Nowadays, companies all over the world use Social Media tools in order to advertise and promote their products, services and themselves. They are using them mostly for marketing purposes. This research explores whether the usage of Social Media in Greek retail businesses is a resource development or risk of increased competition or both or none of them. In addition, it explores the usage of Social Media in Greek retail businesses currently and the strengths, weaknesses, opportunities and threats in using Social Media in Greek retail businesses, as well as the future use of Social Media. It also investigates the failure of companies to use the integrated capabilities of multiple Social Media in order to conduct several business functions in the Greek retail sector. Moreover, this study addresses the way that case company manages the information given by customers through Social Media. Extensive literature search revealed that almost no research has been conducted earlier on the aforementioned issues in the context of Greek retail businesses. Furthermore, this research has been undertaken because Social Media is a contemporary topic, which always develops rapidly and affects the sales of products, services and the attraction of customers by a company. This study has been conducted by reviewing the current literature on the topics, observing customers on Facebook and Instagram pages of the case company, interviewing experts from the case company and analysing the empirical data. The findings contribute to the identification of strengths, weaknesses, opportunities and threats of Social Media in Greek retail businesses. In addition, it highlights the usefulness of Social Media for management tasks by company managers and information and knowledge management for companies. The findings of the study can be useful to retail companies in Greek and other countries about how they can significantly benefit from the usage of Social Media in a variety of ways by avoiding the risks.

This master's thesis deals with an assessment of specific information system of the Red Hat Inc. company, and consequent suggestions of potential changes. The thesis is divided into three parts. The introductory one is focused on theoretical knowledge which together with performed analysis has become a fundamental base for the system assessment and suggestions of changes leading to process improvements.

Nous basant sur plusieurs études de cas effectuées au sein de communautés R&D virtuelles d'Alstom Power Hydro, nous démontrons d'une part, qu'un forum peut soutenir différents types d'intéractions allant de la transmission d'informations à la co-construction de connaissances et co-production de solution. Opérationnalisant et améliorant des grilles scientifiques visant à caractériser des communautés de pratiques virtuelles, nous démontrons aussi, qu'il existe un lien entre la configuration d'une communauté et le type de ses intéractions en ligne. Nous démontrons qu’il existe une configuration optimale, de communautés de pratiques virtuelles appliquées à la R&D, qui garantit des intéractions de type co-construction de connaissance et co-production de solution entre ses membres. A l'heure où Microsoft équipe chaque jour 20000 nouveaux utilisateurs de l'application Share point, cette thèse prend tout sens. En opérationnalisant une méthode d'évaluation des communautés de pratiques virtuelles, et en apportant des conseils pour déployer un forum appliqué à la R&D, nous accompagnons tout projet de création de communauté R&D virtuelle et/ou d'instrumentation de ses intéractions par un forum. / In this dissertation, we explore the potential of a forum to support collaboration and knowledge sharing among Virtual Communities of practice. We thus propose a coding scheme based on the Rainbow model and test it in order to analyze the content of two forums of R&D VcoP. We demonstrate that a forum supports asynchronous argumentative activities and thus enhances global collaboration and knowledge sharing among R&D VcoP members. We then propose an enriched model based on the work of Line Dube and tested it to characterize the R&D VcoP studied. We prove that the community configuration has a direct impact on the online dynamic of the community. We point out the main factors that play a key role in fostering online collaboration and knowledge sharing between R&D Virtual community members.

Submitted by Clebson Anjos (clebson.leandro54@gmail.com) on 2015-05-07T18:27:39Z No. of bitstreams: 1 arquivototal.pdf: 3587950 bytes, checksum: 38dd5cef79d4e1983ebff9852b061940 (MD5) / Made available in DSpace on 2015-05-07T18:27:39Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 3587950 bytes, checksum: 38dd5cef79d4e1983ebff9852b061940 (MD5) Previous issue date: 2015-02-23 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This work develops a case study about the safe discard of digital archival documents present in lawsuits within the Labor Justice of the state of Paraíba through the system entitled "Sistema Unificado de Administração de Processos (SUAP)". The SUAP is an information system that aims to quicken the Labor Justice of Paraíba since it uses Information and Communication Technologies to achieve that goal. After filing a lawsuit - whether in digital or physical media and respecting the table of temporality - it must be fully eliminated (discarded) so as to prevent the subsequent recovery of the information contained therein and thus preserving the confidentiality of such information. The safe discard of digital information differs from the discard of physical information because it requires software applications, procedures and/or mechanisms to ensure that the information stored in digital devices becomes unrecoverable. This research aims to study procedures for the proper disposal of digital archival documents present in lawsuits, is classified as qualitative, has its data collection implemented empirically and performed by laboratory tests of notes, focus group technique and on- line questionnaire, using discourse analysis for the consolidation of the data collected. As a result, mechanisms and software are appointed to enable the safe disposal of digital archival documents, SUAP improvements and the mapping of the organization's processes, besides the disposal model of digital archival documents, which takes into account the characteristics of the TRT- PB and the types of available computer media, based on the literature and analyzed data. / Este trabalho desenvolve um estudo de caso sobre o descarte seguro de documentos arquivísticos digitais presentes em ações judiciais no âmbito da Justiça Trabalhista Paraibana, mediante a utilização do sistema intitulado “Sistema Unificado de Administração de Processos (SUAP)”. O SUAP consiste em um sistema de informação que tem por objetivo dar celeridade à Justiça Trabalhista Paraibana, posto que se utiliza das Tecnologias da Informação e Comunicação para alcançar tal objetivo. Após o arquivamento de uma ação judicial – seja ela em suporte digital ou físico e respeitada a tabela de temporalidade –, os autos respectivos devem ser eliminados totalmente (descarte), de forma que impossibilite a recuperação posterior das informações ali contidas, de modo que reste preservada, assim, a confidencialidade da informação. O descarte seguro de documentos digitais difere do descarte em suporte físico, pois necessita de aplicativos de software, procedimentos, e/ou mecanismos que assegurem a irrecuperabilidade da informação armazenada nos dispositivos digitais. Esta pesquisa tem, como objetivo, estudar os procedimentos que permitam a correta eliminação de documentos arquivísticos digitais presentes em ações judiciais, a qual é classificada como qualitativa, cuja coleta de dados é implementada de forma empírica e realizada mediante anotações de testes de laboratório, técnica de grupo focal e questionário on-line, o qual se utiliza da análise do discurso para a consolidação dos dados coletados. Como resultado, são apontados mecanismos e softwares que permitam o descarte seguro de documentos arquivísticos digitais, melhorias no SUAP e a necessidade de um mapeamento dos processos da organização, além de um modelo de descarte de documentos arquivísticos em suporte digital, que leva em conta as características do TRT-PB e os tipos de mídias informáticas disponíveis, fundamentadas na literatura e nos dados analisados.

Music is perceived and described very subjectively by every individual. Nowadays, people often get lost in their steadily growing, multi-placed, digital music collection. Existing music player and management applications get in trouble when dealing with poor metadata that is predominant in personal music collections. There are several music information services available that assist users by providing tools for precisely organising their music collection, or for presenting them new insights into their own music library and listening habits. However, it is still not the case that music consumers can seamlessly interact with all these auxiliary services directly from the place where they access their music individually. To profit from the manifold music and music-related knowledge that is or can be available via various information services, this information has to be gathered up, semantically federated, and integrated into a uniform knowledge base that can personalised represent this data in an appropriate visualisation to the users. This personalised semantic aggregation of music metadata from several sources is the gist of this thesis. The outlined solution particularly concentrates on users’ needs regarding music collection management which can strongly alternate between single human beings. The author’s proposal, the personal music knowledge base (PMKB), consists of a client-server architecture with uniform communication endpoints and an ontological knowledge representation model format that is able to represent the versatile information of its use cases. The PMKB concept is appropriate to cover the complete information flow life cycle, including the processes of user account initialisation, information service choice, individual information extraction, and proactive update notification. The PMKB implementation makes use of SemanticWeb technologies. Particularly the knowledge representation part of the PMKB vision is explained in this work. Several new Semantic Web ontologies are defined or existing ones are massively modified to meet the requirements of a personalised semantic federation of music and music-related data for managing personal music collections. The outcome is, amongst others, • a new vocabulary for describing the play back domain, • another one for representing information service categorisations and quality ratings, and • one that unites the beneficial parts of the existing advanced user modelling ontologies. The introduced vocabularies can be perfectly utilised in conjunction with the existing Music Ontology framework. Some RDFizers that also make use of the outlined ontologies in their mapping definitions, illustrate the fitness in practise of these specifications. A social evaluation method is applied to carry out an examination dealing with the reutilisation, application and feedback of the vocabularies that are explained in this work. This analysis shows that it is a good practise to properly publish Semantic Web ontologies with the help of some Linked Data principles and further basic SEO techniques to easily reach the searching audience, to avoid duplicates of such KR specifications, and, last but not least, to directly establish a \"shared understanding\". Due to their project-independence, the proposed vocabularies can be deployed in every knowledge representation model that needs their knowledge representation capacities. This thesis added its value to make the vision of a personal music knowledge base come true.:1 Introduction and Background 11 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2 Personal Music Collection Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 Music Information Management 17 2.1 Knowledge Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.1 Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.1.1.1 Knowledge Representation Models . . . . . . . . . . . . . . . . . 18 2.1.1.2 Semantic Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.1.1.3 Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.2 Knowledge Management Systems . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.2.1 Information Services . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.2.2 Ontology-based Distributed Knowledge Management Systems . . 20 2.1.2.3 Knowledge Management System Design Guideline . . . . . . . . 21 2.1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2 Semantic Web Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.1 The Evolution of the World Wide Web . . . . . . . . . . . . . . . . . . . . . 22 Personal Music Knowledge Base Contents 2.2.1.1 The Hypertext Web . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.1.2 The Normative Principles of Web Architecture . . . . . . . . . . . 23 2.2.1.3 The Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2.2 Common Semantic Web Knowledge Representation Languages . . . . . . 25 2.2.3 Resource Description Levels and their Relations . . . . . . . . . . . . . . . 26 2.2.4 Semantic Web Knowledge Representation Models . . . . . . . . . . . . . . 29 2.2.4.1 Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.2.4.2 Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.2.4.3 Context Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.2.4.4 Storing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.2.4.5 Providing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2.4.6 Consuming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.3 Music Content and Context Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.3.1 Categories of Musical Characteristics . . . . . . . . . . . . . . . . . . . . . 37 2.3.2 Music Metadata Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.3.3 Music Metadata Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.3.3.1 Audio Signal Carrier Indexing Services . . . . . . . . . . . . . . . . 41 2.3.3.2 Music Recommendation and Discovery Services . . . . . . . . . . 42 2.3.3.3 Music Content and Context Analysis Services . . . . . . . . . . . 43 2.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.4 Personalisation and Environmental Context . . . . . . . . . . . . . . . . . . . . . . 44 2.4.1 User Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.4.2 Context Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.4.3 Stereotype Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3 The Personal Music Knowledge Base 48 3.1 Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.1 Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.2 Knowledge Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 User Account Initialisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.2 Individual Information Extraction . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.3 Information Service Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.3.4 Proactive Update Notification . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3.5 Information Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3.6 Personal Associations and Context . . . . . . . . . . . . . . . . . . . . . . . 56 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4 A Personal Music Knowledge Base 57 4.1 Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.1.1 The Info Service Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.1.2 The Play Back Ontology and related Ontologies . . . . . . . . . . . . . . . . 61 4.1.2.1 The Ordered List Ontology . . . . . . . . . . . . . . . . . . . . . . 61 4.1.2.2 The Counter Ontology . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.2.3 The Association Ontology . . . . . . . . . . . . . . . . . . . . . . . 64 4.1.2.4 The Play Back Ontology . . . . . . . . . . . . . . . . . . . . . . . . 65 4.1.3 The Recommendation Ontology . . . . . . . . . . . . . . . . . . . . . . . . 69 4.1.4 The Cognitive Characteristics Ontology and related Vocabularies . . . . . . 72 4.1.4.1 The Weighting Ontology . . . . . . . . . . . . . . . . . . . . . . . 72 4.1.4.2 The Cognitive Characteristics Ontology . . . . . . . . . . . . . . . 73 4.1.4.3 The Property Reification Vocabulary . . . . . . . . . . . . . . . . . 78 4.1.5 The Media Types Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.2 Knowledge Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5 Personal Music Knowledge Base in Practice 87 5.1 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1.1 AudioScrobbler RDF Service . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1.2 PMKB ID3 Tag Extractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.2.1 Reutilisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.2.2 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.3 Reviews and Mentions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.4 Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6 Conclusion and Future Work 93 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94