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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
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
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.
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
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).
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.
Semantic Ecosystems among heterogeneous Cognitive 
             y          g       g         g
                    Networks
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
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
Impact of Semantic Technologies
        I    t fS      ti T h l i




Centralized Model                   Interoperability Model


                  Semantic 
                Technologies

                                          Knowledge 
Distributed Model                   building/representation
                                             Model
Interoperability Model
I t       bilit M d l
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
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.
Thank You!


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

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International workshop on semantic sensor web 2011

  • 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.
  • 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
  • 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