Fabio A. Schreiber

A data-oriented survey of context models

Context-aware systems are pervading everyday life, therefore context modeling is becoming a relevant issue and an expanding research field. This survey has the goal to provide a comprehensive evaluation framework, allowing application designers to compare context models with respect to a given target application; in particular we stress the analysis of those features which are relevant for the problem of data tailoring. The contribution of this paper is twofold: a general analysis framework for context models and an up-to-date comparison of the most interesting, data-oriented approaches available in the literature.

And what can context do for data

Common to all aCtors in today’s information world is the problem of lowering the “information noise,” both reducing the amount of data to be stored and accessed, and enhancing the “precision” according to which the available data fit the application requirements. Thus, fitting data to the application needs is tantamount to fitting a dress to a person, and will be referred to as data tailoring. The context will be our scissors to tailor data, possibly assembled and integrated from many data sources. Since the 1980s, many organizations have evolved to comply with the market needs in terms of flexibility, effective customer relationship management, supply chain optimization and so on and so forth: the situation where a set of partners re-engineered their single organizations, generating a unique, extended enterprise, has frequently been observed. Together with the organizations, also their information systems evolved, embracing new technologies like XML and ontologies, used in ERP systems and Webservice based applications. In recent years many organizations introduced into their information systems also Knowledge Management features, to allow easy information sharing among the organizations’ members; these new information sources and their content have to be managed together with other – we might say legacy – enterprise data. This growth of information, if not properly controlled, leads to a data overload that may cause confusion rather than knowledge, and dramatically reduce the benefits of a rich information system. However, distinguishing useful information from noise, i.e., from all the information not relevant to the specific application, is not a trivial task; the same piece of information can be considered differently, even by the same user, in different situations, or places – in a single word, in a different context. The notion of context, formerly emerged in various fields of research like psychology and philosophy, is acquiring great importance also in the computer science field. In a commonsense interpretation, the context is perceived as a set of variables that may be of interest for an agent and that influence its actions. The context has often a significant impact on the way humans (or machines) interpret their environment: a change in context causes a transformation in the actor’s mental representation of the reality, even when the reality is not changed. The word itself, derived from the Latin cum (with or together) and texere (to weave), describes a context not just as a profile, but as an active process dealing with the way humans weave their experience within their whole environment, to give it meaning. In the last few years, sophisticated and general context models have been proposed to support context-aware applications. In the following we list the different meanings attributed to the word context: Presentation-oriented: ˲ context is perceived as the capability of the system to adapt content presentation to different channels or to different devices. These context-models are often rigid, since they are designed for specific applications and rely on a well known set of presentation variables. Location-oriented: ˲ with this family of context models, it is possible to handle and What can context do for data?

PerLa: A Language and Middleware Architecture for Data Management and Integration in Pervasive Information Systems

A declarative SQL-like language and a middleware infrastructure are presented for collecting data from different nodes of a pervasive system. Data management is performed by hiding the complexity due to the large underlying heterogeneity of devices, which can span from passive RFID(s) to ad hoc sensor boards to portable computers. An important feature of the presented middleware is to make the integration of new device types in the system easy through the use of device self-description. Two case studies are described for PerLa usage, and a survey is made for comparing our approach with other projects in the area.

Logical and physical design issues for smart card databases

The design of very small databases for smart cards and for portable embedded systems is deeply constrained by the peculiar features of the physical medium. We propose a joint approach to the logical and physical database design phases and evaluate several data structures with respect to the performance, power consumption, and endurance parameters of read/program operations on the Flash-EEPROM storage medium.

Context Integration for Mobile Data Tailoring

Independent, heterogeneous, distributed, sometimes transient and mobile data sources produce an enormous amount of information that should be semantically integrated and filtered, or, as we say, tailored, based on the user’s interests and context. Since both the user and the data sources can be mobile, and the communication might be unreliable, caching the information on the user device may become really useful. Therefore new challenges have to be faced such as: data filtering in a context-aware fashion, integration of not-known-in-advance data sources, automatic extraction of the semantics. We propose a novel system named Context-ADDICT (Context-Aware Data Design, Integration, Customization and Tailoring) able to deal with the described scenario. The system we are designing aims at tailoring the available information to the needs of the current user in the current context, in order to offer a more manageable amount of information; such information is to be cached on the user’s device according to policies defined at design-time, to cope with data source transiency. This paper focuses on the information representation and tailoring problem and on the definition of the global architecture of the system.

Context information for knowledge reshaping

More and more often, we face the necessity of extracting appropriately reshaped knowledge from an integrated representation of the information space. Be such a global representation a central database, a global view of several ones or an ontological representation of an information domain, we face the need to define personalised views for the knowledge stakeholders: single users, companies or applications. We propose exploiting the information usage context within a methodology for context-aware data design, where the notion of context is formally defined together with its role within the process of view building by information tailoring. This paper presents our context model, called the context dimension tree, which plays a fundamental role in tailoring the information space according to user information needs.

A methodology for a Very Small Data Base design

This paper proposes a design methodology for very small databases for the purpose of being hosted by portable devices. Three main differences w.r.t. the traditional design methodologies are introduced: first, the main mobility issues are considered along with data distribution; second, context awareness is included in the data design issues to allow full exploitation of context-sensitive application functionalities; and third, the peculiarities of the storage device(s) are taken into account by introducing a logistic phase after the usual conceptual and logical phases. The three aspects together determine the VSDB ambient which is the set of personal and environmental characteristics determining the portion of data that must be stored on the portable device. This paper details the design methodology in its conceptual, logical and logistic phases.

Is Time a Real Time? An Overview of Time Ontology in Informatics

Aim of this paper is to introduce several aspects of time in the heterogeneous world of Informatics and define ontologies for time in different domains of computers and their applications. Some philosophical and physical backgrounds are given, to show how, from the richness of often contrasting ideas developed in the framework of these disciplines, many useful concepts have been derived also for computer science. Architectural aspects of computer systems, Information Systems applications, and Real-time systems, are considered as temporally problematic domains.

A context-aware methodology for very small data base design

The design of a Data Base to be resident on portable devices and embedded processors for professional systems requires considering both the device memory peculiarities and the mobility aspects, which are an essential feature of the embedded applications. Moreover, these devices are often part of a larger Information System, comprising fixed and mobile resources. We propose a complete methodology for designing Very Small Data Bases, from the identification of the device resident portions down to the choice of the physical data structure, optimizing the cost and power consumption of the Flash memory, which - in the greatest generality - constitutes the permanent storage of the device.

The ESTEEM platform: enabling P2P semantic collaboration through emerging collective knowledge

In this paper, we present Esteem (Emergent Semantics and cooperaTion in multi-knowledgE EnvironMents), a community-based P2P platform for supporting semantic collaboration among a set of independent peers, without prior reciprocal knowledge and no predefined relationships. Goal of Esteem is to go beyond the existing state-of-the-art solutions for P2P knowledge sharing and to provide an integrated platform for both data and service discovery. A distinguishing feature of Esteem is the use of semantic communities to explicitly give shape to the collective knowledge and expertise of peer groups with similar interests. Key techniques of Esteem will be presented in the paper and concern: shuffling-based communication, ontology and service matchmaking, context management, and quality-aware data integration. An application example of data and service discovery in the health-care domain will be presented, by also discussing results of system and user evaluation.