This document discusses enabling semantic ecosystems among heterogeneous cognitive networks. It describes cognitive networks and their potential to operate at large scales to solve problems across various domains. However, current approaches have limitations like a lack of social perspectives, static coverage models, and obsolete views of resources. Semantic technologies can help address these issues by improving interoperability and enabling knowledge building and representation across distributed systems. This allows for relating heterogeneous data from different sources as part of a unified knowledge ecosystem.

1. Enabling Semantic Ecosystems among heterogeneous
Cognitive Networks
g
Salvatore F. Pileggi, Carlos Fernandez‐Llatas, Vicente Traver
International Workshop on Semantic Sensor Web 2011
(SSW2011)
October 27th , Paris, France
Salvatore F. Pileggi, PhD
Researcher, TSB-ITACA
Universidad Politécnica de Valencia (Spain)
( p )
salpi@itaca.upv.es
www.flaviopileggi.net

2. Index
• Introduction
• Cognitive Networks
• Cognitive Networks: from science to reality
Networks: from
• Semantic Ecosystems among heterogeneous Cognitive Networks
• Current Approaches and Limitations
• The impact of semantic technologies: distributed approach
• Semantic Interoperability
• Knowledge building
g g
• Conclusions

3. Introduction: Cognitive N t
I t d ti C iti Networks
k
• Cognitive network ( ) is a new type of d
k (CN) f data network that makes use of
k h k f
cutting edge technology from several research areas (i.e. machine
learning, knowledge representation, computer network, network
management) to solve some problems current networks are faced with.
Too Much generic
generic…..
• Cognitive Networks working on large scale are object of an increasing
interest by both the scientific and the commercial point of view in the
context of several environments and domains
domains.

4. Cognitive Networks: scale perspective
C iti N t k l ti
Complexity/ Scale Technologic Application Users
Knowledge
Support
Research/
Science
Climatic/environmental
phenomena
Relationships
Cognit
Global
tive Netwo
Relationships Others Citizens
Human behavior
Wide Area
(e.g. metropolitan)
( li )
orks
Collectives
Improve Quality of Life
of Life
Smart Space

5. Metropolitan/Urban Ecosystems (1)
Metropolitan/Urban Ecosystems (1)
• A metropolitan ( urban) ecosystem i d fi d as a l
t lit (or b ) t is defined large scale
l
ecosystem composed of the environment, humans and other living
organisms, and any structure/infrastructure or object physically
located in the reference area.
• We are living in an increasingly urbanized world (tendency will be probably followed
also in the next future)
future).
• Further increases in size and rates of growth of cities will no doubt stress already
impacted environments as well as the social aspect of the problem.
• This tendency is hard to be controlled or modified.
y
• A great number of interdisciplinary initiatives, studies and researches aimed to
understand the current impact of the phenomena as well as to foresee the evolution of
it (scientific or practice focus).

6. Metropolitan/Urban Ecosystems (2)
Metropolitan/Urban Ecosystems (2)
• The study of the h
h d f h human activities, of the environmental and climatic phenomena
i ii f h i l d li i h
is object of interest in the context of several disciplines and applications.
• All these studies are normally independent initiatives, logically separated
researches and, in the majority of the cases, results are hard to be directly related.
This could appear a paradox: interest phenomena happen in the
same physical ecosystem, involving the same actors but the
definition of the dependencies/relationships among atomic
results are omitted even if they are probably the most relevant
results are omitted even if they are probably the most relevant
results.

7. Semantic Ecosystems among heterogeneous Cognitive
y g g g
Networks

8. Current approaches and limitations (1)
C t h d li it ti
• The normal technologic support f enabling k
h l h l i for bli knowledge environment i the
l d i is h
cognitive network that assumes a physical infrastructure (sensors) able to detect
interest information or phenomena and a logic infrastructure able to process the
sensor d t (k
data (knowledge b ildi ) eventually performing actions, responses or
l d building) t ll f i ti
complex analysis.
• The parameters that can potentially affect the “quality” of the applications or
studies are mainly the sensor technology (constantly increasing in terms of
reliability, precision and capabilities), the coverage area, the amount of data and,
finally, h
f ll the process capabilities.
bl

9. Current approaches and limitations (2)
C t h d li it ti
• Current solutions are hard to be proposed on large scale due to the current
limitations of the massive sensors deployment on large scale.
• Furthermore, the following limitations can be clearly identified:
o Lack of social view at the information
o Static coverage models
o Obsolete view at resources
b l
o Not always effective business models

10. Impact of Semantic Technologies
I t fS ti T h l i
Centralized Model Interoperability Model
Semantic
Technologies
Knowledge
Distributed Model building/representation
Model

11. Interoperability Model
I t bilit M d l

12. Knowledge Representation/Building Model
K l d R t ti /B ildi M d l
Local Knowledge
Ontology i Ontology i
High‐level
Concepts
p Domain‐specific Layers
Domain specific Layers
Core
Data
Layer
Low‐level Data Source
Concepts

13. Conclusions
C l i
• The power of collecting and relating h
h f ll i d l i heterogeneous d data f
from di ib d source i
distributed is
the real engine of high‐scale cognitive networks.
• The economic sustainability, as well as the social focus on the great part of the
applications, determines the need of an innovative view at networks and
architectures on the model of most modern virtual organizations.
• These solutions require a high level of interoperability, at both functional and
semantic level.
• The current “Semantic Sensor Web” approach assures a rich and dynamic
technologic environment in which heterogeneous data from distributed source can
be related, merged and analyzed as part of a unique knowledge ecosystem.

14. Thank You!
Salvatore F. Pileggi, PhD
Researcher, TSB-ITACA
Universidad Politécnica de Valencia (Spain)
( p )
salpi@itaca.upv.es
www.flaviopileggi.net