RETAIL DESIGN . ARCHITECTURE COMMERCIALE . MERCHANDISING
BRIO est né en 2005 de l’association de compétences tant issues des annonceurs et de la distribution que des meilleures agences de design et d’architecture. Brio crée le lien entre stratégie retail, la marque, et la création design 3D.
Eléonore GOLOVANOFF , créatrice et co-dirigeante de l’agence
Eléonore Golovanoff est spécialiste des marques et de la création de concepts retail innovants. Après avoir dirigé une agence d’architecture parisienne elle a assuré dès 2005 la création des différents concepts de points de vente de l’opérateur leader (700 boutiques). Ses connaissances tant du design et de l’architecture que de la distribution lui permettent de coordonner la création et le développement des projets de l’agence. Eléonore Golovanoff est Architecte DPLG, intervenante à l'IFM (institut français du merchandising) et à la fédération française du prêt à porter.
Brio eurl - siège social au 9 place du busca 31400 Toulouse - RCS toulouse 495 377 905 - (00)33 1 45 83 00 61 – contact@brioretail.com
Awards : enseigne d’or / popai europe / prix stratégie concept point de vente
WWW.BRIORETAIL.COM
RETAIL DESIGN . ARCHITECTURE COMMERCIALE . MERCHANDISING
BRIO est né en 2005 de l’association de compétences tant issues des annonceurs et de la distribution que des meilleures agences de design et d’architecture. Brio crée le lien entre stratégie retail, la marque, et la création design 3D.
Eléonore GOLOVANOFF , créatrice et co-dirigeante de l’agence
Eléonore Golovanoff est spécialiste des marques et de la création de concepts retail innovants. Après avoir dirigé une agence d’architecture parisienne elle a assuré dès 2005 la création des différents concepts de points de vente de l’opérateur leader (700 boutiques). Ses connaissances tant du design et de l’architecture que de la distribution lui permettent de coordonner la création et le développement des projets de l’agence. Eléonore Golovanoff est Architecte DPLG, intervenante à l'IFM (institut français du merchandising) et à la fédération française du prêt à porter.
Brio eurl - siège social au 9 place du busca 31400 Toulouse - RCS toulouse 495 377 905 - (00)33 1 45 83 00 61 – contact@brioretail.com
Awards : enseigne d’or / popai europe / prix stratégie concept point de vente
WWW.BRIORETAIL.COM
A Survey on Federated Learning Systems Vision, Hype and Reality for Data Priv...OKOKPROJECTS
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
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* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
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'Using Linked Data in Learning Analytics' is a tutorial targeting researchers in Learning Analytics interested in exploiting linked data resources, developers of Learning Analytics solutions that could benefit from Linked Data and data owners wanting to understand how linked data can help the analysis of their data in relation to other sources of information. The tutorial is described in more details at http://linkedu.eu/event/lak2013-linkeddata-tutorial/, where learning material related to the topic of the tutorial will also be disseminated.
http://portal.ou.nl/documents/363049/033208ab-9dba-43be-b1d8-80d6423c0654
http://creativecommons.org/licenses/by-nc-sa/3.0/
d'Aquin, M., Dietze, S., Herder, E., Drachsler, H. (Eds.) (2013). Tutorial: Using Linked Data in Learning Analytics. Tutorial given at LAK 2013, the Third Conference on Learning Analytics and Knowledge. Leuven, Belgium.
Open Government Data on the Web - A Semantic ApproachPeter Krantz
(upload with permission from Armand Brahaj)
Initiatives of making governmental data open are continuously gaining interest recently. While this presents immense benefits for increasing transparency, the problem is that the data are frequently offered in heterogeneous formats, missing clear semantics that clarify what the data describes. The data are displayed in ways that are not always clearly understandable to a broad range of user communities that need to make informed decisions.
Collaborative Knowledge Management in Organization from SECI model FrameworkNatapone Charsombut
A presentation file for TIIM conference 2010 Pattaya Thailand,
ABSTRACT
In the age of social collaboration and sharing that enables by Web 2.0 and Linked Data, many organizations adapt themselves into advantages of interactive, sharing, reusing, interoperability and collaboration on World Wide Web. Organizational learning which is sub of knowledge management also greatly gains benefit from this emerging collaboration culture too. It provides abilities to share valuable insights, to reduce redundant work, to avoid reinventing the wheel, to reduce training time for new employees, to retain intellectual capital as employee turnover in an organization, and to adapt to changing environments and markets.
However, user created content from Web 2.0 multiplying with published structure of data according to Linked Data concept will be a massive amount of data. It is inevitable facing the overwhelming of data. Traditional knowledge management is not designed to extract knowledge from social collaboration. We need a framework that fit for knowledge transfer in highly interaction environment.
SECI model which is a knowledge management based on collaborative knowledge transfer in organization seem to be the best candidate for navigating knowledge creation in this case. This study attempts to address how to apply SECI model to knowledge management system in collaborative organization.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
A Survey on Federated Learning Systems Vision, Hype and Reality for Data Priv...OKOKPROJECTS
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
'Using Linked Data in Learning Analytics' is a tutorial targeting researchers in Learning Analytics interested in exploiting linked data resources, developers of Learning Analytics solutions that could benefit from Linked Data and data owners wanting to understand how linked data can help the analysis of their data in relation to other sources of information. The tutorial is described in more details at http://linkedu.eu/event/lak2013-linkeddata-tutorial/, where learning material related to the topic of the tutorial will also be disseminated.
http://portal.ou.nl/documents/363049/033208ab-9dba-43be-b1d8-80d6423c0654
http://creativecommons.org/licenses/by-nc-sa/3.0/
d'Aquin, M., Dietze, S., Herder, E., Drachsler, H. (Eds.) (2013). Tutorial: Using Linked Data in Learning Analytics. Tutorial given at LAK 2013, the Third Conference on Learning Analytics and Knowledge. Leuven, Belgium.
Open Government Data on the Web - A Semantic ApproachPeter Krantz
(upload with permission from Armand Brahaj)
Initiatives of making governmental data open are continuously gaining interest recently. While this presents immense benefits for increasing transparency, the problem is that the data are frequently offered in heterogeneous formats, missing clear semantics that clarify what the data describes. The data are displayed in ways that are not always clearly understandable to a broad range of user communities that need to make informed decisions.
Collaborative Knowledge Management in Organization from SECI model FrameworkNatapone Charsombut
A presentation file for TIIM conference 2010 Pattaya Thailand,
ABSTRACT
In the age of social collaboration and sharing that enables by Web 2.0 and Linked Data, many organizations adapt themselves into advantages of interactive, sharing, reusing, interoperability and collaboration on World Wide Web. Organizational learning which is sub of knowledge management also greatly gains benefit from this emerging collaboration culture too. It provides abilities to share valuable insights, to reduce redundant work, to avoid reinventing the wheel, to reduce training time for new employees, to retain intellectual capital as employee turnover in an organization, and to adapt to changing environments and markets.
However, user created content from Web 2.0 multiplying with published structure of data according to Linked Data concept will be a massive amount of data. It is inevitable facing the overwhelming of data. Traditional knowledge management is not designed to extract knowledge from social collaboration. We need a framework that fit for knowledge transfer in highly interaction environment.
SECI model which is a knowledge management based on collaborative knowledge transfer in organization seem to be the best candidate for navigating knowledge creation in this case. This study attempts to address how to apply SECI model to knowledge management system in collaborative organization.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
Ontology-Oriented Inference-Based Learning Content Management System dannyijwest
The world is witnessing the electronic revolution in many fields of life such as health, education,
government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings
with it rapid change and greater opportunities to increase learning ability in colleges and schools. The
fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are
full of open source and commercial products, however LCMS systems in general inherit the drawbacks of
information system such as weakness in user expected information retrieval and semantic modelling and
searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the
Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the
constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an
organized fashion. This enables construction of a user-specific course, by semantic querying for topics of
interest. We present the development of an Ontology-oriented Inference-based Learning Content
Management System OILCMS, its architecture, conception and strengths.
Ontology-Oriented Inference-Based Learning Content Management System dannyijwest
The world is witnessing the electronic revolution in many fields of life such as health, education,
government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings
with it rapid change and greater opportunities to increase learning ability in colleges and schools. The
fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are
full of open source and commercial products, however LCMS systems in general inherit the drawbacks of
information system such as weakness in user expected information retrieval and semantic modelling and
searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the
Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the
constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an
organized fashion. This enables construction of a user-specific course, by semantic querying for topics of
interest. We present the development of an Ontology-oriented Inference-based Learning Content
Management System OILCMS, its architecture, conception and strengths.
The world is witnessing the electronic revolution in many fields of life such as health, education, government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings with it rapid change and greater opportunities to increase learning ability in colleges and schools. The fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are full of open source and commercial products, however LCMS systems in general inherit the drawbacks of information system such as weakness in user expected information retrieval and semantic modelling and searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an organized fashion. This enables construction of a user-specific course, by semantic querying for topics of interest. We present the development of an Ontology-oriented Inference-based Learning Content Management System OILCMS, its architecture, conception and strengths.
Discovering Resume Information using linked data dannyijwest
In spite of having different web applications to create and collect resumes, these web applications suffer
mainly from a common standard data model, data sharing, and data reusing. Though, different web
applications provide same quality of resume information, but internally there are many differences in terms
of data structure and storage which makes computer difficult to process and analyse the information from
different sources. The concept of Linked Data has enabled the web to share data among different data
sources and to discover any kind of information while resolving the issues like heterogeneity,
interoperability, and data reusing between different data sources and allowing machine process-able data
on the web.
Information residing in relational databases and delimited file systems are inadequate for reuse and sharing over the web. These file systems do not adhere to commonly set principles for maintaining data harmony. Due to these reasons, the resources have been suffering from lack of uniformity, heterogeneity as well as redundancy throughout the web. Ontologies have been widely used for solving such type of problems, as they help in extracting knowledge out of any information system. In this article, we focus on extracting concepts and their relations from a set of CSV files. These files are served as individual concepts and grouped into a particular domain, called the domain ontology. Furthermore, this domain ontology is used for capturing CSV data and represented in RDF format retaining links among files or concepts. Datatype and object properties are automatically detected from header fields. This reduces the task of user involvement in generating mapping files. The detail analysis has been performed on Baseball tabular data and the result shows a rich set of semantic information.
Similar to Digital venice rgrosso linked open metadata and ontologies (15)
Short presentation at Open knowledge Open Arts in Catania 26-27-28/2/2016 about entities seach by likeness in public administration metadata. Geolocalization of entities by mean of telegram bot: https://web.telegram.org/#/im?p=@geontobot
Esperimenti di estrazione e correlazione di concetti bis
Digital venice rgrosso linked open metadata and ontologies
1. It is said, in this presentation (http://www.slideshare.net/FernandaFaini/presentazione-open-gov-open-
data-forum-pa ) on page 23 and 24, that the "Linked Open Data (5 stars) have in the structure of the
dataset links to other datasets (Linked RDF). "
From here, in my opinion, you can 'leave and go further:
- Then categorize the linked open data with descriptive metadata of the published tables and fields, adding
the sixth star, which brings us to the level LOM (Linked Open Metadata)
- Then, with the use of lightweight ontologies, or conceptual schemes with hierarchies of entities
'relationships and infer knowledge for similarity in the descriptive metadata of tables and fields, thus
achieving' the seventh star that leads us to level LOMO (Linked Open Metadata ontologies)
To get to accomplish all of this in the public administration, a way can 'be the one described in my previous
article on this:
http://nelfuturo.com/wp-content/uploads/pdf/paper02-extended-draft.pdf