PMR: Personalized Mobile Restaurant system

Abstract

Personalized mobile applications involve deploying the existing technologies to accommodate the differences between individuals. The existing applications have undertaken with the price and the location factors in mind. They show the same results regardless the personal special needs of consumers. The realization of the next generation of personalized systems will be a challenging task. For our research purpose, the personal special needs are defined as: (Religions, Cultures, Allergies, Health Condition, Diet, Preferences and Dislikes) needs that affect many aspects of a consumer life. In general there is no shortage of the restaurant apps available in the App Store, However, many of these apps are based on regular factors such as price and location. They are not a great choice for consumers with personal special needs. This paper proposes a novel "Personalized Mobile Restaurant" PMR system. To meet the strict needs of consumers; a requirement driven design approach was followed. The proposed system was tested successfully and promising results were obtained.

A small world based overlay network for improving dynamic load-balancing

“…As the usefulness of any load-balancing algorithm is directly dependent on the quality of its load measurement and the efficiency of being applied to solve practical problems, each approach (our approach, the nearest neighbor algorithm, and the original neighborhood algorithm) was separately applied to a personalized m-cafeteria system (Daraghmi and Yuan, 2013), and the run-time behavior of each algorithm was investigated. The three approaches were run on a set of default values: number of assigned workloads, number of nodes, maximum cluster size, and the average number of the functions executed per node.…”

A food allergy risk detection model based on situation awareness

“…Universidade do Vale do Rio dos Sinos Av. Unisinos,950,Cristo Rei,São Leopoldo,RS,Brasil nquevedo@hotmail.com,cac@unisinos.br,rrrighi@unisinos.br,rigo@unisinos.br gestion of restaurants (Daraghmi and Yuan, 2013), daily food intake (Henricksen and Viller, 2012) and selection of menu restrictions for a safe diet (Iizuka and Okawada, 2012). However, none of the considered models provides support ubiquitously for users who suffer from food allergies.…”

“…PMR (Daraghmi and Yuan, 2013), Personalized Mobile Restaurant System, proposes a model that supports the choice of restaurants by consumers with different profiles (Religions, Cultures, Allergies, Health Condition, Diet, Preferences and Dislikes). The model presents location, profile and nutrition contexts, provides recommendations (restaurants/dishes/ingredients), allows Web and mobile device interactions, ordering of a meal and remote configuration.…”

Um Modelo Ubíquo de Detecção de Riscos de Alergia Baseado em Ciência de Situação

“…PMR (Daraghmi e Yuan, 2013), Sistema de Restaurante Móvel Personalizado, propõe um modelo que dá suporte na escolha de restaurante a consumidores com diferentes perfis (religiões, culturas, alergias alimentares, condições de saúde, doenças crônicas, dieta especial, preferências e aversões). O modelo tem contexto de localização/perfil/nutricional, fornece recomendação(restaurantes/pratos/ingredientes) e possui interações Web/dispositivos móveis, pedidos de refeições e configuração remota.…”

“…Existem modelos que fornecem cuidados ubíquos tais como planejamento alimentar (Antoniou e Nanou, 2003), recomendação evitando alimentos calóricos (Johnson, Vergara e Doll, 2014), sugestão de restaurante (Daraghmi e Yuan, 2013), diário dos alimentos ingeridos (Henricksen e Viller, 2012) e seleção de menus com restrições para uma dieta segura (Iizuka e Okawada, 2012). Porém nenhum dos modelos estudados provê suporte de maneira ubíqua a usuários que sofrem de alergia alimentar.…”