Game in the Game: Examining In-App Advertising in Mobile Sports Games

The purpose of this study is to examine in-app advertisement within mobile games and the content of the advertisement. The universe of this study is sports game applications under games category in Apple Store. The sample is 32 sports games that were selected by random sampling method among popular games listed under sports category in Apple Store. Determined advertisements were analysed with the content analysis method. As a result of the research; it has been determined that sports games that are examined under mobile applications, primarily contain in-app advertisements for strategy, shopping, casino and bank applications, and games. Accordingly, more than half of the ads in evaluated mobile sports games were about mobile games and in-apps about mobile sports games were less. When in-app games were investigated in terms of appearance on screen, the great majority of mobile sport game ads were interstitial ads. As a conclusion; in spite of the changes in entertainment concept as a result of the developments in mobile technology, it has been determined that sport is used as direct or indirect marketing material as it is in the approach of traditional sports marketing. The implications from this research include a better understanding of how in-app advertising is being used to sports marketing and marketing communication.

Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research

A Multidisciplinary Perspective of Big Data in Management Research

In recent years, big data has emerged as one of the prominent buzzwords in business and management. In spite of the mounting body of research on big data across the social science disciplines, scholars have offered little synthesis on the current state of knowledge. To take stock of academic research that contributes to the big data revolution, this paper tracks scholarly work's perspectives on big data in the management domain over the past decade. We identify key themes emerging in management studies and develop an integrated framework to link the multiple streams of research in fields of organisation, operations, marketing, information management and other relevant areas. Our analysis uncovers a growing awareness of big data's business values and managerial changes led by data-driven approach. Stemming from the review is the suggestion for research that both structured and unstructured big data should be harnessed to advance understanding of big data value in informing organisational decisions and enhancing firm competitiveness. To discover the full value, firms need to formulate and implement a data-driven strategy. In light of these, the study identifies and outlines the implications and directions for future research

Evolutionary Service Composition and Personalization Ecosystem for Elderly Care

Current demographic trends suggest that people are living longer, while the ageing process entails many necessities, calling for care services tailored to the individual senior’s needs and life style. Personalized provision of care services usually involves a number of stakeholders, including relatives, friends, caregivers, professional assistance organizations, enterprises, and other support entities. Traditional Information and Communication Technology based care and assistance services for the elderly have been mainly focused on the development of isolated and generic services, considering a single service provider, and excessively featuring a techno-centric approach. In contrast, advances on collaborative networks for elderly care suggest the integration of services from multiple providers, encouraging collaboration as a way to provide better personalized services. This approach requires a support system to manage the personalization process and allow ranking the {service, provider} pairs. An additional issue is the problem of service evolution, as individual’s care needs are not static over time. Consequently, the care services need to evolve accordingly to keep the elderly’s requirements satisfied. In accordance with these requirements, an Elderly Care Ecosystem (ECE) framework, a Service Composition and Personalization Environment (SCoPE), and a Service Evolution Environment (SEvol) are proposed. The ECE framework provides the context for the personalization and evolution methods. The SCoPE method is based on the match between the customer´s profile and the available {service, provider} pairs to identify suitable services and corresponding providers to attend the needs. SEvol is a method to build an adaptive and evolutionary system based on the MAPE-K methodology supporting the solution evolution to cope with the elderly's new life stages. To demonstrate the feasibility, utility and applicability of SCoPE and SEvol, a number of methods and algorithms are presented, and illustrative scenarios are introduced in which {service, provider} pairs are ranked based on a multidimensional assessment method. Composition strategies are based on customer’s profile and requirements, and the evolutionary solution is determined considering customer’s inputs and evolution plans. For the ECE evaluation process the following steps are adopted: (i) feature selection and software prototype development; (ii) detailing the ECE framework validation based on applicability and utility parameters; (iii) development of a case study illustrating a typical scenario involving an elderly and her care needs; and (iv) performing a survey based on a modified version of the technology acceptance model (TAM), considering three contexts: Technological, Organizational and Collaborative environment

Remote online machine condition monitoring using advanced internet, wireless and mobile communication technologies

A conceptual model with wireless and mobile techniques is developed in this thesis for remote real-time condition monitoring, which is applied for monitoring, diagnosing, and controlling the working conditions of machines. The model has the following major functions: data acquisition, data processing, decision making, and remote communication. The data acquisition module is built up within this model using the sensory technique and data I/O interfaces to acquire the working conditions data of a machine and extract the physical information about the machine (e.g. failure, wear, etc.) for data processing and decision making. The data processing is conducted using digital conversion and feature extraction to process the received analogue condition data and convert the data into the physical quantities of working condition of the machine for sequent fault diagnosis. A real-time fault diagnostic scheme for decision-making is applied based on digital filtering and pattern classification to real-time identify the fault symptom of the machine and provide advice for decision making for maintenance. Process control is implemented to control the operation status of the machine automatically, inform the maintenance personnel diagnostic results and alert the working conditions of the machine. Remote communication with wireless and mobile features greatly advance the machine’s condition monitoring technology with real-time fault diagnostic capacity, by providing a wireless-based platform to enable the implementation of data acquisition, real-time fault diagnosis, and decision making through the Internet, wireless, and mobile phone network. The model integrating above techniques and methods has been applied into the following three areas: (1) Development of a Remote Real-time Condition Monitoring System of Industrial Gearbox, supported by the Stimulation Innovation Success programme (2007-2008); (2) Development of a Remote Control System of Solid Desiccant Dehumidifier for Air Conditioning in Low Carbon Emission Buildings, supported by the Sustainable Construction iNET programme (2009-2010); (3) Development of an Innovative Remote Monitoring System of Thermo-Electric-Generations, supported by the Sustainable Construction iNET programme (2010-2011). The combination of wireless and mobile techniques with data acquisition, real-time fault diagnosis, and decision-making, into a model for remote real-time condition monitoring is a novel contribution to this area

Adaptation de services dans un espace intelligent sensible au contexte

Grâce à l’apparition des paradigmes de l’intelligence ambiante, on assiste à l’émergence de nouveaux systèmes intelligents ambiants visant à créer et gérer des environnements intelligents d’une façon intuitive et transparente. Ces environnements sont des espaces intelligents caractérisés notamment par l’ouverture, l’hétérogénéité, l’incertitude et la dynamique des entités qui les constituent. Ces caractéristiques soulèvent ainsi des defies scientifiques considérables pour la conception et la mise en place d’un système intelligent adéquat. Ces défis sont principalement au nombre de trois : l’abstraction et la gestion du contexte, la sensibilité au contexte et l’auto-adaptation face aux changements imprévisibles qui peuvent se produire dans un environnement ambiant. Dans cette thèse, nous avons proposé une architecture d’un système intelligent capable d’adapter les services selon les besoins des utilisateurs en tenant compte, d’une part, du contexte environnemental et de ses différents équipements et d’autre part, des besoins variables exprimés par les utilisateurs. Ce système est construit suivant un modèle sensible au contexte, adaptatif et réactif aux évènements. Il se repose sur des entités modulaires de faible couplage et de forte cohésion lui permettant d’être flexible et efficace. Ce système integer également un module d’adaptation de services afin de repérer le contexte et de l’ajuster dynamiquement suivant les attentes des utilisateurs. Cette adaptation est réalisée via deux algorithmes : le premier est un algorithme par renforcement (Q-learning), le deuxième est un algorithme supervisé (CBR). L’hybridation de ces deux algorithmes permet surmonter les inconvénients de Q-learning pour aboutir à une nouvelle approche capable de gérer le contexte, sélectionner et adapter le service

Sistema multi-agente basado en contexto, localización y reputación para dominios de inteligencia artificial

Las investigaciones en Inteligencia Ambiental (denominada también Computación Ubicua) utilizando tecnologías inalámbricas han crecido en los últimos años a pasos agigantados. El término Inteligencia Ambiental es un nuevo concepto adoptado para hacer referencia a entornos inteligentes, en donde la computadora y los dispositivos móviles asisten al usuario en sus actividades cotidianas. Son entornos con un gran despliegue de diferentes tecnologías, invisibles al usuario, que hacen posible la provisión de servicios personalizados. Los agentes inteligentes son un paradigma especial dentro de la Inteligencia Ambiental. La principal contribución de este trabajo consiste en el diseño metodológico de un sistema basado en contexto (incluyendo servicios de localización) utilizando agentes inteligentes, que da soporte a las necesidades de información de estas nuevas tecnologías, para dominios heterogéneos. Los agentes facilitan la comunicación y las interacciones entre los usuarios del sistema. Para hacer factible este intercambio de mensajes, es necesario contar con una ontología, la cual posibilita, no tan solo la comunicación entre los agentes intervinientes, sino que además, permite a los agentes razonar sobre el contexto en el que se sitúan. En este sentido, el uso de información geográfica permite inferir información de alto nivel sobre dónde se encuentra un agente. Las preferencias de los usuarios permiten seleccionar los mejores servicios en cada escenario de Inteligencia Ambiental. Esta personalización es particularmente adecuada en los sistemas multiagentes con capacidades de aprendizaje. En base a esta concepción, se aborda la adquisición de perfiles de usuarios, de forma no intrusiva, a través de técnicas de computación evolutiva. La adquisición de las preferencias del usuario hacen posible que se le presente al usuario una lista de actividades priorizada por realizar, según el dominio en el que se encuentre a efectos de maximizar factores que le sean relevantes. Además, en base a estos perfiles, los agentes proveedores realizan sus ofertas. Finalmente, las interacciones usuarios-proveedores en un dominio dinámico, requieren de cierto grado de confianza, el cual se adquiere a través del uso de sistemas de confianza y reputación. En este sentido, se presenta CALoR, un modelo de reputación basado en contexto y localización, que permite a los agentes usuarios del sistema realizar recomendaciones a otros agentes usuarios, considerando sus experiencias pasadas, y también aspectos espacio-temporales. Se asume que la recomendación enviada por un agente usuario sobre un agente proveedor es más fiable cuando han interactuado recientemente, y el agente usuario ha podido evaluar el servicio provisto a una distancia considerablemente corta. Resumiendo, en las páginas siguientes, se presentará al lector, una arquitectura multi-agente basada en contexto y localización para dominios heterogéneos de inteligencia ambiental. Se explicará en detalle la construcción del perfil de usuario utilizando algoritmos genéticos para la provisión de servicios personalizados, y se presentará el modelo de reputación CALoR para afianzar las relaciones entre los agentes de la arquitectura. Por cada módulo, se presentan los experimentos realizados y los resultados obtenidos, que demuestran la robustez de la propuesta global. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Researches on Ambient Intelligent (also known as Ubiquitous Computing) using wireless technologies have increased over the last few years. Ambient Intelligence is a new concept adopted for referencing an intelligent environment, where the user is assist by the computer and his mobile device wherever he is. Several technologies are necessaries in these environments to provide personalized services. All immersed devices are invisibles to the user. Ambient Intelligence is an ideal workspace for agents because of autonomous distributed and proactive agent nature. The main contribution of this Thesis consists of designing a methodological approach a Context Aware System (involving location services) using Agents that can be used in very different domains. We review several scenarios to define a multi-agent architecture, that support the information needs of these new technologies, that will be used in heterogeneous domain. We use ontology to facilitate the communication between agents. Furthermore, this ontology allows agent’s reasoning. In this manner, the use of geographic information allows to infer high level information about the real place where the agent is. In Ambient Intelligence scenarios, user preferences allow to select the best services for each user. This personalization is particularly suitable in a multi-agent system with learning capabilities. Based on this, and on the improvement of the mobile technologies, we developed an algorithm that allows learning the user profile. A list of prioritized services would therefore be presented to the user that takes into account user interests. Also, the providers agents could make their offers. Finally, users-providers interactions that take place in a dynamical domain needs a certain trust. Trust is build using trust and reputation systems. In this manner, we present CALoR reputation model that is a reputation model based-on context and location. This model allows the user agents of the systems to make recommendations from each other. CALoR take into account the past interactions of the agents and their spatial-temporal issues. We assume that a recommendation send by a user agent over a provider agent is more reliable when the agents interact closely and recently. In summary, the reader will found in next pages a multi-agent system based-on context and location for heterogeneous domain in ambient intelligence. We explain the detail of the user profile acquisition using genetic algorithms. And we present CALoR system to consolidate the relationship between agents. For each item, we expose our experiments and the obtained results, that validates this approach