LABORATOIRE D INFORMATIQUE DE L UNIVERSITE DE PAU ET DES PAYS DE L ADOUR Towards Digital Ecosystems Dr. Richard Chbeir, Ph.D. in CS Richard.chbeir@univ-pau.fr TH e-gif Day 2016 http://liuppa.univ-pau.fr )
Where I am coming from A Page 2
Where I am coming from BAYONNE ANGLET MONT-DE-MARSAN PAU TARBES Page 3
Page 4 Some photos of Pau/Anglet Campus
Page 5 Some photos of Anglet/Biarritz/Bayonne
What s a digital Ecosystem? Just imagine E-Bangkok App Page 6
What s a digital Ecosystem? Just imagine Sharing Annotating Pictures E- BKK Web Technologies People Page 7
What s a digital Ecosystem? Benefits Information Page 8
What s a digital Ecosystem? Benefits More Information Better decisions Relatively cheap Etc. Flooding Page 9
What s a digital Ecosystem? Web Collective knowledge WEM Extractor Digital Advisor framework Richard Chbeir Univ. de Pau - France Asanee Kawtrakul Kasetsart Univ. - Thailand Dominique Laurent Univ. de Cergy Pontoise - France Nicolas Spyratos Univ. Paris Sud - France Work supported by the French-Thai Mediator Experts Queries Farmers Farmer Diseases Expert Page 10 Query Processor
What s a digital Ecosystem? Another Example Shared Memory Page 11
What s a digital Ecosystem? Definitions A distributed, adaptive, and open system Self organizing, scalable and sustainable Win-win Interactions Ownership and Usage Control of Resources Equilibrium Promoting collaboration Supporting collective knowledge Page 12
What s a digital Ecosystem? Definitions Data Knowledge? Data: the lowest abstraction of data representation and contains no meaning E.g., 2001 is considered as a number consisting of 4 digits Information: The interpretation of the data by giving it well-defined meaning E.g., 2001 is the year of announcement of the Semantic Web" Meta-data: A description about the data and/or information Such as who gave the data (Wiki), when was it given, etc. (e.g., published in 2002, etc.) Page 13 Knowledge: The combination of all known data, information, and metadata concerning a given concept or fact, as well as the relations between them E.g., the year of announcement of the SW" is 2001", following Wikipedia in an article published in 2002
What s a digital Ecosystem? Definitions Knowledge Collective Knowledge? Collective Knowledge is the development of knowledge assets from a distributed pool of contributions Combination of several resources, information, and meta-data concerning a given (set of) concept(s), event(s), user(s), or process(es) And the semantic links between them Page 14
Where to use a digital Ecosystem? Page 15 Various domains Intelligent Transportation Systems Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) Smart cities Energy optimization: automatic energy metering Automatically adjusting the comfort level within the building Handling exceptional situations Medical technology and healthcare Implantable wireless identifiable devices (combining sensor, RFID, Bluetooth, ZigBee, and WiFi technologies)
Challenges Extracting CK Page 16
Challenges (1) Extracting CK Manipulating CK Search and Retrieval Page 17
Challenges (2) Avoid harming Respecting user/agent preferences vs. (collective) rules Respecting privacy Automatic knowledge evaluation and verification Prevent erroneous knowledge manipulation and processing Compare the produced CK through analyzing user feedback, data mining, statistical analysis, etc. Preservation/backup of CK over time Versioning: maintaining a history of CK variation and evolution Identifying the minimum amount of CK necessary for preservation Page 18
Challenges (3) Extrapolation of CK Study the evolution and variation trends of CK Inferring rules to automatically extrapolate new knowledge Promoting sophisticated services Knowledge recommendation: based on explicit needs, past experiences, profile, and preferences Automatic event generation and detection Event prediction: precautionary measures to optimize data/services sustainability and evolution Page 19
Challenges (4) Simplifying knowledge manipulation Semi-automated CK manipulations From traditional experts programmers towards nonexperts Mashups, Wrappers, and Dataflow Visual Programming Languages Page 20
Conclusion The Digital Ecosystem is improving the concept of cooperation and interaction: Machine to Machine (M2M) User to Machine (U2M) But mainly User to User (U2U) To smart interactions Interaction is not only based on a simple exchange of data But rather on an exchange of semantically meaningful information Exchange of knowledge well understood by machines Exchange of services Yielding to self-sustainability and evolution - 21 - Page 21 Towards Collective Intelligence!
Page 22 Some references
Some references Page 23 http://sigappfr.acm.org/medes/
Page 24 What do you think?
ขอบค ณ Richard.chbeir@univ-pau.fr 25