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An Engine for Ontology-Based Stream Processing Theory and - - PowerPoint PPT Presentation

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An Engine for Ontology-Based Stream Processing Theory and Implementation Christian Neuenstadt 6. Februar 2018 Lbeck Motivation STARQL Transformation Evaluation Motivation - Use Case C. Neuenstadt An Engine for Ontology-Based Stream

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An Engine for Ontology-Based Stream Processing

Theory and Implementation Christian Neuenstadt

  • 6. Februar 2018

Lübeck

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Motivation STARQL Transformation Evaluation

Motivation - Use Case

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Motivation STARQL Transformation Evaluation

Query Answering

SPARQL

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Motivation STARQL Transformation Evaluation

Query Transformation

From SPARQL to SQL

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Motivation STARQL Transformation Evaluation

Query Transformation

From SPARQL to SQL

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Motivation STARQL Transformation Evaluation

Query Transformation

From SPARQL to SQL

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Transformation of Temporal Queries

Ontology-Based Stream Processing

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Motivation STARQL Transformation Evaluation

The Idea of STARQL

[S]treaming and [T]emporal ontology [A]ccess with a [R]easoning-based [Q]uery [L]anguage

  • We have developed a new query language for ontology-based streams

1) Uses temporal operators on state sequences 2) Adopts current ontology standards 3) Evaluates multiple streams

  • We have implemented a query transformation strategy
  • We execute transformed STARQL queries in modern database environments
  • DBMS for historic data
  • DSMS for live streams
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Motivation STARQL Transformation Evaluation

A static graph pattern

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Motivation STARQL Transformation Evaluation

A temporal graph pattern

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Motivation STARQL Transformation Evaluation

Graph pattern and streaming data

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Motivation STARQL Transformation Evaluation

Graph pattern and streaming data

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Motivation STARQL Transformation Evaluation

Graph pattern and streaming data

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Motivation STARQL Transformation Evaluation

Graph pattern and streaming data

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Motivation STARQL Transformation Evaluation

From temporal graphs to temporal states

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Motivation STARQL Transformation Evaluation

From temporal graphs to temporal states

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Motivation STARQL Transformation Evaluation

A window operator

SMsmt[NOW − 3s, NOW] → 1s

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Motivation STARQL Transformation Evaluation

A window operator

SMsmt[NOW − 3s, NOW] → 1s

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Motivation STARQL Transformation Evaluation

A window operator

SMsmt[NOW − 3s, NOW] → 1s

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Motivation STARQL Transformation Evaluation

STARQL Example 1 - Threshold

∃i, x(R1(x, i) ∧ x > 93)

Example

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SELECT ?x

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FROM S_Msmt [NOW-3s, NOW]-> 1s

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WHERE { :tempSensor :mountedAt :GasTurbine }

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HAVING EXISTS ?i IN (GRAPH ?i { :tempSensor :hasVal ?x }

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AND ?x > 93)

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Motivation STARQL Transformation Evaluation

STARQL Example 2 - Monotonic Increase

∀i, j, x, y(R1(sens, x, i) ∧ R2(sens, y, j) ∧ i < j → x ≤ y))

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CONSTRUCT GRAPH NOW {:tempSensor rdf:type MonInc }

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FROM S_Msmt [NOW-3s, NOW]-> 1s

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WHERE { :tempSensor :mountedAt :GasTurbine }

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HAVING FORALL ?i, ?j, ?x, ?y IN (

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IF GRAPH ?i { :tempSensor :hasVal ?x }

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AND GRAPH ?j { :tempSensor :hasVal ?y }

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AND ?i < ?j

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THEN ?x <= ?y )

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Motivation STARQL Transformation Evaluation

Transformation of temporal Graph Patterns with STARQL

Static mapping example ?sens :type :Sensor ← SELECT SensorName AS ?sens FROM Sensors (1) Time based mapping example GRAPH i { ?sens hasVal ?y } ← SELECT sId as ?sens, val as ?y FROM Slice(Measurement,i,r,sl,st). (2) i: index of the specifjc temporal state r: range of the window operator sl: slide parameter of the window operator st: sequencing strategy of the sequence generator

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Schematic Transformation of STARQL queries

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Motivation STARQL Transformation Evaluation

Comparison of implemented backend examples

PostgreSQL PipelineDB Exareme Spark Live Streams No Yes Yes Yes Static Data Yes Yes Yes Yes Historic Streams Yes No Yes Yes API JDBC JDBC REST API REST API / built in “Semantic access to streaming and static data at Siemens” Journal of Web Semantics 2017 “Towards Analytics Aware Ontology Based Access to Static and Streaming Data” ISWC 2016 “OBDA for Temporal Querying and Streams” HiDeSt@KI 2015 “A Stream-Temporal Query Language for Ontology Based Data Access” DL 2014 / KI 2014

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Motivation STARQL Transformation Evaluation

Comparison of implemented backend examples

PostgreSQL PipelineDB Exareme Spark Live Streams No Yes Yes Yes Static Data Yes Yes Yes Yes Historic Streams Yes No Yes Yes API JDBC JDBC REST API REST API / built in

“Semantic access to streaming and static data at Siemens” Journal of Web Semantics 2017 “Towards Analytics Aware Ontology Based Access to Static and Streaming Data” ISWC 2016 “OBDA for Temporal Querying and Streams” HiDeSt@KI 2015 “A Stream-Temporal Query Language for Ontology Based Data Access” DL 2014 / KI 2014

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Motivation STARQL Transformation Evaluation

Comparison of implemented backend examples

PostgreSQL PipelineDB Exareme Spark Live Streams No Yes Yes Yes Static Data Yes Yes Yes Yes Historic Streams Yes No Yes Yes API JDBC JDBC REST API REST API / built in “Semantic access to streaming and static data at Siemens” Journal of Web Semantics 2017 “Towards Analytics Aware Ontology Based Access to Static and Streaming Data” ISWC 2016 “OBDA for Temporal Querying and Streams” HiDeSt@KI 2015 “A Stream-Temporal Query Language for Ontology Based Data Access” DL 2014 / KI 2014

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Motivation STARQL Transformation Evaluation

Comparison of implemented backend examples

PostgreSQL PipelineDB Exareme Spark Live Streams No Yes Yes Yes Static Data Yes Yes Yes Yes Historic Streams Yes No Yes Yes API JDBC JDBC REST API REST API / built in “Semantic access to streaming and static data at Siemens” Journal of Web Semantics 2017 “Towards Analytics Aware Ontology Based Access to Static and Streaming Data” ISWC 2016 “OBDA for Temporal Querying and Streams” HiDeSt@KI 2015 “A Stream-Temporal Query Language for Ontology Based Data Access” DL 2014 / KI 2014

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Motivation STARQL Transformation Evaluation

Experimental Evaluation

Prototypical Implementation

Experiment 1: PostgreSQL / Spark (Historic Data)

  • Threshold and MonInc query executed on difgerent data volumns
  • Time scales for larger dataset with INTRAstate comparison
  • But INTERstate comparisons are expensive!!

Experiment 2: Multi Core Evaluation

  • Prototypical implementation per window execution based on pl/pgSQL
  • Reduces data set per execution dramatically for interstate queries
  • Scales by number of cores
  • Overhead for each window execution is not applicable to Spark
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Motivation STARQL Transformation Evaluation

Related Work

SRBenchmark Evaluation Language SPARQLStream C-SPARQL CQELS STARQL Supported queries 17 17 11 11 Missing functionalities of STARQL are: ASK queries(1) and Property Paths(6) Overall comparison Query Language:

  • All other three languages handle incoming triples as one graph per window.
  • Only C-SPARQL accesses timestamps or temporal ordering directly

Transformation:

  • Only SPARQLstream and STARQL can be transformed to relational algebra
  • C-SPARQL / CQELS use their own execution environment
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Motivation STARQL Transformation Evaluation

Summary/Outlook

  • We have shown how we can query intra/inter state-based temporal sequences

with temporal analytics in a new query language with syntax and semantics.

  • We defjned a new extended query transformation strategy that allows for an

execution on relational DB and streaming systems.

  • We executed the transformed queries on large volumns of batch and streamed

data successfully and showed their scalability regarding distributed window execution.

  • Future extensions:

1) Extend temporal operators and aggregation functions 2) Optimize window execution on backend systems 3) Extend ontology language

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