Early Data Tailoring for Ubiquitous Information Access in Highly Dynamic Environments
- 260 Downloads
Nowadays content and services are available at different sources and places, thus a user can be seen as an integral part of numerous applications in which he/she interacts with service providers, product sellers, governmental organisations, friends and colleagues.
Information access personalization can be defined as any set of actions that can tailor information to a particular user or set of users. To achieve effective personalization, single users and organizations must rely on all available information, including: user profile, channel peculiarities, users current interests and typical behaviour, source content, source structure, as well as domain knowledge. In addition, efficient and intelligent techniques are needed to effectively use the discovered knowledge to enhance the users’ experience.
These techniques must address important challenges emanating from the size and the heterogeneous nature of the data itself, as well as the dynamic nature of user interactions with data sources. These challenges include scalability of the personalization solutions in the process of data integration, and successful integration of techniques from knowledge representation, semantic web, information retrieval and filtering, databases, user modelling and human-computer interaction.
Our approach addresses these issues with particular attention to the process of data tailoring, which consists of the exploitation of knowledge about the user, the adopted channel and the environment, altogether called context, to the end of reducing the amount of information imported on the mobile device. Tailoring is needed because of two main reasons: one is the need to keep information manageable, in order for the user not to be confused by too much noise; the second reason is the frequent case that the mobile device be a small one, like a palm computer or a cellular phone, in which condition only the most precious information must be kept on board.
We consider open, networked, peer-based systems, according to paradigms where there is no previous knowledge and relationship among the parties, which may be mobile as well as fixed devices. The interaction among such devices is transient, since it is subject to network and device availability: indeed the nature of these devices and the variety of ambient strongly affect the cooperation methods and techniques. Semantic based caching techniques are exploited to cope with the above mentioned network availability problems, always allowing the single party to retain the appropriate portion of needed data, while other fragments, stored at different peers, can be queried only when a connection is available. The mobile and dynamic context where the devices cooperate determines the fraction of data located on board, which, due to the limited amount of memory available, must be refreshed according to semantic context-based criteria. On the other hand, power aware data access techniques must be employed to manage the problem of limited battery life.
Consider the example of a semantic community formed to enable scientific collaboration in the medicine context; here, different resource structures and meanings are provided by the community peers. Besides selecting and semantically integrating the most appropriate resources provided by the various peers, it is the special goal of such techniques to obey the constraints imposed by the device context. For instance, during a home visit, a doctor in need of information about the symptoms of a rare disease can search, through his/her PDA or cell phone, clinical databases and research reports on the web looking for assistance in his/her diagnosis. Obviously the doctor must not be disoriented by the different formats of the retrieved documents, by the possible lexical discrepancies in their contents and by information which would be useless in the specific environment (tropical diseases symptoms in an Eskimo patient), but the most valuable information must be presented in the most suitable form for the operational context and the available device.
In this work we describe the methodology driving the selection of the device-residing portion of data; analysis dimensions for the detection of the context provide the different perspectives the mobile device is viewed from, and are used to set out its ambient. The identified dimensions and their current values drive the choice of the information to be kept on the mobile device, to be actually selected at run time. In order to formalize and then automatically obtain this view (the device ambient), we model the dimensions as a hierarchical, DAG-shaped, structure which allows us to consider an ontological specification of each considered concept, and to model semantic constraints between different dimensions as well. The dimension DAG contributes to the automatic selection and interpretation of the shared resources to be imported to the device.
Moreover, when interesting concepts are found and selected from the available data sources, it is also useful to collect on the device other concepts which are possibly related to them. However, since devices may have a limited memory, dynamic conflict resolution strategies must be devised, possibly based on various notions of semantic nearness, to decide which data must be retained and which can be discarded.