR - RSP Engine

RDF Stream Processing Engine

SR - RSP Engine

heterogeneous data (unbounded) streams

SR - RSP Engine

data streams integration through RDF data model

SR - RSP Engine

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

SR - RSP Engine

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

continuous querying (Q) answering

SR - RSP Engine Execution Semantics

S2ROperator

Window

R2ROperator

SPARQL

R2SOperator

Rstream,Itream,Dstream

RDF StreamRDF Stream

Engine StreamMappings Mappings

S2ROperator

Window

R2ROperator

SPARQL

R2SOperator

Stream to Relation

S2ROperator

Window

R2ROperator

SPARQL

R2SOperator

Stream to Relation

Relation to Relation

S2ROperator

Window

R2ROperator

SPARQL

R2SOperator

Stream to Relation

Relation to Relation

Relation to Stream

SR - C-SPARQL Engine

Input TripleInferred Triple

RDF Stream

active window

RDF Stream

DSMS Reasoner T,Q

active window

RDF Stream

DSMS Reasoner

RDF Stream

active window

RDF Stream

DSMS Reasoner

RDF Stream

active window

RDF Stream

DSMS Reasoner

RDF Stream

active window

RDF Stream

DSMS Reasoner

RDF Stream

C-SPARQL Query

active window

RDF Stream

DSMS Reasoner

RDF Stream

Continuous Query

SPARQL Query

C-SPARQL Query

active window

RDF Stream

DSMS Reasoner

RDF Stream

Continuous Query

SPARQL Query

C-SPARQL Query

active window

RDF Stream

DSMS Reasoner

RDF Stream

Continuous Query

SPARQL Query

C-SPARQL Query

active window

Agenda

Background

Stream Reasoning

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SR Example

BlueRoom RedRoom

is with

Running Example

BlueRoom RedRoom

RedSensor

BlueSensor

is with

Running Example

BlueRoom RedRoom

RedSensor

BlueSensor

R RFID is with

Running Example

BlueRoom RedRoom

RedSensor

BlueSensor

R RFID is withFoursquare

Running Example

BlueRoom RedRoom

RedSensor

BlueSensor

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

Streaming Data Static Data

isWith

isConnectedTo

Running Example - Data Model

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

?x ?room ?person

:RedSensor :RedRoom :Alice

:Bob :RedRoom :Bob

Running Example - Query

Agenda

Background

Stream Reasoning

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SR Example

Heaven - Research Question

My contributions are

Can we enable Systematic Comparative Research Approach of RSP Engines, exploiting existing queries,

dataset and metrics?

Test Stand

Evaluate engines with Test Stands

In Aerospace engineering…

Experimental Environment

Reproducibility, Repeatability, ComparabilityEvaluation of running systems

Heaven - Test Stand

ResultCollector Streamer RSPEngine

Experiment

Analyser

Start MB StopTestStand

Test Stand

Method

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?

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

Test Stand

Method

Test Stand

Baselines

Method

Analysis

Heaven - Dashboard Example

Increasing Window

ension (ms)

Memory(mb)

Latency(ms)

Increasing Window

ension (ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Increasing Window

ension (ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Increasing Window

ension (ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Memory(mb)

Latency(ms)

Increasing Window

ension (ms)

Heaven - Pattern Identification Example

6.3 SOAK Test Evaluation Results

(a) Graph Naive

Triple Slots

in Number

Window 1 10 100 1000 10000

(b) Graph Incremental

Triple Slots

in Number

Window 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).

Memory

cancellare graph lasciare naive

Heaven- Visual Comparison Example

Agenda

Semantic Web

Stream Reasoning

Get in Touch

Heaven

Thank You

Thank You!

Contact

RiccardoTommasini+

@rictomm

tomma156

riccardo@knoesis.orgRiccardo Tommasini

riccardotommasini

Resources

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

Resources

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

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