1. Agenda for today
09: :00 Overview and Goals of LarKC, Frank van Harmelen
10: :30 Introduction to the LarKC Architecture, Spyros Kotoulas
10: :00 Coffee break
11: :30 Hands-on 1: Working with an existing LarKC workflow, Alexey Cheptsov
11: :00 Introduction to the LarKC Data Layer, Vassil/Naso
12: :00 Hands-on 2: Building a LarKC decider plug-in to create a workflow from existing plug-ins, Luka Bradesko
13: :30 Lunch
14: :00 Introduction to Distributed Processing in LarK, Alex Cheptsov
15: :00 Hands-on 3: Building a LarKC plug-in and integrating it into an existing workflow, Luka Bradesko
16: :30 Coffee break
16: :00 Hands-on 4: Understanding and Manipulating the Urban Computing workflow, Emanuele Della Valle
17:00 – 17:15 Demonstrate thread-pooling, Alex Cheptsov
17: Closing and Open Discussion, Frank van Harmelen

2. Welcome to the 3rd LarKC Early Adopters Workshop Frank van Harmelen Vrije Universiteit Amsterdam

3. Health Warning Today is a WORK shop we first tell you some stuff,
then you do stuff
(repeat)
Goal of today:
ours: show LarKC to outsiders <who are we>,
yours: <tell us now>

4. Goals of today At the end of today you will
understand the goals of LarKC
understand the architecture of LarKC
have hands on experience with platform and plugins
At the end of the day, you will be able to:
roll your own LarKC plugin
roll your own LarKC application

5. LarKC = a platform for large scale reasoning
Goals of LarKC
LarKC = a platform for large scale reasoning
Quote from EU Project Officer:
“LarKC's value is as an experimental platform.
LarKC is as an environment where people can go to replicate (or extend) their results in an environment where all the infrastructural heavy lifting has already been taken care of” 5 5

6. LarKC = a platform for large scale reasoning
Goals of LarKC
LarKC = a platform for large scale reasoning
Quote from US high-tech CTO:
Semantic web research is stifled by the complexity of writing a large scale engine, with services for data access, storage, aggregation, inference, transport, transformation, etc,
Physics research has dealt with a similar problem by providing large scale infrastructure into which experiments can be plugged.
The idea behind LarKC, which I found so compelling, is that people who wanted to build small scale plugins, for example, plugins for some non-standard deduction, or transformation of text to triples, or estimating the weights for relational models, could do so, taking advantage of the EU's investment in a platform with significant capabilities.“ 6 6

7. LarKC = a platform for large scale reasoning
Goals of LarKC
LarKC = a platform for large scale reasoning
Quote from EU Reviewer:
“Significant progress is sometimes made not by making something possible that was impossible before,
but by substantially lowering the costs
of something that was only possible before at high cost” 7 7

8. LarKC = a platform for large scale reasoning
What do we mean by:
LarKC = a platform for large scale reasoning
reusable components
reconfigurable workflows
provide infrastructure needed by all users:
storage & retrieval
registration of plugins
communication (plugin2datalayer, plugin2plugins)
synchronisation (anytime behaviour)
remote execution (abstracts from local/remote storage)
remote data-access (abstracts from local/remote invation)
(will) provide instrumentation & measuring
(will) provide caching & data-locality
integration of very heterogeneous components
heterogeneous data: unstructured text, (semi)structured data
heterogeneous code: Java, scripts, remote services ("wrap & integrate") 8 8

9. LarKC = a platform for large scale reasoning
What do we mean by:
LarKC = a platform for large scale reasoning
not only from
raw large numbers
from performant data-layer
from parallel computing in plugins
from parallel deployment of plugins …
but also from
interaction of multiple components
e.g. avoid reasoning through selection: SELECT + REASON
allowing for incompletenes and anytime behaviour 9 9

10. What do we mean by: LarKC = a platform for large scale reasoning
not only: deductive inference over given axioms
but also:
where do the axioms come from? (IDENTIFY)
which part of knowledge & data is required (SELECTion)
when is an answer "good enough" or "best possible" (DECIDEr)
non-deductive inference (inductive, statistical) (REASONer)
“ReaSearch: integrating reasoning and search" 10 10

11. Overall approach of LarKC
Very lightweight platform
communication, synchronisation, registration
LarKC = “SPARQL endpoint on steroids”
The real work happens in the plugins
LarKC gives you:
very scalable datalayer
standardised interfaces for combining components
utilities & infrastructure to abstract from remote deployment
Three types of LarKC users:
people building plugins
people configuring workflows
people using workflows

12. How to deploy LarKC All local: Calling remote plugins: Fully remote
platform local, plugins local
Example: workstation
Calling remote plugins:
platform local, (some) plugins remote
Example: laptop
Fully remote
platform remote (eg. as a web-service)
plugins remote
Example: cluster

13. Why would people (like you) want to use LarKC
workflow builders:
easier to get some application scenario running
Plugin builders:
easier integration with components by others,
wider take up of your own component by others

14. What does a workflow look like?
Decider
Query
Transformer
Identifier
Info Set Transformer
Selector
Reasoner
Data Layer 14

15. What does a workflow look like?
Decider
Query
Transformer
Identifier
Info Set Transformer
Selector
Reasoner
Data Layer
Data Layer
Data Layer
Data Layer
Data Layer 15

16. What does a workflow look like?
Decider
Query
Transformer
Identifier
Info Set Transformer
Selector
Reasoner 16

17. What does a workflow look like?
Decider
Identifier
Selector
Reasoner 17

18. What does a workflow look like?
Decider
Selector
Reasoner 18

19. What does a workflow look like?
Decider
Selector
Reasoner 19

20. What does a workflow look like?
Decider
ETCETERA
Identifier
Info Set Transformer
Query
Transformer
Identifier
Selector
Reasoner
Identifier
Info Set Transformer 20

21. What does a DECIDEr look like?
Can be a hardcoded sequence of plugins
Can be a self-configuring selection of plugins
Can make run-time decisions on progress and resource consumption
Coded as:
Java
a Cyc knowledge base
...
as long as it complies with the DECIDEr API

22. Already any plugins available?
existing web-services (e.g. Sindice, Swoogle)
another RDF store (geo-queries in Allegrograph)
a very large (workflow-based) system (GATE)
existing reasoners (Jena, Pellet, Cyc, IRIS)
XSLT scripts (XML-2-RDF)
spreading activitation (new)
RDF-2-weightedRDF (new)
5x IDENTIFY
3x TRANSFORM
10x SELECT
4x REASON
4x DECIDE
Sometimes sophisticated, sometimes simple
Sometimes novel, sometimes wrapped

23. Goals of LarKC, and where we are
Scalable: > 109 triples, lazy pipes
Reconfigurable: plugins with standard API’s
Open: Apache license
heterogenous: TRANSFORM, wrappers
experimentation: wrap & integrate
allow incompleteness: IDENTIFY, SELECT
enable distribution: plugin containers
anytime behaviour: streaming APIs
web-enabled: remote plugins & data

24. What we will not show today
Available but not demo’d:
lot’s of plugins
C-SPARQL: extension of SPARQL to enable stream-querying
cognition-based heuristics (e.g. selection rules, stopping rules)
very cool data-sets
Linked Life Data 2.7B explicit, 4.1B closure, 580M things,2.1M interlinks
Milan traffic grid
Interest-enhanced DBLP (615k authors + interests)
LDSR (1.3B explit + 2.2B closure, 400m things)
very large/fast inference engines:
MarVIN (P2P)
WebPIE (MapReduce)
Not yet available (but will be):
instrumentation & measuring
smart data caching

25. Agenda for today
09: :00 Overview and Goals of LarKC, Frank van Harmelen
10: :30 Introduction to the LarKC Architecture, Spyros Kotoulas
10: :00 Coffee break
11: :30 Hands-on 1: Working with an existing LarKC workflow, Alexey Cheptsov
11: :00 Introduction to the LarKC Data Layer, Vassil/Naso
12: :00 Hands-on 2: Building a LarKC decider plug-in to create a workflow from existing plug-ins, Luka Bradesko
13: :30 Lunch
14: :00 Introduction to Distributed Processing in LarK, Alex Cheptsov
15: :00 Hands-on 3: Building a LarKC plug-in and integrating it into an existing workflow, Luka Bradesko
16: :30 Coffee break
16: :00 Hands-on 4: Understanding and Manipulating the Urban Computing workflow, Emanuele Della Valle
17:00 – 17:15 Demonstrate thread-pooling, Alex Cheptsov
17: Closing and Open Discussion, Frank van Harmelen