This is the third year for the minitrack on Multimedia Information Systems at the Annual Hawaii International Conference on System Sciences. The presence of this minitrack reflects the importance and recent proliferation of multimedia applications. This proliferation has been driven by technological advances resulting in dramatic decreases in price/performance ratios in processing and storage technologies, video and audio input/output devices, imaging systems, and telecommunications. As it is not disputed that multimedia technology will be an indispensable aspect of computer/human interaction in the next-generation computing environment, approaches for applying multi-media technology and mechanisms for managing multimedia information in organizations are in demand.
Haptic perception refers to the ability of human beings to perceive spatial properties through touch-based sensations. In haptics, contextual clues about material, shape, size, texture, and weight configurations of an object are perceived by individuals leading to recognition of the object and its spatial features. In this paper, we present strategies and algorithms to model context in haptic applications that allow users to haptically explore objects in virtual reality/augmented reality environments. Initial results show significant improvement in accuracy and efficiency of haptic perception in augmented reality environments when compared to conventional approaches that do not model context in haptic rendering.
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.
Much of modern scientific research depends on databases, but do we need anything more than conventional database technology to support scientific data? One of the reasons for the development of the Grid is the sheer size of the datasets involved. This has introduced new problems for distributed data, distributed scientific programming, and the combination of the two. However there are other, equally important issues which demand new database technology. In this talk I want raise some of them. Annotation of existing data now provides a new form of communication between scientists, but conventional database technology provides little support for attaching annotations. I shall show why new models of both data and query languages are needed. Closely related to annotation is provenance — knowing where your data has come from. This is now a real problem in bioinformatics with literally hundreds of databases, most of which are derived from others by a process of transformation, correction and annotation. Preserving past states of a database — archiving — is also important for verifying the basis of scientific research, yet few published scientific databases do a good job of archiving. Past “editions” of the database get lost. I shall describe a system that allows frequent archiving and efficient retrieval with remarkably little space overhead. Finally, what do scientific databases have to do with multimedia information systems? Ostensibly nothing. However presentation of data has given us some clues about how to approach some of the problems above.
The importance of web discussion boards is growing with the interest of sharing knowledge and doubts with colleagues in a working/studying environment. The challenge is to organize the structure of discussion boards, to make the navigation easier, and to effectively extract relevant information. Message hierarchies in web discussion boards, manually organised by users participating into the discussion, might grow uncontrolled, thus making navigation more and more difficult for users. The goal of this paper is to develop a technique to organise messages in a message board, by automatically classifying and annotating pairs of postings to guide users through discussion segments relevant to their navigational goals.
