LarKC Tutorial at ISWC 2009 - Architecture

LarKC Architecture and Technology Michael Witbrock, Cycorp Europe (+UIBK) with contributions from all LarKC developers

Realising the Architecture Workflow Support System Plug-in Registry Data Layer Plug-in API Data Layer API RDF Store Plug-in Manager

LarKC Plug-in API: General Plug-in Model
Plug-ins are assembled into Workflows, to realise a LarKC Experiment or Application
Plug-ins are identified by a URI ( Uniform Resource Identifier)
Plug-ins provide MetaData about what they do (Functional properties): e.g. type = Selecter
Plug-ins provide information about their behaviour and needs, including Quality of Service information (Non-functional properties): e.g. Throughput, MinMemory, Cost,…
Plug-ins can be provided with a Contract that tells them how to behave (e.g. Contract : “give me the next 10 results”) and Context information used to store state between invocations
+ URI getIdentifier() + QoSInformation getQoSInformation() Plug-in
Functional properties
Non-functional properties
WSDL description
Plug-in description

LarKC Plug-in API: IDENTIFY
IDENTIFY : Given a query, identify resources that could be used to answer it
Sindice – Triple Pattern Query  RDF Graphs
Google – Keyword Query  Natural Language Document
Triple Store – SPARQL Query  RDF Graphs
+ Collection<InformationSet> identify (Query theQuery, Contract contract, Context context) Identifier

LarKC Plug-in API: TRANSFORM (1/2)
Query TRANSFORM : Transforms a query from one representation to another
SPARQL Query  Triple Pattern Query
SPARQL Query  Keyword Query
SPARQL Query  SPARQL Query (different abstraction)
SQARQL Query  CycL Query
+ Set<Query> transform(Query theQuery, Contract theContract, Context theContext) QueryTransformer

LarKC Plug-in API: TRANSFORM (2/2)
Information Set TRANSFORM : Transforms data from one representation to another
Natural Language Document  RDF Graph
Structured Data Sources  RDF Graph
RDF Graph  RDF Graph (e.g. foaf vocabulary to facebook vocabulary )
+ InformationSet transform(InformationSet theInformationSet, Contract theContract, Context theContext) InformationSetTransformer

LarKC Plug-in API: SELECT
SELECT : Given a set of statements (e.g. a number of RDF Graphs) will choose a selection/sample from this set
Collection of RDF Graphs  Triple Set (Merged)
Collection of RDF Graphs  Triple Set (10% of each)
Collection of RDF Graphs  Triple Set (N Triples)
+ SetOfStatements select(SetOfStatements theSetOfStatements, Contract contract, Context context) Selecter

LarKC Plug-in API: REASON
REASON : Executes a query against the supplied set of statements
SPARQL Query  Variable Binding (Select)
SPARQL Query  Set of statements (Construct)
SPARQL Query  Set of statements (Describe)
SPARQL Query  Boolean (Ask)
+ V ariableBinding sparqlSelect(SPARQLQuery theQuery, SetOfStatements theSetOfStatements, Contract contract, Context context) + SetOfStatements sparqlConstruct(SPARQLQuery theQuery, SetOfStatements theSetOfStatements, Contract contract, Context context) + SetOfStatements sparqlDescribe(SPARQLQuery theQuery, SetOfStatements theSetOfStatements, Contract contract, Context context) + BooleanInformationSet sparqlAsk(SPARQLQuery theQuery, S etOfStatements theSetOfStatements, Contract contract, Context context) Reasoner

LarKC Plug-in API: DECIDE
DECIDE : Builds the workflow and manages the control flow
Scripted Decider: Predefined workflow is built and executed
Self-configuring Decider: Uses plug-in descriptions (functional and non-functional properties) to build the workflow
+ V ariableBinding sparqlSelect(SPARQLQuery theQuery, QoSParameters theQoSParameters) + SetOfStatements sparqlConstruct(SPARQLQuery theQuery, QoSParameters theQoSParameters) + SetOfStatements sparqlDescribe(SPARQLQuery theQuery, QoSParameters theQoSParameters) + BooleanInformationSet sparqlAsk(SPARQLQuery theQuery, QoSParameters theQoSParameters) Decider

Released System: larkc.sourceforge.net
Open Apache 2.0 license
Previous early adopters workshop @ ESWC
20 people attended
participants modified plug-ins, modified workflows
Standard Open Environment: subversion connection, command line build, or eclipse, netbeans soon?
Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Plug-in Registry Pipeline Support System

LarKC Plug-in API + Collection<InformationSet> identify (Query theQuery, Contract contract, Context context) Identifier + Set<Query> transform(Query theQuery, Contract theContract, Context theContext) QueryTransformer + InformationSet transform(InformationSet theInformationSet, Contract theContract, Context theContext) InformationSetTransformer + SetOfStatements select(SetOfStatements theSetOfStatements, Contract contract, Context context) Selecter + VariableBinding sparqlSelect(SPARQLQuery theQuery, SetOfStatements theSetOfStatements, Contract contract, Context context) + SetOfStatements sparqlConstruct(SPARQLQuery theQuery, SetOfStatements theSetOfStatements, Contract contract, Context context) + SetOfStatements sparqlDescribe(SPARQLQuery theQuery, SetOfStatements theSetOfStatements, Contract contract, Context context) + BooleanInformationSet sparqlAsk(SPARQLQuery theQuery, S etOfStatements theSetOfStatements, Contract contract, Context context) Reasoner + VariableBinding sparqlSelect(SPARQLQuery theQuery, QoSParameters theQoSParameters) + SetOfStatements sparqlConstruct(SPARQLQuery theQuery, QoSParameters theQoSParameters) + SetOfStatements sparqlDescribe(SPARQLQuery theQuery, QoSParameters theQoSParameters) + BooleanInformationSet sparqlAsk(SPARQLQuery theQuery, QoSParameters theQoSParameters) Decider
5 types of plug-ins
Plug-in API enables interoperability (between plug-in and platform and between plug-ins)
Plug-ins I/O abstract data structures of RDF triples => flexibility for assembling plug-ins and for plug-in writers
Compatibility ensured by DECIDER and workflow configurators, based on plug-in description

LarKC Plug-in API LarKC Architecture Data Layer API Pipeline Support System Plug-in Registry RDF Store RDF Store RDF Store RDF Doc RDF Doc RDF Doc Data Layer Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Application Platform Utility Functionality APIs Plug-ins External systems External data sources Plug-in API Plug-in API Plug-in API Plug-in API Plug-in API Plug-in API

What does a workflow look like? Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Plug-in Registry Workflow Support System RDF Store Identifier Info Set Transformer Reasoner Decider Selecter Query Transformer

What Does a Workflow Look Like? Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Plug-in Registry Workflow Support System RDF Store Identifier Info Set Transformer Reasoner Decider Selecter Query Transformer Data Layer Data Layer Data Layer Data Layer RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph Default Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph

LarKC Data Model :Transport By Reference RDF Graph Dataset: Collection of named graphs Labeled Set: Pointers to data Current Scale: O(10 10 ) triples RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph Default Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph

What Does a Workflow Look Like? Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Plug-in Registry Workflow Support System RDF Store Identifier Info Set Transformer Reasoner Decider Selecter Query Transformer Data Layer Data Layer Data Layer Data Layer RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph Default Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph

What Does a Pipeline Look Like? Info Set Transformer Identifier Identifier Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Plug-in Registry Wlorkflow Support System RDF Store Identifier Info Set Transformer Reasoner Decider Selecter Query Transformer Data Layer Data Layer Data Layer Data Layer

Remote and Heterogeneous Plug-ins Remote Plug-in Manager Adaptor External or non-Java Code TRANSFORM SPARQL-CycL Research Cyc TRANSFORM SPARQL- GATE API GATE IDENTIFY SPARQL SINDICE IDENTIFY SPARQL Medical Data Data Layer

What Does a Workflow Look Like? Info Set Transformer Identifier Identifier Info Set Transformer Reasoner Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Plug-in Registry Workflow Support System RDF Store Identifier Info Set Transformer Reasoner Decider Selecter Query Transformer Data Layer Data Layer Data Layer Data Layer

Decider Using Plug-in Registry to Create Pipeline D 1.3.1
Represent Properties
Functional
Non-functional (e.g. QoS)
WSMO-Lite Syntax
Logical Representation
Describes role
Describes Inputs/Outputs
Automatically extracted using API
Decider can use for dynamic configuration
Rule-based
Fast
Q T I S R VB A Q T I S R VB B

LarKC Plug-ins
Provide SPARQL end-points
Run in separate threads
Automatically add meta-data to registry when loaded
Communicate RDF data by passing labelled sets or references to labelled sets
Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API
Parallelisation in progress
Plug-in Manager Transformer Plug-in API Plug-in Manager Identifier Plug-in API ransformer Transformer Transformer Plug-in Manager Identifier Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Selector Plug-in API Plug-in Manager Selector Plug-in API
Split/Join connectors in progress

LarKC Data Layer Data Layer API Data Layer Data Layer API Pipeline Support System Plug-in Registry RDF Store RDF Store RDF Store RDF Doc RDF Doc RDF Doc Data Layer Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Application Platform Utility Functionality APIs Plug-ins External systems External data sources

LarKC Data Layer
Main goal:
The LarKC Data Layer supports all LarKC plug-ins with respect to:
storage, retrieval and light-weight inference on top of large volumes of data
automates the exchange of RDF data by reference and by value
offers other utility tools to manage data (e.g. merger)
RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph Default Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph RDF Graph Dataset Labeled Set

The implementation of the data layer was evaluated against
Well-known benchmarks: LUBM (Lehigh Univ. Benchmark) and BSBM (Berlin SPARQL Benchmark), and
Two views to the web of linked data used in LarKC: PIKB (Pathway and Interaction Knowledge Base) and LDSR ( Linked Data Semantic Repository )
Loading:
15B statements at 18 KSt/sec. on $10,000 server
1B statements at 66 KSt/sec. on $2,000 desktop
Reasoning & Materialization:
LUBM: 21 KSt/sec for 1BSt and 10 KSt/sec for 7B expl. statements
LDSR: 14 KSt/sec for 357M expl. statements
PIKB: 10 KSt/sec for 1.5B expl. Statements
Competitive with State of the Art
LarKC Data Layer Performance

Inference with both LDSR and PIKB prove to be much more complex than LUBM , because
The datasets are much better interconnected
There are plenty of owl:sameAs links
OWL vocabulary is used disregarding its formal semantics
E.g. in DBPedia there are skos:broader cycles of categories with length 180
Optimizations of the handling of owl:sameAs are crucial
PIKB: 1.47B explicit statements + 842M inferred
LDSR loaded in 7 hours on desktop:
Number of imported statements (NIS): 357M
Number of new inferred statements: 512M
Number of stored statements (NSS): 869M
Number of retrievable statements (NRS): 1.14B
owl:sameAs optimisation allowed reducing the indices by 280M statements
LarKC Data Layer Evaluation: Linked Data

Plug-in Architecture Signs of Success
Platform and Plug-in APIs are useable
In the twenties of plug-ins already
Plug-ins written with little help from architects
Plug-ins run successfully, and perform together
Outside plugin-writers:
OKKAM, NeOn, Aberdeen
Plug-in Manager Identifier Plug-in API

Active and Ready for the Public
2170 check-outs
1380 commits
23 users of code repository
LarKC + Alpha
Plus Early Adopters Workshop branch
20 downloads of alpha 1 public release since 30 th May 2009.

Project Timeline 14 Surveys (plug-ins, platform) & Requirements (use cases) Prototype Internal Release Public Release Final Release 42 0 6 18 33 10 Plug-ins Use Cases V1 Use Cases V2 Use Cases V3 Data caching Offer computing resources Anytime behaviour Monitoring & instrumentation

Rapid Progress, but We’re Not Finished…
Optimisation of complex workflows.
Extend meta-data representation for QoS, parallelism and use it.
Concentrate on parallel and distributed execution.
Concentrate on parallel and distributed data layer; caching and data migration.
Support more plug-in needs while maintaining platform integrity (e.g. efficient weight modification for spreading activation)
Data write for persistent transformation (e.g. rumination reasoning in Marvin experiments)
Support workflows inspired by human cognition (e.g. workflow interruption for optimal stopping)
Support anytime/streaming
Experimental instrumentation and monitoring
Data Layer API Pipeline Support System Plug-in Registry RDF Store RDF Store RDF Store RDF Doc RDF Doc RDF Doc Data Layer Decider Plug-in API Plug-in Manager Query Transformer Plug-in API Plug-in Manager Identifier Plug-in API Plug-in Manager Info. Set Transformer Plug-in API Plug-in Manager Selecter Plug-in API Plug-in Manager Reasoner Plug-in API Application
Data Caching
Anytime Behaviour
Plug-in Registration and Discovery
Plug-in Monitoring and Measurement
Support for Developers
Plug-ins
Classified according to:
Resources
Heterogeneity
Usage
Interoperability
Parallelization “within plug-ins”
Distributed/remote execution
Data Layer
Sources
Initial Project Objectives (DoW)
LarKC Collider Platform (WP5 discussions)
LarKC Rapid Prototyping
LarKC Use Cases (WP6, WP7a, WP7b)
LarKC Plug-ins (WP2, WP3, WP4)
Detailed information in D5.3.1 Requirements Analysis and report on lessons learned during prototyping Requirements (WP 5)

Distributed Data Layer
Caching, data warming/cooling
Data Streaming between remote components
Parallelization and distribution on different types of environments (high-performance grid, desktop grid, etc.)
Experimental instrumentation and monitoring
Open Issues & Next Steps
Requirements traceability and update
Architecture refinement
Platform validation Early Adopters

LarKC Tutorial at ISWC 2009 - Architecture

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LarKC Tutorial at ISWC 2009 - Architecture — Presentation Transcript

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