International workshop on semantic sensor web 2011


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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. The natural convergence point for these heterogeneous disciplines is the need of a strong advanced technologic support that enables the generation of distributed observations on large scale as well as the intelligent process of obtained information. An approach based on the Semantic Sensor Web could be the key issue for enabling semantic ecosystems among heterogeneous Cognitive Networks.

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International workshop on semantic sensor web 2011

  1. 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 )
  2. 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. 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. 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 NetwoRelationships Others Citizens Human behavior Wide Area (e.g. metropolitan) ( li ) orks Collectives Improve Quality of Life of  Life Smart Space
  5. 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. 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. 7. Semantic Ecosystems among heterogeneous Cognitive  y g g g Networks
  8. 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. 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. 10. Impact of Semantic Technologies I t fS ti T h l iCentralized Model Interoperability Model Semantic  Technologies Knowledge Distributed Model building/representation Model
  11. 11. Interoperability ModelI t bilit M d l
  12. 12. Knowledge Representation/Building Model K l d R t ti /B ildi M d lLocal Knowledge Ontology i Ontology i High‐level  Concepts p Domain‐specific Layers Domain specific Layers Core Data Layer Low‐level  Data Source Concepts
  13. 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. 14. Thank You! Salvatore F. Pileggi, PhD Researcher, TSB-ITACA Universidad Politécnica de Valencia (Spain) ( p )