Dario Bonino

The DOG gateway: enabling ontology-based intelligent domotic environments

This paper moves a first step towards the creation of Intelligent Domotic Environments (IDE) in real-life home-living. A new Domotic OSGi Gateway (DOG) is presented, able to manage different domotic networks as a single, technology neutral, home automation system. The adoption of a standard framework such as OSGi, and of sophisticated modeling techniques stemming from the Semantic Web research community, allows DOG to go beyond simple automation and to support reasoning-based intelligence inside home environments. By exploiting automatic device generalization, syntactic and semantic command validation, and internetwork scenario definition, DOG provides the building blocks for supporting the evolution of current, isolated, home automation plants into IDEs, where heterogeneous devices and domotic systems are coordinated to behave as a single, intelligent, proactive system. The paper introduces the DOG architecture and the underlying ontology modeling. A case study is also illustrated, where DOG controls a laboratory reconstruction of a simple domotic environment.

What would you ask to your home if it were intelligent? Exploring user expectations about next-generation homes

Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications; however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deployment.

A real-time evolutionary algorithm for Web prediction

As an increasing number of users access information on the World Wide Web, there is a opportunity to improve well known strategies for Web caching, prefetching, dynamic user modeling and dynamic site customization in order to obtain better subjective performance and satisfaction in Web surfing. We propose a new method to exploit user navigational path behavior to predict, in real-time, future requests. Predicting user next requests is useful not only for document caching/prefetching, it is also suitable for quick dynamic portal adaptation to user behavior. Real-time user adaptation prevents the use of statistical techniques on Web logs, and we propose the adoption of a predictive user model based on finite state machines together with an evolutionary algorithm that evolves a population of FSMs for achieving a good prediction rate.

DOSE: a distributed open semantic elaboration platform

The paper proposes a distributed open semantic elaboration platform based on a modular multilingual enabled architecture, which includes ontology, annotations, lexical entities and search functions. The platform is implemented as a distributed set of services including: semantic annotations for document substructures (e.g. chapters, sections, paragraphs), an external annotation repository (based on XPath and XPointer technologies) that is automatically populated starting from a known ontology and a lexical representation of concept classes, and a semantic search engine used to extract and recombine relevant document fragments. Annotated resources may be XML or XHTML static or dynamic documents, and need not be stored nor modified. Preliminary experimental results are presented to show the feasibility and the advantages of the proposed approach.

DOG: An Ontology-Powered OSGi Domotic Gateway

This paper proposes an ontology-powered Domotic OSGi gateway (DOG) able to expose different domotic networks as a single, technology neutral, home automation system. The adoption of a standard framework such as OSGi, and of sophisticated modeling techniques stemming from the Semantic Web research community, allows DOG to go beyond simple automation and to support reasoning-based intelligence inside home environments.

OntoSphere3D: A Multidimensional Visualization Tool for Ontologies

In this paper, we propose a novel approach for inspecting and editing ontologies using a 3D space, where information is represented on a 3D viewport enriched by several visual cues. This approach aims at tackling space allocation issues for ontology visual models by adopting a dynamic collapsing mechanism and different views, at different granularities, for granting a constant navigability of the rendered model. A Protege plug-in, based on the presented principles and named OntoSphere3D, has been developed. Preliminary results, comparing it to other solutions as Jambalaya, OWLViz and TgViz on specific operations, show that the approach is feasible and can actually lead to a more effective modeling process with respect to currently available solutions

Dynamic prediction of Web requests

As an increasing number of users access information on the World Wide Web, there is a opportunity to improve well known strategies for Web prefetching, dynamic user modeling and dynamic site customization in order to obtain better subjective performance and satisfaction in Web surfing. We propose a new method to exploit user navigational path behavior to predict, in real-time, future requests. Real-time user adaptation avoids the use of statistical techniques on Web logs by adopting a predictive user model. We designed a new model derived from the finite state machine (FSM) formalism together with an evolutionary algorithm that evolves a population of FSMs for achieving a good prediction rate, and we evaluated the performance of the prediction system using the concepts of precision and applicability.

A blueprint for integrated eye-controlled environments

Eye-based environmental control requires innovative solutions for supporting effective user interaction, for allowing home automation and control, and for making homes more “attentive” to user needs. Several approaches have already been proposed, which can be seen as isolated attempts to address partial issues and specific sub-sets of the general problem. This paper aims at tackling gaze-based home automation as a whole, exploiting state-of-the-art technologies and trying to integrate interaction modalities that are currently supported and that may be supported in the near future. User–home interaction is sought through two, complementary, interaction patterns: direct interaction and mediated interaction. Integration between home appliances and devices and user interfaces is granted by a central point of abstraction and harmonization called House Manager. Innovative points can be identified in the wide flexibility of the approach which allows on one side to integrate virtually all home devices having a communication interface, and, on the other side, combines direct and mediated user interaction exploiting the advantages of both. A discussion of interaction and accessibility issues is also provided, justifying the presented approach from the point of view of human–environment interaction.

Dynamic optimization of semantic annotation relevance

The introduction of semantics in the next generation of the Web, the semantic Web, is strongly based on conceptual description of resources by means of semantic annotations. Effective technologies are therefore required lo correctly map the available syntactic information onto a set of relevant conceptual entities able to model the knowledge domain to which a resource belongs. In attempting to address such issue, we propose an evolutionary optimization of semantic annotation relevance which can improve text-to-concept mapping using information from both the syntactic and the semantic domains. The proposed algorithm leverages relevance information on resource contents, with respect to a subset of a given ontology, and performs several ontology navigation steps for extracting the set of most relevant annotations, in terms of semantic expressiveness. The fitness function of the algorithm is strongly time dependent since the set of annotation to be refined may vary according to user requests, to changes in the domain ontology and is related to the granularity of the annotation set.

DogSim: A state chart simulator for Domotic Environments

Smart Homes and Domotic Environments are promising to revolutionize the daily human life providing users with increased care, adaptability and safety. However, to fully exploit their potential, in everyday life, suitable design and verification tools must be available, allowing architects and designers to correctly implement their ideas and to verify the effects of designed policies on real world environments. This paper describes DogSim: a framework and API for automatic generation of state chart simulators from ontology-based descriptions of domotic environments. DogSim has been tested on the model of a 6-room flat equipped with 95 devices. Results show that the approach is feasible and that can easily address realistic home scenarios.