connecting stream reasoners on the web

Connecting Stream Reasoners on the Web

Jean-Paul Calbimonte

Institute of Information Systems

University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)

Stream Reasoning Workshop

Berlin, 08.12.2016

@jpcik

HES-SO University of AppliedSciences and Arts

Western Switzerland

6

Streams with Linked Data?

e.g. publish data streams as RDF/Linked Data?

URIs as names of things

HTTP URIs

useful information when URI is dereferenced

Link to other URIs

users

applicationsWEB

static vs. streams

one-off vs. continuous

7

RDF Streams before RDF Streams

http://richard.cyganiak.de/2007/10/lod/

2011

Linked Sensor Data

MetOfficeAEMET

8

RDF Streams before RDF Streamsi.e. just use RDF

:observation1 rdf:type om-owl:Observation .

:observation1 om-owl:observedProperty weather:_AirTemperature .

:observation1 om-owl:procedure :sensor1 .

:observation1 om-owl:result :obsresult1 .

:observation1 om-owl:resultTime "2015-01-01T10:00:01"

:obsresult1 om-owl:floatValue 35.4 .

Plain triples

Where is the

timestamp?

:observation2 rdf:type om-owl:Observation .

:observation2 om-owl:observedProperty weather:_AirTemperature .

:observation2 om-owl:procedure :sensor1 .

:observation2 om-owl:result :obsresult2 .

:observation2 om-owl:resultTime "2015-01-01T10:00:02"

:obsresult2 om-owl:floatValue 36.4 .

What is the order

in the RDF graph?

Appended to a file?

Or to some RDF dataset?

How to store it?

9

The RSP CommunityResearch work

Many Papers

PhD Thesis

Datasets

Prototypes

Benchmarks

RDF Streams

Stream Reasoning

Complex Event Processing

Stream Query Processing

Stream Compression

Semantic Sensor Web

Man

y t

op

ics

To

ns o

f w

ork

http://www.w3.org/community/rspW3C RSP Community Group

Effort to our work on RDF stream processing

discussstandardizecombineformalizeevangelize

10

W3C RSP Documentshttps://www.w3.org/community/rsp/

http://streamreasoning.github.io/RSP-QL/RSP_Requirements_Design_Document/

Use cases

Implementations

State of the Art

Challenges & issues

Requirements

Design principles

• RDF Stream model

• RDF Stream query language

Abstract syntax RDF Streams

11

Connecting RSPs on the Web

MorphStreams

CSPARQL

s

Etalis

TrOWL

s

s SPARQL

CQELS

s

Minimal agreements: standards, serialization, interfaces

Formal models for RSPs and reasoning

Working prototypes/systems! Reactive RSPs

12

Connecting RSPs and Stream Reasoners

Stream Data Model

Stream Query Languages

Producing Streams

Consuming Streams

13

RDF Stream Data Model

14

RSP Data Model

https://github.com/streamreasoning/RSP-QL/blob/master/Semantics.md

Timestamped Graph

:g1 {:axel :isIn :RedRoom. :darko :isIn :RedRoom}

{:g1 prov:generatedAtTime "2001-10-26T21:32:52"}

Many/One-triple graphs

Multiple time predicates

Implicit timestamp

Different timestamp representations

Contemporaneity

Allows:

A RDF stream S consists of a sequence of timestamped graphs (with a partial order)

RDF Stream

:g1 {:axel :isIn :RedRoom. :darko :isIn :RedRoom} {:g1,prov:generatedAtTime,t1}

:g2 {:axel :isIn :BlueRoom. } {:g2,prov:generatedAtTime,t2}

:g3 {:minh :isIn :RedRoom. } {:g3,prov:generatedAtTime,t3} ...

Substream, time-bounded substream, window, window function, …

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RDF Streams in W3C RSP

:g1 {:axel :isIn :RedRoom. :darko :isIn :RedRoom}

{:g1, prov:generatedAtTime, "2015-06-18T12:00:00Z"^^xsd:dateTime}

:g2 {:axel :isIn :BlueRoom. }

{:g2, prov:generatedAtTime, "2015-06-18T12:00:35"^^xsd:dateTime}

:g3 {:minh :isIn :RedRoom. }

{:g3, prov:generatedAtTime, "2015-06-18T12:02:07"^^xsd:dateTime}

...

https://www.w3.org/community/rsp/

http://streamreasoning.github.io/RSP-QL/RSP_Requirements_Design_Document/

Graph-based

Flexible time

property

RDF-friendly

Flexible

metadata

:g_1 :startsAt "2015-06-18T12:00:00"^^xsd:dateTime

:g_1 :endsAt "2015-06-18T13:00:00"^^xsd:dateTime

:g_2 :validBetween [:startsAt "2015-06-18T12:00:00"^^xsd:dateTime;

:endsAt "2015-06-18T13:00:00"^^xsd:dateTime]

Intervals

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Query Languages

17

Similar (not equals!) query languagesSELECT ?sensor

FROM NAMED STREAM <http://www.cwi.nl/SRBench/observations> [NOW-3 HOURS SLIDE 10 MINUTES]

WHERE {

?observation om-owl:procedure ?sensor ;

om-owl:observedProperty weather:WindSpeed ;

om-owl:result [ om-owl:floatValue ?value ] . }

GROUP BY ?sensor HAVING ( AVG(?value) >= "74"^^xsd:float )

SELECT ?sensor

WHERE {

STREAM <http://www.cwi.nl/SRBench/observations> [RANGE 10800s SLIDE 600s] {

?observation om-owl:procedure ?sensor ;

om-owl:observedProperty weather:WindSpeed ;

om-owl:result [ om-owl:floatValue ?value ] .} }

GROUP BY ?sensor HAVING ( AVG(?value) >= "74"^^xsd:float )

SELECT ?sensor

FROM STREAM <http://www.cwi.nl/SRBench/observations> [RANGE 1h STEP 10m]

WHERE {

?observation om-owl:procedure ?sensor ;

om-owl:observedProperty weather:WindSpeed ;

om-owl:result [ om-owl:floatValue ?value ] . }

GROUP BY ?sensor HAVING ( AVG(?value) >= "74"^^xsd:float )

SPARQLStream

CQELS

C-SPARQL

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RSP-QL

FROM NAMED WINDOW :win1 ON ex:social [RANGE PT10M]

FROM NAMED WINDOW :win2 ON ex:social [RANGE PT1M]

FROM NAMED WINDOW :win3 ON ex:sensors [RANGE PT1M]

SELECT ?room ?person

FROM NAMED WINDOW :win ON ex:social

WHERE {

WINDOW :win {?person :isIn ?room}

}

SELECT ?room

FROM NAMED WINDOW :win ON ex:social [RANGE PT10M SLIDE PT01M]

WHERE {

WINDOW :win { :axel :isIn ?room }

}

RSP-QL Semantics: a Unifying Query Model to Explain Heterogeneity

of RDF Stream Processing Systems. D. Dell’Aglio, E. Della Valle, J.-P.

Calbimonte, O. Corcho. International Journal on Semantic Web and

Information Systems IJSWIS, Volume 10(4). IGI Global, 2015.

Multiple windows

Extend SPARQL

Time based Windows

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RSEP-QL

SELECT ?x ?v

FROM NAMED :s1 WIN [RANGE 9] AS :w1

FROM NAMED :s2 WIN [RANGE 2] AS :w2

EVENT ON :w1 { ?x :p ?y. } AS E1

EVENT ON :w1 { ?y :q ?z. } AS E2

EVENT ON :w1 { ?z :r ?v. } AS E3

WHERE {

WINDOW :w2 { ?x :r ?v. }

MATCH { (E1 SEQ E2) SEQ E3 }

}

A Query Model to Capture Event Pattern Matching in RDF Stream Processing

Query Languages. [pdf]

D. Dell’Aglio, M. Dao-Tran, J.-P. Calbimonte, D. Le Phuoc and E. Della Valle. In Proc. of

the 20th International Conference on Knowledge Engineering and Knowledge

ManagementEKAW 2016. Bologna, Italy, November 2016

Combine time

windows and CEP

Sequencing events

as in CEP

20

Producing RDF Streams

21

Feed an RDF Stream to a RSP engine

Ad-hoc

Conversi

on to

RDF

Live Non-RDF Streams

RDF

RDF datasets

RSP

Add (internal)

timestamp

on insertion

What is currently done in most RSPs

Continuous

additions

RDF +

timestamps

22

R2RML Mappings

2

2

:ObsValueMap

rr:subjectMap [

rr:template "http://opensense.epfl.ch/data/ObsResult_NO2_{sensor}_{time}"];

rr:predicateObjectMap [

rr:predicate qu:numericalValue;

rr:objectMap [ rr:column "no2"; rr:datatype xsd:float; ]];

rr:predicateObjectMap [

rr:predicate obs:uom;

rr:objectMap [ rr:parentTriplesMap :UnitMap; ]].

:ObservationMap

rr:subjectMap [

rr:template "http://opensense.epfl.ch/data/Obs_NO2_{sensor}_{time}"];

rr:predicateObjectMap [

rr:predicate ssn:observedProperty;

rr:objectMap [ rr:constant opensense:NO2]];

URI of subject

URI of predicate

Object: colum name

Column names in a template

Can be used for mapping both databases, CSVs, JSON, etc

23

Feed an RDF Stream to a RSP engine

Conversio

n to

RDF

Live Non-RDF Streams

RDF

RDF datasets

RSP

Add (internal)

timestamp

on insertion

Adding mappings to the data flow

Continuous

additions

RDF +

timestamps

24

An example: TripleWave

Running modesSources

TripleWave: Spreading RDF Streams on the Web. [pdf]

A. Mauri, J.-P. Calbimonte, D. Dell’Aglio, M. Balduini, M. Brambilla, E. Della Valle and K. Aberer. In Proc.

of the International Semantic Web Conference ISWC 2016. Kobe, Japan, October 2016.

25

An example: TripleWave{"type":"Feature",

"properties":{

"time":1388620046000,

"url":"http://earthquake.usgs.gov/earthquakes/eventpage/ak10992887",

"mag":1.1,

"magType":"ml",

"type":"earthquake",

"title":"M 1.1 - 117km NW of Talkeetna, Alaska"},

"geometry":{

"type":"Point",

"coordinates":[-151.6458,63.102,14.1]},

"id":"ak10992887"

}

{

"http://www.w3.org/ns/prov#generatedAtTime": "2015-06-30T16:44:59.587Z",

"@id": "http://streamreasoning.org/TripleWave/ak10992887",

"@graph": [

{ "@id": "http://streamreasoning.org/TripleWavee/ak10992887",

"@type": "http://example.org/onto/earth#Earthquake",

"url": "http://earthquake.usgs.gov/earthquakes/eventpage/ak10992887",

"location": {"@id": "http://streamreasoning.org/TripleWave/ak10992887Location"},

"description": "M 1.1 - 117km NW of Talkeetna, Alaska" },

{ "@id": "http://streamreasoning.org/TripleWave/ak10992887Location",

"@type": "https://schema.org/Place",

"longitude": "-151.6458", "latitude": "63.102" } ],

"@context": "https://schema.org/"

}

Original JSON Web

Earthquake Stream

JSON-LD RDF

Stream

26

Data stream characteristics

2

6

Data regularity• Raw data typically collected as time series

• Very regular structure.

• Patterns can be exploited

E.g. mobile NO2 sensor readings

29-02-2016T16:41:24,47,369,46.52104,6.63579

29-02-2016T16:41:34,47,358,46.52344,6.63595

29-02-2016T16:41:44,47,354,46.52632,6.63634

29-02-2016T16:41:54,47,355,46.52684,6.63729

...

Data order• Order of data is crucial

• Time is the key attribute for establishing an order among the data items.

• Important for indexing

• Enables efficient time-based selection, filtering and windowing

Timestamp Sensor Observed

Value

Coordinates

27

Consuming RDF Streams

28

Discoverable RDF Datasets

e.g. VOID, DCAT

Vocabularies

Dataset catalogs

Distribution

Endpoints

Schemas/Shapes

29

An example: TripleWave

Running modesSources

TripleWave: Spreading RDF Streams on the Web. [pdf]

A. Mauri, J.-P. Calbimonte, D. Dell’Aglio, M. Balduini, M. Brambilla, E. Della Valle and K. Aberer. In Proc.

of the International Semantic Web Conference ISWC 2016. Kobe, Japan, October 2016.

30

Stream Metadata in TripleWave

{

"@context":

{ "sld": "http://streamreasoning.org/ontologies/SLD4TripleWave#",

"generatedAt": {

"@id": "http://www.w3.org/ns/prov#generatedAtTime",

"@type": "http://www.w3.org/2001/XMLSchema#dateTime" }

},

"@type": "sld:sGraph",

"sld:streamLocation": "ws://localhost:8101/TripleWave/replay",

"sld:tBoxLocation": {"@id":"http://purl.oclc.org/NET/ssnx/ssn"},

"sld:contains": {"@list": [

{ "generatedAt": "2016-04-21T13:01:18.663Z", "@id": "tr:1461243678663" },

{ "generatedAt": "2016-04-21T13:01:19.784Z", "@id": "tr:1461243679784" }

]},

"sld:lastUpdated": "2016-04-21T13:02:06.575Z"

}

RSP vocabulary

Endpoint

Schema

Stream elements

31

Things to do…

Stream Catalog Vocabularies

Endpoint protocol interactions

• HTTP, WebSocket, MQTT

Efficient serialization

Federated query processing

Concrete implementations

Large scale evaluation

Muchas gracias!

Jean-Paul CalbimonteUniversity of Applied Sciences and Arts Western Switzerland

HES-SO Valais-Wallis

@jpcik