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Implementations Jean-Paul Calbimonte jean-paul.calbimonte@hevs.ch - - PowerPoint PPT Presentation

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Tutorial on RDF Stream Processing 2016 M.I. Ali, J-P Calbimonte, D. Dell'Aglio, E. Della Valle, and A. Mauri http://streamreasoning.org/events/rsp2016 RDF Stream Processing Implementations Jean-Paul Calbimonte jean-paul.calbimonte@hevs.ch

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SLIDE 1

Tutorial on RDF Stream Processing 2016

M.I. Ali, J-P Calbimonte, D. Dell'Aglio,

  • E. Della Valle, and A. Mauri

http://streamreasoning.org/events/rsp2016

RDF Stream Processing Implementations

Jean-Paul Calbimonte

jean-paul.calbimonte@hevs.ch http://jeanpi.org @jpcik

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http://streamreasoning.org/events/rsp2016

Share, Remix, Reuse — Legally

  • This work is licensed under the Creative Commons

Attribution 3.0 Unported License.

  • You are free:
  • to Share — to copy, distribute and transmit the work
  • to Remix — to adapt the work
  • Under the following conditions
  • Attribution — You must attribute the work by inserting

– “[source http://streamreasoning.org/rsp2014]” at the end of each reused slide – a credits slide stating

  • These slides are partially based on “RDF Stream Processing 2014”

by M. Balduini, J-P Calbimonte, O. Corcho, D. Dell'Aglio, E. Della Valle http://streamreasoning.org/rsp2014

  • To view a copy of this license, visit

http://creativecommons.org/licenses/by/3.0/

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http://streamreasoning.org/events/rsp2016

RSP for developers

  • RDF Streams in practice
  • RSP Query Engines
  • Developing with an RSP Engine
  • Handling Results
  • RSP Services

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http://streamreasoning.org/events/rsp2016

RDF Streams in Practice

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RSP: Keep the data moving

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Process data in-stream

Not required to store Active processing model

input streams

RSP

queries/ rules

  • utput streams/events

RDF Streams

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RDF Stream

… Gi Gi+1 Gi+2 … Gi+n …

unbounded sequence Gi {(s1,p1,o1), (s2,p2,o2),…} [ti]

1+ triples implicit/explicit timestamp/interval

RDF streams in theory

How do I code this? Use Web standards?

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Linked Data on the Web

Web of Data Linked Data W3C Standards: RDF, SPARQL, etc.

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Linked Data principles for RDF streams?

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

URIs as names of things HTTP URIs useful information when URI is dereferenced Link to other URIs

users application s

WEB

Use RDF model to continuously query real-time data streams?

static vs. streams

  • ne-off vs.

continuous

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http://streamreasoning.org/events/rsp2016

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(Sensor) Data Streams on the Web

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http://mesowest.utah.edu/ http://earthquake.usgs.gov/earthquakes/feed/v1.0/ http://swiss-experiment.ch

  • Monitoring
  • Alerts
  • Notifications
  • Hourly/daily updates
  • Myriad of Formats
  • Ad-hoc access points
  • Informal description
  • Convention-semantics
  • Uneven use of standards
  • Manual exploration
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RDF Streams before RDF Streams

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

2011

Linked Sensor Data

MetOffice AEMET

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Sensor Data & Linked Data

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Zip Files Number of Triples Example: Nevada dataset

  • 7.86GB in n-triples format
  • 248MB zipped

An example: Linked Sensor Data

http://wiki.knoesis.org/index.php/LinkedSensorData

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Sensor Data & Linked Data

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<http://knoesis.wright.edu/ssw/MeasureData_Precipitation_4UT01_2003_3_31_5_10_00> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://knoesis.wright.edu/ssw/ont/sensor-observation.owl#MeasureData> . <http://knoesis.wright.edu/ssw/MeasureData_Precipitation_4UT01_2003_3_31_5_10_00> <http://knoesis.wright.edu/ssw/ont/sensor-observation.owl#floatValue> "30.0"^^<http://www.w3.org/2001/XMLSchema#float> . <http://knoesis.wright.edu/ssw/MeasureData_Precipitation_4UT01_2003_3_31_5_10_00> <http://knoesis.wright.edu/ssw/ont/sensor-observation.owl#uom> <http://knoesis.wright.edu/ssw/ont/weather.owl#centimeters> . <http://knoesis.wright.edu/ssw/Observation_Precipitation_4UT01_2003_3_31_5_10_00> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://knoesis.wright.edu/ssw/ont/weather.owl#PrecipitationObservation> . <http://knoesis.wright.edu/ssw/Observation_Precipitation_4UT01_2003_3_31_5_10_00> <http://knoesis.wright.edu/ssw/ont/sensor-observation.owl#observedProperty> <http://knoesis.wright.edu/ssw/ont/weather.owl#_Precipitation> . <http://knoesis.wright.edu/ssw/Observation_Precipitation_4UT01_2003_3_31_5_10_00> <http://knoesis.wright.edu/ssw/ont/sensor-observation.owl#procedure> <http://knoesis.wright.edu/ssw/System_4UT01> . <http://knoesis.wright.edu/ssw/Observation_Precipitation_4UT01_2003_3_31_5_10_00> <http://knoesis.wright.edu/ssw/ont/sensor-observation.owl#samplingTime> <http://knoesis.wright.edu/ssw/Instant_2003_3_31_5_10_00> . <http://knoesis.wright.edu/ssw/Instant_2003_3_31_5_10_00> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2006/time#Instant> . <http://knoesis.wright.edu/ssw/Instant_2003_3_31_5_10_00> <http://www.w3.org/2006/time#inXSDDateTime> "2003-03-31T05:10:00-07:00^^http://www.w3.org/2001/XMLSchema#dateTime" .

What do we get in these datasets?

Nice triples

What is measured Measurement Unit Sensor When is it measured

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RDF Streams before RDF Streams

i.e. just use RDF

:observation1 rdf:type

  • m-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

  • m-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?

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Feed an RDF Stream to a RSP engine

Ad-hoc Conversion to RDF

Live Non-RDF Streams

RDF

RDF datasets

RSP

Add (internal) timestamp

  • n insertion

What is currently done in most RSPs

Continuous additions

RDF + timestamps

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Feed an RDF Stream to C-SPARQL

public class SensorsStreamer extends RdfStream implements Runnable { public void run() { .. while(true){ ... RdfQuadruple q=new RdfQuadruple(subject,predicate,object, System.currentTimeMillis()); this.put(q); } } } something to run on a thread timestamped triple the stream is “observable”

Data structure, execution and callbacks are mixed Observer pattern Tightly coupled listener

Added timestamp

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Actor Model

Actor 1 Actor 2 m

No shared mutable state Avoid blocking operators Lightweight objects Loose coupling

communicate through messages

mailbox state behavior

non-blocking response send: fire-forget Implementations: e.g. Akka for Java/Scala

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RDF Stream

  • bject DemoStreams {

... def streamTriples={ Iterator.from(1) map{i=> ... new Triple(subject,predicate,object) } }

Data structure

Infinite triple iterator

Execution

val f=Future(DemoStreams.streamTriples) f.map{a=>a.foreach{triple=> //do something }}

Asynchronou s iteration

Message passing

f.map{a=>a.foreach{triple=> someSink ! triple }}

send triple to actor

Immutable RDF stream  avoid shared mutable state  avoid concurrent writes  unbounded sequence

Ideas using akka actors

Futures  non blocking composition  concurrent computations  work with not-yet- computed results Actors  message-based  share-nothing async  distributable

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RDF Stream … other issues: Graph implementation? Timestamps: application vs system? Serialization?

 Loose coupling  Immutable data streams  Asynchronous message passing  Well defined input/output

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Data stream characteristics

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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

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Feed an RDF Stream to a RSP engine

Conversion to RDF

Live Non-RDF Streams

RDF

RDF datasets

RSP

Add (internal) timestamp

  • n insertion

Adding mappings to the data flow

Continuous additions

RDF + timestamps

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R2RML Mappings

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: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

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An example: TripleWave

Running modes Sources

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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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RSP Engine Implementations

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Existing RSP systems (oversimplified!)

  • C-SPARQL: RDF Store + Stream processor
  • Combined architecture
  • CQELS: Implemented from scratch. Focus on

performance

  • Native + adaptive joins for static-data and streaming

data

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RDF Store Stream processor

C-SPARQL query continuous results

Native RSP

CQELS query continuous results

translator

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Existing RSP systems (oversimplified!)

  • SPARQLstream: Ontology-based stream query answering
  • Virtual RDF views, using R2RML mappings
  • SPARQL stream queries over the original data streams.
  • EP-SPARQL: Complex-event detection
  • SEQ, EQUALS operators
  • Instans: RETE-based evaluation

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DSMS/CEP

SPARQLStream query continuous results

rewriter R2RML mappings Prolog engine

EP-SPARQL query continuous results

translator

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Classification of existing systems

Model Continuous execution Union, Join, Optional, Filter Aggregates Time window Triple window R2S operator Sequence, Co-ocurrence TA- SPARQL TA-RDF ✗ ✔ Limited ✗ ✗ ✗ ✗ tSPARQL tRDF ✗ ✔ ✗ ✗ ✗ ✗ ✗ Streaming SPARQL RDF Stream ✔ ✔ ✗ ✔ ✔ ✗ ✗ C-SPARQL RDF Stream ✔ ✔ ✔ ✔ ✔ Rstream

  • nly

time function CQELS RDF Stream ✔ ✔ ✔ ✔ ✔ Istream

  • nly

✗ SPARQLStr eam (Virtual) RDF Stream ✔ ✔ ✔ ✔ ✗ ✔ ✗ EP- SPARQL RDF Stream ✔ ✔ ✔ ✗ ✗ ✗ ✔ Instans RDF ✔ ✔ ✔ ✗ ✗ ✗ ✗

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Disclaimer: other features may be missing

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C-SPARQL

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A Reminder of SPARQL

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Where C-SPARQL Extends SPARQL

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C-SPARQL Language

Query and Stream Registration

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C-SPARQL Language

Query and Stream Registration

  • All C-SPARQL queries over RDF streams are continuous
  • Registered through the REGISTER statement
  • The output of queries is in the form of
  • Instantaneous tables of variable bindings
  • Instantaneous RDF graphs
  • RDF stream
  • Only queries in the CONSTRUCT form can be registered as

generators of RDF streams

  • Composability:
  • Query results registered as streams can feed other registered

queries just like every other RDF stream

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C-SPARQL Language

Query registration - Example

  • Using the social stream fb, Who is where?

REGISTER QUERY QWhoIsWhereOnFb AS PREFIX : <http://…/sr4ld2014-onto#> SELECT ?room ?person FROM STREAM <http://…/fb> [RANGE 1m STEP 10s] WHERE { ?person1 :posts [ :who ?person ; :where ?room ] . }

  • The resulting variable bindings has to be interpreted as an
  • instantaneous. It expires as soon as the query is

recomputed

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C-SPARQL Language

Stream registration - Example

  • Results of a C-SPARQL query can be stream out for down

stream queries

REGISTER STREAM SWhoIsWhereOnFb AS PREFIX : <http://…/sr4ld2014-onto#> CONSTRUCT { ?person :isIn ?room } FROM STREAM <http://…/fb> [RANGE 1m STEP 10s] WHERE { ?person1 :posts [ :who ?person ; :where ?room ] . }

  • The resulting RDF triples are streamed out on an RDF

stream

  • More details in the C-SPARQL Engine hands-on session

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C-SPARQL Language

Stream Registration - Notes

  • The output is constructed in the format of an RDF stream.
  • Every query execution may produce from a minimum of

zero triples to a maximum of an entire graph.

  • The timestamp is always dependent on the query

execution time only, and is not taken from the triples that match the patterns in the WHERE clause.

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C-SPARQL Language

FROM STREAM Clause

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C-SPARQL Language

FROM STREAM Clause

  • FROM STREAM clauses are similar to SPARQL datasets
  • They identify RDF stream data sources
  • They represent windows over a RDF stream
  • They define the RDF triples available for querying and

filtering.

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C-SPARQL Language

FROM STREAM Clause - windows

  • physical: a given number of triples
  • logical: a variable number of triples which occur during a

given time interval (e.g., 1 hour)

  • Sliding: they are progressively advanced of

a given STEP (e.g., 5 minutes)

  • Tumbling: they are advanced of exactly their time interval

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C-SPARQL Language

FROM STREAM Clause - Example

  • Using the social stream fb, how many people are in the

same room? Count on a window of 1 minute that slides every 10 seconds

REGISTER QUERY HowManyPoepleAreInTheSameRoom AS PREFIX : <http://…/sr4ld2014-onto#> SELECT ?room (COUNT(DISTINCT ?s) as ?person) FROM STREAM <http://…/fb> [RANGE 1m STEP 10s] WHERE { ?person1 :posts [ :who ?person ; :where ?room ] . } GROUP BY ?room

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C-SPARQL Language

C-SPARQL reports only snapshots

t t+10 t+20 t+30 t+40 t+50 t+60 t+70 t+80 d1 d2 d3

d1 d1 d1 d1 d1 d2 d2 d2 d2 d3 d3

Incoming timestamped RDF triples Time window [RANGE 40s STEP 10s] Window content

t+40

d1 d1, d2 d1, d2 d1, d2, d3 d2, d3

t+50 t+60 t+70 t+80

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C-SPARQL Language

Multiple FROM STREAM Clause - Example

  • Using the social stream fb and fs, how many people are

in the same room? Count on a window of 1 minute that slides every 10 seconds

REGISTER QUERY HowManyPoepleAreInTheSameRoom AS PREFIX : <http://…/rsp2014-onto#> SELECT ?room (COUNT(DISTINCT ?s) as ?person) FROM STREAM <http://…/fb> [RANGE 1m STEP 10s] FROM STREAM <http://…/fs> [RANGE 1m STEP 10s] WHERE { ?person1 :posts [ :who ?person ; :where ?room ] . } GROUP BY ?room

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C-SPARQL Language

Query Chaining

  • A C-SPARQL query Q1 registered using the STREAM clause

streams results on an RDF stream

  • A down stream C-SPARQL query Q2 can open a window on

the RDF stream of Q1 using the FROM STREAM clause

  • E.g.,

43 Is in on 4 query

4

Stream

f

Stream Is with on f query Is In across

f and 4

query Stream Stream

:Bob :posts [ :who :Bob ; :where :BlueRoom ] . :Carl :posts [ :who :Carl , :Bob ] . :Bob :isIn :BlueRoom . :Carl :isWith :Bob . :Carl :isIn :BlueRoom .

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C-SPARQL Language

TimeStamp Function

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C-SPARQL Language

TimeStamp Function – Syntax and Semantics

  • The timestamp of a triple can be bound to a variable using

a timestamp() function

  • Syntax
  • timestamp(variable|IRI|bn, variable|IRI, variable|IRI|bn|literal)
  • Semantics

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Triple Result of evalutaion It is not in the window Type Error It appears once in the window Timestamp of triple It appears multiple times in the window The timestamp of the most recent triple

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C-SPARQL Language

TimeStamp Function - Example

  • Who is “following” whom?

REGISTER QUERY FindFollowers AS PREFIX f: <http://larkc.eu/csparql/sparql/jena/ext#> PREFIX : <http://…/sr4ld2014-onto#> SELECT ?someOne ?someOneElse ?room FROM STREAM <http://…/isIn> [RANGE 1m STEP 10s] WHERE { ?someOne :isIn ?room . ?someOneElse :isIn ?room . FILTER(?someOne!=?someOneElse ) FILTER (f:timestamp(?someOne :isIn ?room) < f:timestamp(?someOneElse :isIn ?room) }

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C-SPARQL Language

Accessing background Information

  • C-SPARQL allows for asking the engine to issue the query

also against RDF graphs using the FROM clauses.

  • E.g., Where else can Alice go?

REGISTER QUERY WhereElseCanAliceGo AS PREFIX : <http://…/sr4ld2014-onto#> SELECT ?room FROM STREAM <http://…/isIn> [RANGE 10m STEP 10m] FROM <http://…/bgInfo> WHERE { ?:Alice :isIn ?someRoom . ?someRoom :isConnectedTo ?room . }

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IRI identifying the graph containing the background information

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C-SPARQL Language

C-SPARQL queries and reasoning - example

  • Memo
  • posts is a sub property of observes
  • Data
  • Query under RDFS entailment regime

REGISTER QUERY QueryUnderRDFSEntailmentRegime AS PREFIX : <http://…/sr4ld2014-onto#> SELECT ?x ?room ?person FROM 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

48 RDF graph Time-stamp Stream :RedSensor :observes [ :who :Alice; :where :RedRoom ] . t1 sensors :Bob :posts [ :who :Bob ; :where :RedRoom] . t2 fs ?x ?room ?person :RedSensor :RedRoom :Alice :Bob :RedRoom :Bob

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Introduction

C-SPARQL Engine Architecture

  • Simple, modular

architecture

  • It relies entirely on

existing technologies

  • Integration of
  • DSMSs (Esper) and
  • SPARQL engines (Jena-

ARQ)

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Introduction

C-SPARQL Engine Features at a glance 1/3

  • In-memory RDF stream Processing
  • Continuous queries, filtering, aggregations, joins, sub-queries

via C-SPARQL

  • Push based
  • Reactive
  • C-SPARQL Engine 0.9.5 supports
  • SPARQL 1.1 (tested with http://www.w3.org/wiki/SRBench)
  • query chaining
  • background RDF graph access and update (via SPARQL 1.1

Update)

  • naïve stream reasoning (via Jena Generic Rule Reasoner)
  • time aware matching via timestamp function
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Introduction

C-SPARQL Engine Features at a glance 2/3

  • Extensible Middleware
  • Runtime management of

– RDF streams – C-SPARQL query – Result listerners

  • API driven
  • Quick start available
  • C-SPARQL Engine

– http://streamreasoning.org/download/csparqlreadytogopack

  • Source code are released open source under Apache 2.0
  • C-SPARQL Engine

– https://github.com/streamreasoning/CSPARQL-engine – https://github.com/streamreasoning/CSPARQL-ReadyToGoPack

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Introduction

C-SPARQL Engine Features at a glance 3/3

  • Known limitations
  • large background data and timestamp function can spoil

performance

  • no support for named graphs and named streams
  • no support for multiple windows on the same stream
  • triple based windows are buggy

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Introduction

C-SPARQL Engine as general RSP

  • RSP-services proposes a unified interface for the RDF

stream processors and offers Rest services to interact with them.

  • RSP-services-csparql represents the specific

implementation of the RSP-services for the C-SPARQL engine (more detailed information in the hands-on session)

  • Quick start available
  • RDF Stream Processging RESTful Interface (RSP-service) for

C-SPARQL Engine

– http://streamreasoning.org/download/rsp-service4csparql

  • Source code are released open source under Apache 2.0
  • RSP-services

– https://github.com/streamreasoning/rsp-services-csparql – https://github.com/streamreasoning/rsp-services-api – https://github.com/streamreasoning/rsp-services-client-example

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Resources

  • Read out more
  • C-SPARQL semantics

– Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: C-SPARQL: a Continuous Query Language for RDF Data Streams. Int. J. Semantic Computing 4(1): 3-25 (2010)

  • Most recent syntax

–

  • D. F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus,

Querying RDF streams with C-SPARQL, SIGMOD Record 39 (1) (2010) 20–26.

  • RSP-services

– M Balduini,E Della Valle: A Restful Interface for RDF Stream

  • Processors. International Semantic Web Conference (Posters &

Demos) 2013: 209-212

  • Downloads
  • http://streamreasoning.org/download/csparqlreadytogopack
  • http://streamreasoning.org/download/rsp-service4csparql
  • Contact points
  • marco.balduini@polimi.it
  • emanuele.dellavalle@polimi.it

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SPARQL Stream & Morph-streams

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Morph-streams: Overview

56

Query rewriting Query Processing Client

SPARQLStream [tuples] [triples/bin dings] Algebra expression

R2RML Mappings

Morph-streams procesing SPARQLStream queries

SELECT ?proximity FROM STREAM

<http://streamreasoning.org/SensorReadings.srdf> [NOW–5 S]

WHERE { ?obs a ssn:ObservationValue; qudt:numericalValue ?proximity; FILTER (?proximity>10) } SELECT prox FROM sens.win:time(5 sec) WHERE prox >10

π timed,prox ω σprox>10

5 Seconds

sens

Data translation SNEE Esper GSN Cosm

pull/push

https://github.com/jpcik/morph-streams

Other

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SPARQLStream Language

57

FROM NAMED STREAM ISTREAM DSTREAM RSTREAM WINDOW Underlying data source restrictions

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SPARQLStream Language

  • NamedStream

 ‘FROM’ [‘NAMED’] ‘STREAM’ StreamIRI ‘[’ Window ‘]’

  • Window

 ‘NOW-’ Integer TimeUnit [UpperBound] [Slide]

  • UpperBound

 ‘TO NOW-’ Integer TimeUnit

  • Slide

 ‘SLIDE’ Integer TimeUnit

  • TimeUnit

 ‘MS’ | ‘S’ | ‘MINUTES’| ‘HOURS’ | ‘DAY’

  • Select

 ‘SELECT’ [Xstream] [Distinct | Reduced] …

  • Xstream

 ‘RSTREAM’ | ‘ISTREAM’ | ‘DSTREAM’

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SELECT ISTREAM ?room FROM NAMED STREAM <http://www.streamreasoning.org/streams/socialsensor.srdf> [NOW-10 S] WHERE {…

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SPARQLStream: examples

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PREFIX sr4ld: <http://www.streamreasoning.org/ontologies/socialsensor,owl#> SELECT ?room FROM NAMED STREAM <http://www.streamreasoning.org/streams/socialsensor.srdf> [NOW-10 S] WHERE { ?obs sr4ld:observedBy ?sensor. ?obs sr4ld:where ?room. }

SPARQLStream

All rooms where something was observed in the last 10s

PREFIX sr4ld: <http://www.streamreasoning.org/ontologies/socialsensor,owl#> SELECT (COUNT(?person) AS ?nmb) ?room FROM NAMED STREAM <http://www.streamreasoning.org/streams/socialsensor.srdf> [NOW-10 S] WHERE { ?obs sr4ld:who ?pers. ?obs sr4ld:where ?room. } GROUP BY ?room

Number of persons observed in each room in the last 10s

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Underlying Query Processors

Esper

  • CEP/DSMS
  • EPL language

SNEE

  • DSMS/Sensor Network Query Evaluator
  • Compile queries to sensor code

GSN

  • Sensor middleware
  • REST API

Cosm/Xively

  • Sensor middleware
  • Open platform
  • REST API

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SELECT prox FROM sensors [FROM NOW-5 MINUTES TO NOW] WHERE prox >10 SELECT prox FROM sensors.win:time(5 minute) WHERE prox >10 http://montblanc.slf.ch:22001/multidata?vs[0]=sens

  • rs&

field[0]=proximity_field&c_min[0]=10& from=15/05/2012+05:00:00&to=15/05/2012+10:00: 00 http://api.cosm.com/v2/feeds/14321/datastreams/ 4?start=2012-05-15T05:00:00Z&end=2012-05- 15T10:00:00Z

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Morph-streams: Overview

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Query rewriting Query Processing Client

SPARQLStream [tuples] [triples/bin dings] Algebra expression

R2RML Mappings

Morph-streams procesing SPARQLStream queries

SELECT ?proximity FROM STREAM

<http://streamreasoning.org/SensorReadings.srdf> [NOW–5

S] WHERE { ?obs a ssn:ObservationValue; qudt:numericalValue ?proximity; FILTER (?proximity>10) } SELECT prox FROM sens.win:time(5 sec) WHERE prox >10

π timed,prox ω σprox>10

5 Seconds

sens

Data translation SNEE Esper GSN Cosm

pull/push

https://github.com/jpcik/morph-streams

Other

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3rd: Mapping the two models

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Observation Sensor Person Room where who

  • bserves

subClassOf

(person, room,…) detections Define mappings

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R2RML – There is a recommendation!

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We can use the W3C recommendation

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R2RML - Overview

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Encoding in R2RML

65

:triplesMap a rr:TriplesMap; rr:logicalTable [ rr:tableName ”sensors"; ] rr:subjectMap [ rr:template "http://streamreasoning.org/data/Observation/{person}{timed}"; rr:class sr4ld:Observation; rr:graph sr4ld:socialstream.srdf ]; rr:predicateObjectMap [ rr:predicate sr4ld:who ; rr:objectMap [ rr:template “http://streamreasoning.org/data/Person/{person}” ]];.

the stream name

subject URI triple predicate + object Mapping definition

stream attributes

the object (a URI in this case)

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Underlying Query Processors

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SELECT ?proximity FROM STREAM <http://streamreasoning.org/SensorReadings.srdf> [NOW–5 S] WHERE { ?obs a ssn:ObservationValue; qudt:numericalValue ?proximity; FILTER (?proximity>10) } SELECT prox FROM sensors [FROM NOW-5 MINUTES TO NOW] WHERE prox >10 timed, prox

π ω σprox>10

5 Seconds

sensors

SELECT prox FROM sensors.win:time(5 minute) WHERE prox >10 http://montblanc.slf.ch:22001/multidata?vs[0]=sensors&field[0]=proximi ty_field&c_min[0]=10& from=15/05/2012+05:00:00&to=15/05/2012+10:00:00 http://api.cosm.com/v2/feeds/14321/datastreams/4?start=2012-05- 15T05:00:00Z&end=2012-05-15T10:00:00Z

Query rewriting

R2RML

SNEE (DSMS) Esper (CEP) GSN (middlwr) Cosm Xively SPARQLStream

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Underlying query processors

Features Esper SNEE GSN Cosm/Xively Projection ✔ ✔ ✔ Fixed Proj expression ✔ ✔ ✖ ✖ Joins ✔ ✔✖ only window ✖ ✖ Union ✖ ✔✖ not windows ✔ ✖ Selection ✔ ✔ ✔ ✖✔ limited Aggregates ✔ ✔ ✔✖ ✖ Time window ✔ ✔ ✔ ✔ Tuple window ✔ ✔ ✔ ✖ R2S ✔ ✔ ✖ ✖ Conjunction, Disj ✔ ✖ ✖ ✖ Repetition pattern ✔ ✖ ✖ ✖ Sequence ✔ ✖ ✖ ✖

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Morph-streams: With reasoning!

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Query rewriting Query Processing Client

SPARQLStream [tuples] [triples/bin dings] Algebra expression

R2RML Mappings

Morph-streams procesing SPARQLStream queries

Data translation SNEE Esper GSN Cosm

pull/push

https://github.com/jpcik/morph-streams

Other Ontology TBox

Rewrite taking into account the ontology TBox

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Reasoning with Morph-streams

  • Rewriting the SPARQLStream queries:

69

SELECT ?x FROM NAMED STREAM <http://linkeddata.es/s/obs.srdf> [NOW - 5 MINUTES] WHERE { ?x ssn:observedBy ?y } SELECT ?x FROM NAMED STREAM <http://linkeddata.es/s/obs.srdf> [NOW - 5 MINUTES] WHERE { {?x ssn:observedBy ?y} UNION {?x a ssn:Observation} UNION {?x a aws:TemperatureObservation} UNION {?x a aws:HumidityObservation} }

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Morph-streams: With reasoning!

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Query rewriting Query Processing Client

SPARQLStream [tuples] [triples/bin dings] Algebra expression

R2RML Mappings Data translation SNEE Esper GSN Cosm

pull/push

https://github.com/jpcik/morph-streams

Other Ontology TBox

Rewrite only happens once

Query rewriting + translation

Then continuous query is registered

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Now some code

Morph-streams:

  • Coded in Scala
  • JAR bundle, use it from Scala or Java code
  • Maven, Sbt
  • Examples
  • One off query
  • Register continuous query
  • Pull data
  • Push
  • Basic REST
  • https://github.com/jpcik/morph-streams
  • https://github.com/jpcik/morph-web

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Code examples

  • Parse SPARQLStream

val query= “PREFIX sr4ld: <…>. SELECT ?a …” val syntax= StreamQueryFactory.create(query);

  • Execute One-off query

val query= “PREFIX sr4ld: <…>. SELECT ?a …” mapping=Mapping(new URI(mappings/social.ttl)) val adapter:QueryEvaluator=Application.adapter(system) val results= adapter.executeQuery(query,mapping) 72

Mapping Bindings

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Code examples

  • Register and Pull

val queryid= adapter.registerQuery(query,mapping) val results1=adapter.pull(queryid) val results2=adapter.pull(queryid)

  • Register and Push

class ExampleReceiver extends StreamReceiver{

  • verride def receiveData(s:SparqlResults):Unit=

Logger.debug("got: "+res) } val receiver=new ExampleReceiver val queryid= adapter.listenToQuery(query,mapping,receiver) 73

Query identifier Implement receiver

For Java users: Exactly the same interface!

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Querying RSPs in Practice

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ExecContext context=new ExecContext(HOME, false); String queryString =" SELECT ?person ?loc … ContinuousSelect selQuery=context.registerSelect(queryString); selQuery.register(new ContinuousListener() { public void update(Mapping mapping){ String result=""; for(Iterator<Var> vars=mapping.vars();vars.hasNext();) result+=" "+ context.engine().decode(mapping.get(vars.next())); System.out.println(result); } });

RSP Querying Example with CQELS (code.google.com/p/cqels)

CQELS continuous query:

get result updates adding listener register query

SELECT ?person ?loc WHERE { STREAM <http://deri.org/streams/rfid> [RANGE 3s] {?person :detectedAt ?loc} }

Tightly coupled listeners Results delivery: push & pull?

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CQELS fed by a TripleWave WebSocket

val conf = ConfigFactory.load.getConfig("experiments.rsp") val qq="""CONSTRUCT { ?s ?p ?o } WHERE { STREAM <ws://localhost:4040/primus> [RANGE 0ms] {?s ?p ?o} }""" val sys=new RspSystem("wstreams") val cqels=new CqelsEngine sys.startStream(Props( new WebSocketStream(cqels,"ws://localhost:4040/primus",conf))) cqels.registerQuery(qq, cqels.createListener(lissy)) def lissy(triples:TripleList):Unit={ println("tikki: "+triples) }

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Similar models, similar (not equals!) query languages

77

SELECT ?sensor FROM NAMED STREAM <http://www.cwi.nl/SRBench/observations> [NOW-3 HOURS SLIDE 10 MINUTES] WHERE { ?observation om-owl:procedure ?sensor ;

  • m-owl:observedProperty weather:WindSpeed ;
  • m-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 ;

  • m-owl:observedProperty weather:WindSpeed ;
  • m-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 ;

  • m-owl:observedProperty weather:WindSpeed ;
  • m-owl:result [ om-owl:floatValue ?value ] . }

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

SPARQLStream CQELS C-SPARQL

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Query using SQL on Streams

Model Continuous execution Union, Join, Optional, Filter Aggregates Time window Triple window R2S operator Sequence, Co-

  • currence

Time function TA-SPARQL TA-RDF ✗ ✔ Limite d ✗ ✗ ✗ ✗ ✗ tSPARQL tRDF ✗ ✔ ✗ ✗ ✗ ✗ ✗ ✗ Streaming SPARQL RDF Stream ✔ ✔ ✗ ✔ ✔ ✗ ✗ ✗ C-SPARQL RDF Stream ✔ ✔ ✔ ✔ ✔ ✗ ✗ ✔ CQELS RDF Stream ✔ ✔ ✔ ✔ ✔ ✗ ✗ ✗ SPARQLStrea m (Virtual) RDF Stream ✔ ✔ ✔ ✔ ✗ ✔ ✗ ✗ EP-SPARQL RDF Stream ✔ ✔ ✔ ✗ ✗ ✗ ✔ ✗ Instans RDF ✔ ✔ ✔ ✗ ✗ ✗ ✗ ✗

W3C RSP  review features in existing systems  agree on fundamental

  • perators

 discuss on possible semantics

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

RSP is not always/only SPARQL- like querying SPARQL protocol is not enough RSP RESTful interfaces? Powerful languages for continuous query processing

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W3C RSP-CG: RSP-QL

PREFIX e: <http://somevocabulary.org/> PREFIX s: <http://someinvasivesensornetwork.org/streams#> PREFIX g: <http://somesocialnetwork.org/graphs#> PREFIX : <http://acrasycompany.org/rsp> REGISTER STREAM :GallehaultWasTheBar UNDER ENTAILMENT REGIME <http://www.w3.org/ns/entailment/RIF> AS CONSTRUCT ISTREAM { ?poi rdf:type :Gallehault ; :count ?howmanycouples ; :for (?somebody ?someoneelse) } FROM NAMED WINDOW :veryLongWindow ON s:1 [RANGE PT4H STEP PT1H] FROM NAMED WINDOW :longWindow ON s:1 [FROM NOW-PT35M TO NOW-PT5M STEP PT5M] FROM NAMED WINDOW :shortWindow ON s:1 [RANGE PT10M STEP PT5M] FROM NAMED GRAPH g:SocialGraph FROM GRAPH g:POIs WHERE { ?poi rdf:type e:bar . WINDOW :veryLongWindow { {?somebody e:enters ?poi} BEGIN AT ?t3 {?someoneelse e:enters ?poi} BEGIN AT ?t4 FILTER(?t3>?t4) } WINDOW :longWindow { { ?somebody e:isCloseTo ?someoneelse MINUS { ?somebody e:isCloseTo ?yetanotherone . FILTER (?yetanotherone != ?someoneelse) } } WITH DURATION ?duration FILTER (?duration>="PT30M"^^xsd:duration) } WINDOW :shortWindow { { ?somebody e:exits ?bar} BEGIN AT ?t1 { ?someoneelse e:exits ?bar } BEGIN AT ?t2 FILTER (abs(?t2-?t1)<"PT1M"^^xsd:duration ) } GRAPH g:SocialGraph { FILTER NOT EXIST { ?somebody e:knows ?someoneelse } } FILTER (?somebody != ?someoneelse) } AGGREGATE { GROUP BY ?poi COUNT(?somebody) AS ?howmanycouples }

Continuously look for bars where people are falling in love (because of a book ) Register stream Time windows Sequencing Duration Stored Graphs Aggregates Access to time Reasoning

They entered the same bar They are close to each other, with no-one else They get out together Didn’t know each other

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RSP Communication

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RDF Stream Processing

RSP Engine

RDF graphs

input RDF streams streams of results

background knowledge

continuous queries

stream producers RSP Engine

producer

subscribe notify

  • cont. query

consumer

push results subscribe

stream consumers

continuous queries

RSP Implementations

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Reactive Systems

Event-Driven

Jonas Boner. Go Reactive: Event-Driven, Scalable, Resilient & Responsive

  • Systems. 2013.

Events: react to

Scalable

Load:

Resilient

Failure:

Responsive

Users:

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Actor Model

Actor 1 Actor 2 m

No shared mutable state Avoid blocking operators Lightweight objects Loose coupling

communicate through messages

mailbox state behavior

non-blocking response send: fire-forget Implementations: e.g. Akka for Java/Sca

Parent

Actor 1 Supervisi

  • n

hierarchy

Supervision

Actor 2 Acto r4

X

Actor 2 Actor 1 Actor

2

m

Actor 3 Actor 4

m m Remoting

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RDF Streams: Actors

val sys=ActorSystem.create("system") val consumer=sys.actorOf(Props[RdfConsumer]) class Streamer extends StreamRDF{

  • verride def triple(triple:Triple){

consumer ! triple } } class RdfConsumer extends Actor{ def receive= { case t:Triple => if (t.predicateMatches(RDF.‘type‘)) println(s"received triple $t") }

RDF consumer Actor receive method Implements behavior Message-passing model RDF producer Async message passing

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RSP Producer & Consumer

Proce ssor

faster producers >> slower processor/consumer

Prod ucer Prod ucer Prod ucer Prod ucer

RDF streams

Cons umer Cons umer Cons umer

unresponsive

  • verload

Overload of the processor/receiver Unresponsiveness in stream processor

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Dynamic Push-Pull

Producer Consumer

m

data flow demand flow

Push when consumer is faster Pull when producer is faster Dynamically switch modes Communication is dynamic depending on demand vs supply

Producer Consumer

m m m m m m m m m m

push

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Evaluation: throughput

Basic dynamic pull push On top of CQELS Limitations of Thread model Not yet fully async

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Reactive RSP workflows

Morph Streams CSPARQL

s Etali s TrOWL s s CQELS Dyn amit e s

Minimal agreements: standards, serialization, interfaces Formal models for RSPs and reasoning Working prototypes/systems!

Event-driven message passing Async communication Immutable streams Transparent Remoting Parallel and distributed Supervised Failure Handling Responsive processing

Reactive RSPs

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RSPs and the Linked Data Principles

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URIs as Names of Things

http://mysensorweb.me/mytemperature/20151110Z10:00: 00 Different concepts http://mysensorweb.me/mytemperature/latest http://mysensorweb.me/mytemperature/lastMinute http://mysensorweb.me/mytemperature/lastMonth Different granularities Different uses http://mysensorweb.me/mytemperature/avgLastMonth http://mysensorweb.me/mytemperature

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HTTP URIs

http://mysensorweb.me/mytemperature/latest Internet of Things

How about XMPP, CoAP, MQTT? Websockets?

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De-referenceable URIs

GET http://mysensorweb.me/mytemperature/latest

:Obs1 a my:TemperatureObservation; my:hasValue 33.5 ; my:hasUnit u:Celsius; my:atTime “20151110Z10:00:00”.

GET http://mysensorweb.me/mytemperature

Get the whole stream?

GET http://mysensorweb.me/mytemperature/lastMonth

Get continuous updates?

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Link to other URIs

  • Broken links?
  • Mix streaming and stored data
  • Persist or not persist?
  • Volatile links?

http://mysensorweb.me/mytemperature/20151110Z10:00:00

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For Java/Scala developers

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Semantic Web Devs

Have fun Love challenges Need cool tools

RDF4J

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Scala: Functions and Objects

JVM language Both object and functional oriented Easy Java-interop Reuse Java libraries Growing community

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RDF in Jena: in Scala

String personURI = "http://somewhere/JohnSmith"; Model model = ModelFactory.createDefaultModel(); model.createResource(personURI).addProperty(VCARD.FN,"John Smith");

Type inference Not too useful ; and ()

Terser & compact code Type-safe DSL Compiler takes care

val personURI = "http://somewhere/JohnSmith" val model = ModelFactory.createDefaultModel model.createResource(personURI).addProperty(VCARD.FN,"John Smith")

sw:John Smith “John Smith”

vcard:FN

val personURI = "http://somewhere/JohnSmith" implicit val model = createDefaultModel add(personURI,VCARD.FN->"John Smith")

boilerplate String converted to Resource

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Some more RDF

99

String personURI = "http://somewhere/JohnSmith"; String givenName = "John"; String familyName = "Smith"; String fullName = givenName + " " + familyName; Model model = ModelFactory.createDefaultModel(); model.createResource(personURI) .addProperty(VCARD.FN,fullName) .addProperty(VCARD.N,model.createResource() .addProperty(VCARD.Given,givenName) .addProperty(VCARD.Family,familyName)); val personURI = "http://somewhere/JohnSmith" val givenName = "John" val familyName = "Smith" val fullName = s"$givenName $familyName" implicit val model = createDefaultModel add(personURI,VCARD.FN->fullName, VCARD.N ->add(bnode,VCARD.Given -> givenName, VCARD.Family->familyName))

sw:John Smith

“John Smith”

vcard:FN

_:n “John” “Smith”

vcard:N vcard:Giv en vcard:Fam ily Blank node

Scala DSLs customizable

Predicate-objects are pairs

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Some more RDF in Jena

implicit val m=createDefaultModel val ex="http://example.org/" val alice=iri(ex+"alice") val bob=iri(ex+"bob") val charlie=iri(ex+"charlie") alice+(RDF.`type`->FOAF.Person, FOAF.name->"Alice", FOAF.mbox->iri("mailto:alice@example.org"), FOAF.knows->bob, FOAF.knows->charlie, FOAF.knows->bnode) bob+ (FOAF.name->"Bob", FOAF.knows->charlie) charlie+(FOAF.name->"Charlie", FOAF.knows->alice)

Still valid Jena RDF You can do it even nicer

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Exploring an RDF Graph

ArrayList<String> names=new ArrayList<String>(); NodeIterator iter=model.listObjectsOfProperty(VCARD.N); while (iter.hasNext()){ RDFNode obj=iter.next(); if (obj.isResource()) names.add(obj.asResource() .getProperty(VCARD.Family).getObject().toString()); else if (obj.isLiteral()) names.add(obj.asLiteral().getString()); } val names=model.listObjectsOfProperty(VCARD.N).map{ case r:Resource=> r.getProperty(VCARD.Family).obj.toString case l:Literal=> l.getString }

Imperative iteration of collections Type-based conditional execution Type casting Case type Map applied to operators

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Query with SPARQL

val queryStr = """select distinct ?Concept where {[] a ?Concept} LIMIT 10""" val query = sparql(queryStr) query.serviceSelect("http://dbpedia.org/sparql").foreach{implicit qs=> println(res("Concept").getURI) } val f=Future(query.serviceSelect("http://es.dbpedia.org/sparql")).fallbackTo( Future(query.serviceSelect("http://dbpedia.org/sparql"))) f.recover{ case e=> println("Error "+e.getMessage) } f.map(_.foreach{implicit qs=> println(res("Concept").getValue) })

Remote SPARQL endpoin Simplified access to Query solutions Futures: asnyc execution Non blocking code Fallback alternative executi

slide-102
SLIDE 102

http://streamreasoning.org/events/rsp2016

104

Muchas Gracias!

slide-103
SLIDE 103

Tutorial on RDF Stream Processing 2016

M.I. Ali, J-P Calbimonte, D. Dell'Aglio,

  • E. Della Valle, and A. Mauri

http://streamreasoning.org/events/rsp2016

RDF Stream Processing Implementations

Jean-Paul Calbimonte

jean-paul.calbimonte@hevs.ch http://jeanpi.org @jpcik