Visual analysis of sensor logs in smart spaces: Activities vs. situations

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Learning frequent behaviours of the users in intelligent environments

Intelligent Environments (IEs) are expected to support people in their daily lives. One of the hidden assumptions in IEs is that they propose a change ofu perspective in the relationships between humans and technology, shifting from a techno-centered perspective to a human-centered one. Unlike current computing systems where the user has to learn how to use the technology, an IE adapts its behaviour to the user, even anticipating his/her needs, preferences or habits. For that, the environment should learn how to react to the actions and needs of the users, and this should be achieved in an unobtrusive and transparent way. In order to provide personalized and adapted services, it is clear the need of knowing preferences and frequent habits of users. Thus, the ability to learn patterns of behaviour becomes an essential aspect for the successful implementation of IEs. In that sense, a perfect learning system would gain knowledge about everything related to users that would help the environment act intelligently and proactively. The efforts in this research work are focused on discovering frequent behaviours of the users. For that, it has been designed and developed the Learning Frequent Patterns of User Behaviour System (LFPUBS) that, taking into account all the particularities of IEs, learns frequent behaviours of the users. The core of the LFPUBS is the Learning Layer that unlike some other components is independent of the particular environment in which the system is being applied. On the one hand, it includes a language that allows the representation of discovered behaviours in a clear and unambiguous way. On the other hand, coupled with the language, an algorithm that discovers frequent behaviours has been designed and implemented. Finally, LFPUBS was validated using data collected from two real environments. Results obtained in such validation tests showed that LFPUBS was able to discover frequent behaviours of the users. Moreover, some improvements were identified for future versions of the system.Ingurune Adimentsuek bertan bizi diren pertsonei beren egunerokotasunean laguntzea dute helburu. Kontzeptu berri honek pertsona eta teknologiaren arteko erlazioan aldaketa bat dakarki berarekin, teknologiari garrantzia ematetik pertsonengan oinarritzera. Horrela, orain arteko sistemekin alderatuz, non pertsonek teknologia nola erabili ikasi behar duten, orain teknologia (ingurunea) bera da pertsonengana egokitu behar dena. Horretarako, inguruneak bertan dauden pertsonen behar, ohitura, etab. ezagutu behar ditu. Jakintza guzti hori ordea, pertsonak inondik inora gogaitu gabe lortu behar du inguruneak. Pertsona bakoitzari dagozkion edo nahi dituen zerbitzuak emateko inguruneak pertsona horren ohiko jokabideak jakitea behar du. Orduan, ingurune horrek ikasketa prozesu bat jarraitu behar du, non, pertsona horien ohiko jokabideak era automatiko eta garden batean lortuko dituen. Ikerketa lan honen helburua horixe izan da, ingurune adimentsu bateko pertsonen ohiko jokabideak era automatiko eta garden batean deskubrituko dituen sistema bat dise˜natu eta garatzea. Gainera, garatutako sistema horrek, Learning Frequent Patterns of User Behaviour System (LFPUBS), ingurune adimentsuen berezitasun guztiak hartzen ditu kontutan. LFPUBS-en barruan berebiziko garrantzia du Ikasketako geruzak, zein ez dagoen sistema aplikatzen ari den ingurunearen menpe. Geruza horretan bi osagaik merezi dute aparteko aipamena, alde batetik, ikasitako jakintza era garbi batean adieraztea ahalbidetzen duen hizkuntzak, eta beste alde batetik, jakintza bera deskubritzen duen algoritmoak. Azkenik, esan beharra dago LFPUBS balioztatua izan dela benetako bi ingurune adimentsuetan jasotako datuekin. Lortutako emaitzek garbi adierazten dute LFPUBS-ren gaitasuna ohiko jokabideak deskubritzeko garaian. Gainera, emaitzen analisi sakon batek LFPUBS-ri buruzko hobekuntzak argitaratzeko balio izan du.Los Entornos Inteligentes (EIs) tratan de facilitar las actividades diarias a las personas que se encuentran en él. El concepto de Entornos Inteligentes supone un cambio radical en las relaciones entre los usuarios y la tecnología. El cambio consiste en que se pasa desde una perspectiva centrada en la tecnología a una perspectiva centrada en el usuario. A diferencia de los sistemas actuales donde el usuario se tiene que adaptar a la tecnología, ahora, esta es la que se adapta a las preferencias, costumbres o gustos del usuario. Los entornos inteligentes deberán aprender cuáles son los comportamientos frecuentes de los usuarios para así adaptarse a los usuarios y proveer servicios personalizados. De este modo, la capacidad de aprendizaje se convierte en un requisito indispensable para dichos entornos. El objetivo de este trabajo de investigación es desarrollar un sistema que aprenda de forma automática, los comportamientos frecuentes de los usuarios de entornos inteligentes. Para ello, se ha diseñado y desarrollado el Learning Frequent Patterns of User Behaviour System (LFPUBS), que teniendo en cuenta todas las particularidades de dichos entornos, descubre tales comportamientos. El núcleo del LFPUBS es la capa de Aprendizaje que a diferencia de otros componentes es independiente del entorno donde está siendo aplicado el sistema. Dicha capa, por un lado incluye un lenguaje que permite representar los patrones de una forma clara y no ambigua y por otro, en concordancia con el lenguaje, incluye el algoritmo que descubre dichos patrones. Finalmente, LFPUBS ha sido validado utilizando los datos recogidos en dos entornos inteligentes reales. Los resultados obtenidos durante esos experimentos permitieron comprobar que LFPUBS es capaz de descubrir los comportamientos frecuentes de los usuarios. Además, mejoras para futuras versiones del sistema fueron identificadas

Discovering frequent user-environment interactions in intelligent environments

Intelligent Environments are expected to act proactively, anticipating the user's needs and preferences. To do that, the environment must somehow obtain knowledge of those need and preferences, but unlike current computing systems, in Intelligent Environments the user ideally should be released from the burden of providing information or programming any device as much as possible. Therefore, automated learning of a user's most common behaviors becomes an important step towards allowing an environment to provide highly personalized services. In this paper we present a system that takes information collected by sensors as a starting point, and then discovers frequent relationships between actions carried out by the user. The algorithm developed to discover such patterns is supported by a language to represent those patterns and a system of interaction which provides the user the option to fine tune their preferences in a natural way, just by speaking to the system

User-centred design of flexible hypermedia for a mobile guide: Reflections on the hyperaudio experience

A user-centred design approach involves end-users from the very beginning. Considering users at the early stages compels designers to think in terms of utility and usability and helps develop the system on what is actually needed. This paper discusses the case of HyperAudio, a context-sensitive adaptive and mobile guide to museums developed in the late 90s. User requirements were collected via a survey to understand visitors’ profiles and visit styles in Natural Science museums. The knowledge acquired supported the specification of system requirements, helping defining user model, data structure and adaptive behaviour of the system. User requirements guided the design decisions on what could be implemented by using simple adaptable triggers and what instead needed more sophisticated adaptive techniques, a fundamental choice when all the computation must be done on a PDA. Graphical and interactive environments for developing and testing complex adaptive systems are discussed as a further step towards an iterative design that considers the user interaction a central point. The paper discusses how such an environment allows designers and developers to experiment with different system’s behaviours and to widely test it under realistic conditions by simulation of the actual context evolving over time. The understanding gained in HyperAudio is then considered in the perspective of the developments that followed that first experience: our findings seem still valid despite the passed time

Discovering Knowledge through Highly Interactive Information Based Systems

[EN] The new Internet era has increased a production of digital data. The mankind had an easy way to the knowledge access never before, but at the same time the rapidly increasing rate of new data, the ease of duplication and transmission of these data across the Net, the new available channels for information dissemination, the large amounts of historical data, questionable quality of the existing data and so on are issues for information overload that causes more difficult to make decision using the right data. Soft-computing techniques for decision support systems and business intelligent systems present pretty interesting and necessary solutions for data management and supporting decision-making processes, but the last step at the decision chain is usually supported by a human agent that has to process the system outcomes in form of reports or visualizations. These kinds of information representations are not enough to make decisions because of behind them could be hidden information patterns that are not obvious for automatic data processing and humans must interact with these data representation in order to discover knowledge. According to this, the current special issue is devoted to present nine experiences that combine visualization and visual analytics techniques, data mining methods, intelligent recommendation agents, user centered evaluation and usability patterns, etc. in interactive systems as a key issue for knowledge discovering in advanced and emerging information systems.[ES] La nueva era de Internet ha aumentado la producción de datos digitales. Nunca nates la humanidad ha tenido una manera más fácil el acceso a los conocimientos, pero al mismo tiempo el rápido aumento de la tasa de nuevos datos, la facilidad de duplicación y transmisión de estos datos a través de la red, los nuevos canales disponibles para la difusión de información, las grandes cantidades de los datos históricos, cuestionable calidad de los datos existentes y así sucesivamente, son temas de la sobrecarga de información que hace más difícil tomar decisiones con la información correcta. Técnicas de Soft-computing para los sistemas de apoyo a las decisiones y sistemas inteligentes de negocios presentan soluciones muy interesantes y necesarias para la gestión de datos y procesos de apoyo a la toma de decisiones, pero el último paso en la cadena de decisiones suele ser apoyados por un agente humano que tiene que procesar los resultados del sistema de en forma de informes o visualizaciones. Este tipo de representaciones de información no son suficientes para tomar decisiones debido detrás de ellos podrían ser patrones de información ocultos que no son obvios para el procesamiento automático de datos y los seres humanos deben interactuar con estos representación de datos con el fin de descubrir el conocimiento. De acuerdo con esto, el presente número especial está dedicado a nueve experiencias actuales que combinan técnicas de visualización y de análisis visual, métodos de minería de datos, agentes de recomendación inteligentes y evaluación centrada en el usuario y patrones de usabilidad, etc. En sistemas interactivos como un tema clave para el descubrimiento de conocimiento en los sistemas de información avanzados y emergentes