The construction of spaces composed of physical artefacts augmented with computational, sensing, auto-identification and wireless communication capabilities is becoming increasingly practical at larger scale and drives research interest in the technical challenges related to the everyday use of such intelligent environments. Nevertheless, several barriers remain before intelligent environments can be effectively used, notably the fact that abundance of such computational and communication capability does not necessarily imply the availability of useful or usable services and applications. In fact, the contrary is often the case since such spaces are the source and possibly also the repository of massive amounts of data created by the continuous archival of personal experiences, which users cannot access in a meaningful way. A major challenge in making intelligent environments useful is indeed the development of efficient and effective navigation mechanisms that is, the ability to search, locate and retrieve information as and when needed so as to fit the task at hand. To be sure, in addition to capture, intelligent environments must provide mechanisms for the effective navigation of recorded personal experiences as a core ingredient of their architecture.

We believe that an appropriate data management approach can be developed on top of the digital equivalent of the conceptual experience record, which we call a trail. A trail brings together the different aspects of the real experience for example what the visitor saw, what they thought, and what they did. Consequently, trails are in many cases the conceptual mechanism with which individuals organise and recall their personal experiences. For this reason, trails suggest themselves naturally as an appropriate candidate for the role of the data primitive on which to build an archival and retrieval engine for personal experiences. Moreover, trails have the additional advantage that they can be displayed within a temporal context, possibly also combined with the spatial attributes of their recording context.

Furthermore, the use of trails as a data primitive also allows us to associate records of a personal experience, for example a visit to a physical location, with related digital resources held on object repositories, potentially maintained online. Such links between artefacts and digital metadata offers the opportunity to augment personal experience records with semantic information for example artefact descriptions, production and usage history and other cultural information. To this end, we develop a general data model that can be used to record, archive and reconstruct personal experiences and a suitable representation of the intelligent environment tailored to this task. This model caters for data related to the experience record, multimedia content obtained by the infrastructure automatically and by the user deliberately and external semantic and multimedia content. This representation is employed for reconstruction and visualisation in such a way that it assists recall, navigation of the archived records and accurate and efficient retrieval of personal experiences. By doing so, we aim to reconstruct the experience in a way that facilitates recall and reflection that goes beyond simple temporal and spatial cues, but extends to spatial and temporal relationships.

Our navigation techniques have evolved from web search and navigation mechanisms, and recognise the inherently social nature of knowledge co-construction and interpretation of object collections. At its core, lies the acknowledgement of the need for adaptive path finding mechanisms that cater for the fundamentally explorative and experiential nature of knowledge management and learning.