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Profiling information
sources and services
for discovery
Mathieu d’Aquin - @mdaquin
Insight Centre - NUI Galway
Let’s start with a quote
“access billions of heterogeneous data sources, stored or real-time, locally
or globally, from personal and public terminals, mobile, wearable, Internet
of Things, and cyber-physical systems [...] present relevant information
services in a meaningful and personalised way”
-- Fahim Kawsar, Christophe Diot, Carles Sierra and Nozha Boujemaa,
Discovery & Identification Technologies for the Next Generation Internet
Let’s start with a quote
“access billions of heterogeneous data sources, stored or real-time,
locally or globally, from personal and public terminals, mobile, wearable,
Internet of Things, and cyber-physical systems [...] present relevant
information services in a meaningful and personalised way”
-- Fahim Kawsar, Christophe Diot, Carles Sierra and Nozha Boujemaa,
Discovery & Identification Technologies for the Next Generation Internet
More concretely: A scenario
Let’s think about an autonomous,
physical mobile agents, a robot,
coming into a new environment
(building, city, organisation, event)
where data sources are available:
1. How does it find the data
sources relevant to its task?
2. How does it use the data
sources relevant to its task?
3. How does it contribute to data
sources relevant to its task?
Metadata and content
Discovery currently
handled through data
cataloguing.
Should focus on the
content of data: In which
context is it valid, and what
can be done with it?
Example: What question can it answer?
Example: What question can it answer?
Servicecode
Area
Restaurant
Organisation
isa
population
deprivation
locatedIn
rating
employee
The
population
of Walnut
Tree is 4096
What is the
population
of Walnut
Tree?
Example: How good is it? Does it fit what I know?
Consensus
(agreement)
Controversy
Disagreement (negative consensus)
Example: Do I actually have the right to use it?
Conclusion
With information sources expanding in all directions,
we need mechanisms by which their consumption and
use can emerge dynamically through autonomous
agents being able to make sense of new data spaces.
Requires profiling which is based on the
“meta-analysis” of data, to extract:
- Scope, validity (in time, space, rights, etc.)
- Competence, inter-relatedness
- Views, biases, quality issues
- Exploitability
@mdaquin - mdaquin.net

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Profiling information sources and services for discovery

  • 1. Profiling information sources and services for discovery Mathieu d’Aquin - @mdaquin Insight Centre - NUI Galway
  • 2. Let’s start with a quote “access billions of heterogeneous data sources, stored or real-time, locally or globally, from personal and public terminals, mobile, wearable, Internet of Things, and cyber-physical systems [...] present relevant information services in a meaningful and personalised way” -- Fahim Kawsar, Christophe Diot, Carles Sierra and Nozha Boujemaa, Discovery & Identification Technologies for the Next Generation Internet
  • 3. Let’s start with a quote “access billions of heterogeneous data sources, stored or real-time, locally or globally, from personal and public terminals, mobile, wearable, Internet of Things, and cyber-physical systems [...] present relevant information services in a meaningful and personalised way” -- Fahim Kawsar, Christophe Diot, Carles Sierra and Nozha Boujemaa, Discovery & Identification Technologies for the Next Generation Internet
  • 4. More concretely: A scenario Let’s think about an autonomous, physical mobile agents, a robot, coming into a new environment (building, city, organisation, event) where data sources are available: 1. How does it find the data sources relevant to its task? 2. How does it use the data sources relevant to its task? 3. How does it contribute to data sources relevant to its task?
  • 5. Metadata and content Discovery currently handled through data cataloguing. Should focus on the content of data: In which context is it valid, and what can be done with it?
  • 6. Example: What question can it answer?
  • 7. Example: What question can it answer? Servicecode Area Restaurant Organisation isa population deprivation locatedIn rating employee The population of Walnut Tree is 4096 What is the population of Walnut Tree?
  • 8. Example: How good is it? Does it fit what I know? Consensus (agreement) Controversy Disagreement (negative consensus)
  • 9. Example: Do I actually have the right to use it?
  • 10. Conclusion With information sources expanding in all directions, we need mechanisms by which their consumption and use can emerge dynamically through autonomous agents being able to make sense of new data spaces. Requires profiling which is based on the “meta-analysis” of data, to extract: - Scope, validity (in time, space, rights, etc.) - Competence, inter-relatedness - Views, biases, quality issues - Exploitability