Letizia Tanca

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?

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 preference-based personalization of contextual data

The widespread use of mobile appliances, with limitations in terms of storage, power, and connectivity capability, requires to minimize the amount of data to be loaded on users devices, in order to quickly select only the information that is really relevant for the users in their current contexts: in such a scenario, specific methodologies and techniques focused on data reduction must be applied. We propose an extension to the data tailoring approach of Context-ADDICT, whose aim is to dynamically hook and integrate heterogeneous data to be stored on small, possibly mobile devices. The main goal of our extension is to personalize the context-dependent data obtained by means of the Context-ADDICT methodology, by allowing the user to express preferences that specify which data s/he is more interested in (and which not) in each specific context. This step allows us to impose a partial order among the data, and to load only the top (most preferred) portion of the data chunks. A running example is used to better illustrate the approach.

CARVE: Context-aware automatic view definition over relational databases

Classical database design strategies, based on the a priori definition of application views to be finally integrated in the design of the global databases, are not appropriate for the design needs of modern, highly dynamic information systems. This paper presents CARVE (Context-Aware Relational View dEfinition), a methodology for context-aware view definition, well-suited for the design of modern, dynamic applications that, in different environments and situations, need to access different portions of data. The methodological approach includes a context-design phase, followed by a phase when each of the possible application contexts is automatically associated with its relevant part of information (context-aware view). Accordingly, CARVE is based on a context model, on guidelines to define partial views related to components of the context, and on a set of operators used to perform partial-view composition to derive the context-aware views. The paper leverages on previously presented preliminary results and introduces and formalizes the overall methodology and its steps, providing a revised and assessed approach. The results of the application of the approach to a set of case studies are reported, together with a careful evaluation.

Data Mining for XML Query-Answering Support

Extracting information from semistructured documents is a very hard task, and is going to become more and more critical as the amount of digital information available on the Internet grows. Indeed, documents are often so large that the data set returned as answer to a query may be too big to convey interpretable knowledge. In this paper, we describe an approach based on Tree-Based Association Rules (TARs): mined rules, which provide approximate, intensional information on both the structure and the contents of Extensible Markup Language (XML) documents, and can be stored in XML format as well. This mined knowledge is later used to provide: 1) a concise idea-the gist-of both the structure and the content of the XML document and 2) quick, approximate answers to queries. In this paper, we focus on the second feature. A prototype system and experimental results demonstrate the effectiveness of the approach.

Ontology-Based Information Tailoring

Current applications are often forced to filter the richness of datasources in order to reduce the information noise the user is subject to. We consider this aspect as a critical issue of applications, to be factorized at the data management level. The Context-ADDICT system, leveraging on ontology-based context and domain models, is able to personalize the data to be made available to the user by context-aware tailoring. In this paper we present a formal approach to the definition of the relationship between context (represented by an appropriate context model) and application domain (modeled by a domain ontology). Once such relationship has been defined, we are able to work out the boundary of the portion of the domain relevant to a user in a certain context. We also sketch the implementation of a visual tool supporting the application designer in this modeling task

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.