Challenges for the comprehensive management of cloud services in a PaaS framework

The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies

Applying mobile agents to E-business

[[abstract]]E-commerce and M-commerce can extend the marketing of a company or enterprise to unlimited region. Through Internet and WWW, the limitation of distance and region are broken for business behaviors. Agent technique is one of the important technologies developed to support the Internet applications. Especially, the Internet and WWW technologies broken the limitation of space, and the agent techniques solve the problems of temporality. Even if the users are off-line, the agents are still active in the world of computer network and play the roles that their users assigned. In this paper, a mechanism is proposed for E-marketplace based on agents and mobile agents. Some issues of research are discussed. They include the platform of mobile agents, the types and classifications of agents and mobile agents, behaviors of commerce transactions and processing models, negotiation mechanisms, etc. Moreover, they include the techniques of information retrieval, data mining, and knowledge base, etc. Based on the proposed mechanism of E-marketplace, the applications of E-commerce will be more effective, easier to develop, and more creating the marketing of business.[[notice]]補正完

Bayesian Neural Networks as a pricing model to reduce information costs in peer-to-peer online marketplaces

Treballs Finals del Grau d'Economia i Estadística. Doble titulació interuniversitària, Universitat de Barcelona i Universitat Politècnica de Catalunya. Curs: 2017-2018. Tutors: Xavier Puig Oriol; Salvador Torra Porras(eng) The main purpose of this thesis is to disclose the potential of exploiting the synergies between Statistical Science and Machine Learning. In particular, we propose a specific feed-forward Bayesian Neural Network (BNN) as a parametric statistical model able to both yield better punctual predictions than linear models and handle uncertainty through more grounded intervals than the ones offered by bootstrapping conventional Neural Networks. On top of proposing a complete methodology (based on DoE for architecture selection and MCMC to conduct inference) to apply BNNs in real cases, we analyze, using theoretical arguments from Microeconomics, the positive effect on society that it would have to use BNNs as pricing model for peer-to-peer online marketplaces and, moreover, we implement them for the case of Airbnb in Barcelona.(cat) El principal objectiu d’aquest treball consisteix en demostrar el potencial de combinar el coneixement de l’estadística i de l’aprenentatge automàtic (Machine Learning) per tal de proporcionar noves eines que permetin aprofitar les oportunitats que les Tecnologies de la Informació i Comunicació han generat en els darrers anys. Aquestes oportunitats es basen en la creació de tot un univers de dades que està esperant a ser analitzat i convertit en informació útil. En particular, aquest projecte es centra en les plataformes online de transaccions entre iguals (P2P OM) com ara Airbnb, Uber o Blablacar, ja que estant tenint un gran impacte en la nostra societat al modificar el procés mitjançant el qual adquirim béns i serveis. En aquest projecte, s’argumenta que, construint un model de predicció de preus mitjançant totes les transaccions realitzades en la plataforma, es podria proporcionar als usuaris oferents una eina de recomanació de preus per tal de determinar el preu de mercat del bé o servei que ofereixen, de forma ràpida i objectiva. L’objectiu és aconseguir que els oferents prenguin decisions més acurades sobre els preus, apropant-los, així, a les preferències de la demanda i incrementant el nombre de transaccions realitzades. Per tal de construir el model de predicció de preus es proposa treballar amb Xarxes Neuronals Bayesianes (BNN), amb l’objectiu d’oferir millors prediccions puntuals que el model lineal i, sobretot, intervals pel preu de mercat que realment capturin el comportament del mercat, cosa que les Xarxes Neuronals Artificials convencionals (ANN) tenen serioses dificultats per aconseguir-ho. Ara bé, i aquí és on es fan paleses les sinergies entre l’estadística i l’aprenentatge automàtic, en aquest projecte, a diferència del treball d’autors previs, es proposa les BNN com un model estadístic paramètric i, com a conseqüència, es desenvolupa tota una metodologia per tal de poder implementar-les en problemes aplicats, com ara el cas de les P2P OM. Aquesta metodologia es fonamenta en tres pilars principals que són: La manera d’implementar els mètodes MCMC per tal de capturar la multimodalitat inherent en BNN i com determinar que s’estan obtenint mostres de la a posteriori, l’ús de Disseny d’experiments en comptes de validació creuada per tal de determinar una arquitectura adient per la BNN i, finalment, el desenvolupament de tècniques pròpies i adaptació de aportacions d’altres autors per tal d’entendre com està funcionant la BNN. Finalment, s’implementa la BNN proposada d’acord amb la metodologia dissenyada pel cas de Airbnb a Barcelona, amb l’objectiu de demostrar tant el major rendiment i capacitats de la BNN respecte al model lineal i les ANN, com la utilitat que tindria un model de predicció de preus a l’hora d’ajudar els usuaris de les P2P OM a decidir un preu. A més a més, i fent ús de les tècniques d’interpretació de la BNN també s’observa com es poden extreure conclusions sobre el nivell de competència en cadascun dels barris de Barcelona i, a més a més, es pot explorar els efectes de canviar algunes característiques de l’apartament sobre el preu de mercat, cosa que pot ajudar als usuaris a decidir canvis i inversions

Next Generation Business Ecosystems: Engineering Decentralized Markets, Self-Sovereign Identities and Tokenization

Digital transformation research increasingly shifts from studying information systems within organizations towards adopting an ecosystem perspective, where multiple actors co-create value. While digital platforms have become a ubiquitous phenomenon in consumer-facing industries, organizations remain cautious about fully embracing the ecosystem concept and sharing data with external partners. Concerns about the market power of platform orchestrators and ongoing discussions on privacy, individual empowerment, and digital sovereignty further complicate the widespread adoption of business ecosystems, particularly in the European Union. In this context, technological innovations in Web3, including blockchain and other distributed ledger technologies, have emerged as potential catalysts for disrupting centralized gatekeepers and enabling a strategic shift towards user-centric, privacy-oriented next-generation business ecosystems. However, existing research efforts focus on decentralizing interactions through distributed network topologies and open protocols lack theoretical convergence, resulting in a fragmented and complex landscape that inadequately addresses the challenges organizations face when transitioning to an ecosystem strategy that harnesses the potential of disintermediation. To address these gaps and successfully engineer next-generation business ecosystems, a comprehensive approach is needed that encompasses the technical design, economic models, and socio-technical dynamics. This dissertation aims to contribute to this endeavor by exploring the implications of Web3 technologies on digital innovation and transformation paths. Drawing on a combination of qualitative and quantitative research, it makes three overarching contributions: First, a conceptual perspective on \u27tokenization\u27 in markets clarifies its ambiguity and provides a unified understanding of the role in ecosystems. This perspective includes frameworks on: (a) technological; (b) economic; and (c) governance aspects of tokenization. Second, a design perspective on \u27decentralized marketplaces\u27 highlights the need for an integrated understanding of micro-structures, business structures, and IT infrastructures in blockchain-enabled marketplaces. This perspective includes: (a) an explorative literature review on design factors; (b) case studies and insights from practitioners to develop requirements and design principles; and (c) a design science project with an interface design prototype of blockchain-enabled marketplaces. Third, an economic perspective on \u27self-sovereign identities\u27 (SSI) as micro-structural elements of decentralized markets. This perspective includes: (a) value creation mechanisms and business aspects of strategic alliances governing SSI ecosystems; (b) business model characteristics adopted by organizations leveraging SSI; and (c) business model archetypes and a framework for SSI ecosystem engineering efforts. The dissertation concludes by discussing limitations as well as outlining potential avenues for future research. These include, amongst others, exploring the challenges of ecosystem bootstrapping in the absence of intermediaries, examining the make-or-join decision in ecosystem emergence, addressing the multidimensional complexity of Web3-enabled ecosystems, investigating incentive mechanisms for inter-organizational collaboration, understanding the role of trust in decentralized environments, and exploring varying degrees of decentralization with potential transition pathways

Enhancing Agent Mediated Electronic Markets with Ontology Matching Services and Social Network Support

Os Mercados Eletrónicos atingiram uma complexidade e nível de sofisticação tão elevados, que tornaram inadequados os modelos de software convencionais. Estes mercados são caracterizados por serem abertos, dinâmicos e competitivos, e constituídos por várias entidades independentes e heterogéneas. Tais entidades desempenham os seus papéis de forma autónoma, seguindo os seus objetivos, reagindo às ocorrências do ambiente em que se inserem e interagindo umas com as outras. Esta realidade levou a que existisse por parte da comunidade científica um especial interesse no estudo da negociação automática executada por agentes de software [Zhang et al., 2011]. No entanto, a diversidade dos atores envolvidos pode levar à existência de diferentes conceptualizações das suas necessidades e capacidades dando origem a incompatibilidades semânticas, que podem prejudicar a negociação e impedir a ocorrência de transações que satisfaçam as partes envolvidas. Os novos mercados devem, assim, possuir mecanismos que lhes permitam exibir novas capacidades, nomeadamente a capacidade de auxiliar na comunicação entre os diferentes agentes. Pelo que, é defendido neste trabalho que os mercados devem oferecer serviços de ontologias que permitam facilitar a interoperabilidade entre os agentes. No entanto, os humanos tendem a ser relutantes em aceitar a conceptualização de outros, a não ser que sejam convencidos de que poderão conseguir um bom negócio. Neste contexto, a aplicação e exploração de relações capturadas em redes sociais pode resultar no estabelecimento de relações de confiança entre vendedores e consumidores, e ao mesmo tempo, conduzir a um aumento da eficiência da negociação e consequentemente na satisfação das partes envolvidas. O sistema AEMOS é uma plataforma de comércio eletrónico baseada em agentes que inclui serviços de ontologias, mais especificamente, serviços de alinhamento de ontologias, incluindo a recomendação de possíveis alinhamentos entre as ontologias dos parceiros de negociação. Este sistema inclui também uma componente baseada numa rede social, que é construída aplicando técnicas de análise de redes socias sobre informação recolhida pelo mercado, e que permite melhorar a recomendação de alinhamentos e auxiliar os agentes na sua escolha. Neste trabalho são apresentados o desenvolvimento e implementação do sistema AEMOS, mais concretamente: • É proposto um novo modelo para comércio eletrónico baseado em agentes que disponibiliza serviços de ontologias; • Adicionalmente propõem-se o uso de redes sociais emergentes para captar e explorar informação sobre relações entre os diferentes parceiros de negócio; • É definida e implementada uma componente de serviços de ontologias que é capaz de: • o Sugerir alinhamentos entre ontologias para pares de agentes; • o Traduzir mensagens escritas de acordo com uma ontologia em mensagens escritas de acordo com outra, utilizando alinhamentos previamente aprovados; • o Melhorar os seus próprios serviços recorrendo às funcionalidades disponibilizadas pela componente de redes sociais; • É definida e implementada uma componente de redes sociais que: • o É capaz de construir e gerir um grafo de relações de proximidade entre agentes, e de relações de adequação de alinhamentos a agentes, tendo em conta os perfis, comportamento e interação dos agentes, bem como a cobertura e utilização dos alinhamentos; • o Explora e adapta técnicas e algoritmos de análise de redes sociais às várias fases dos processos do mercado eletrónico. A implementação e experimentação do modelo proposto demonstra como a colaboração entre os diferentes agentes pode ser vantajosa na melhoria do desempenho do sistema e como a inclusão e combinação de serviços de ontologias e redes sociais se reflete na eficiência da negociação de transações e na dinâmica do mercado como um todo.In electronic commerce, the diversity of the involved actors can lead to different conceptualizations of their needs and capabilities, giving rise to semantic incompatibilities that might hamper negotiations and the fulfilling of satisfactory transactions. In order to provide help in conversation among different actors, markets must offer ontology services to facilitate interoperability. However, humans tend to be reluctant to accept others’ conceptualizations, except if they become convinced that a good deal can be achieved. In this context, the application and exploitation of relationships captured by social networks can result in the establishment of more accurate trust relationships between businesses and customers, as well as the improvement of the negotiation efficiency and therefore the users’ satisfaction with the electronic commerce system. The AEMOS system is an agent-based electronic commerce platform that provides ontology matching services in order to facilitate the interoperability between agents that use different ontologies. AEMOS also includes a social network component that allows improving the ontology alignment recommendations and supporting the agents’ decisions about which alignments to select based on the information collected throughout the market and by exploring social network analysis techniques. This work presents the development and implementation of the AEMOS system, illustrating how the collaboration between the different agents can be helpful in improving the system’s performance, and how the inclusion and combination of ontology services and social networks reflects in the efficiency of the negotiation process

Bayesian Neural Networks as a pricing model to reduce information costs in peer-to-peer online marketplaces

The main purpose of this thesis is to disclose the potential of exploiting the synergies between Statistical Science and Machine Learning. In particular, we propose a specific feed-forward Bayesian Neural Network (BNN) as a parametric statistical model able to both yield better punctual predictions than linear models and handle uncertainty through more grounded intervals than the ones offered by bootstrapping conventional Neural Networks. On top of proposing a complete methodology (based on DoE for architecture selection and MCMC to conduct inference) to apply BNNs in real cases, we analyze, using theoretical arguments from Microeconomics, the positive effect on society that it would have to use BNNs as pricing model for peer-to-peer online marketplaces and, moreover, we implement them for the case of Airbnb in Barcelon

A Hybrid Simulation Framework of Consumer-to-Consumer Ecommerce Space

In the past decade, ecommerce transformed the business models of many organizations. Information Technology leveled the playing field for new participants, who were capable of causing disruptive changes in every industry. Web 2.0 or Social Web further redefined ways users enlist for services. It is now easy to be influenced to make choices of services based on recommendations of friends and popularity amongst peers. This research proposes a simulation framework to investigate how actions of stakeholders at this level of complexity affect system performance as well as the dynamics that exist between different models using concepts from the fields of operations engineering, engineering management, and multi-model simulation. Viewing this complex model from a systems perspective calls for the integration of different levels of behaviors. Complex interactions exist among stakeholders, the environment and available technology. The presence of continuous and discrete behaviors coupled with stochastic and deterministic behaviors present challenges for using standalone simulation tools to simulate the business model. We propose a framework that takes into account dynamic system complexity and risk from a hybrid paradigm. The SCOR model is employed to map the business processes and it is implemented using agent based simulation and system dynamics. By combining system dynamics at the strategy level with agent based models of consumer behaviors, an accurate yet efficient representation of the business model that makes for sound basis of decision making can be achieved to maximize stakeholders\u27 utility

Blockchain-based reputation models for e-commerce: a systematic literature review

The Digital Age is the present, and nobody can deny that. With it has come a digital transformation in various sectors of activity, and e-commerce is no exception. Over the last few decades, there has been a massive increase in its utilization rates, as it has several advantages over traditional commerce. At the same time, the rise in the number of crimes on the Internet and, consequently, the understanding of the risks involved in online shopping has led consumers to become more cautious, looking for information about the seller and taking it into account when making a purchase decision. The need to get to know the merchant better before making a purchase decision has encouraged the creation of reputation systems, whose services play an essential role in today's e-commerce context. Reputation systems act as mechanisms to reduce information asymmetry between consumers and sellers and establish rankings that attest to fulfilling standards and policies considered necessary for shops operating in the digital market. The critical problems in current reputation systems are the frauds and attacks that such systems currently have to deal with, which results in a lack of trust between users. These security and fraud issues are critical because users' trust is commonly based on reputation models, and many of these current systems are not immune to them, thus compromising e-commerce growth. The need for a better and safer model emerges with the development of e-commerce. Through reading the articles and pursuing the answers to the primary questions, blockchain is data register technology to be analysed in order to gain a better acknowledgment of the potential of such technology. More research work and investigation must be done to fully understand how to create a more assertive reputation model. Thus, this study systematizes the knowledge generated by reputation models in E-commerce studies in Scopus, WoS databases, and Google Scholar, using PRISMA methodology. A systematic approach was adopted in conducting a literature review. The need for a systematic literature review came from the knowledge that there are reputation systems that mitigate some of the problems. In addition to identifying some indicators used in reputation models, we also conclude that these models could help provide some insurance to buyers and sellers, with a commitment to being a problem solver, being able to mitigate known problems such as Collusion, Sybil attacks, laundering attacks, and preventing online fraud ranging from ballot stuffing and bad-mouthing. Nevertheless, the results of the present work demonstrate that even though these reputation models still cannot solve all of the problems, attacking one fraud opens the door to an attack. The architecture of the models was identified, with the realization that a few lacks that need to be fulfilled