Trio is a new database system that manages not only data, butalsotheaccuracy and lineage of the data. Inexact (uncertain, probabilistic, fuzzy, approximate, incomplete, and imprecise!) databases have been proposed in the past, and the lineage problem also has been studied. The goals of the Trio project are to combine and distill previous work into a simple and usable model, design a query language as an understandable extension to SQL, and most importantly build a working system—a system that augments conventional data management with both accuracy and lineage as an integral part of the data. This paper provides numerous motivating applications for Trio and lays out preliminary plans for the data model, query language, and prototype system.

There are a variety of ways to represent information and each representation scheme typically has associated tools to manipulate information so long as it is represented properly. We are particularly interested in representations for superimposed information, where supplementary information can be used to highlight, annotate, elaborate, and interconnect underlying information. The Resource Description Framework (RDF) and the Topic Map model are two such examples that can represent superimposed information.

Data annotations are an important kind of metadata that occur in the form of externally assigned descriptions of particular features in Web accessible documents. Such metadata are eventually used in data retrieval tasks on heterogeneous, possible distributed Web-accessible documents.

In several scientific application domains, such as the computational sciences, the transparent and integrated access to distributed and heterogeneous data sources is key to leveraging the knowledge and findings of researchers. Standard database integration approaches, however, are either not applicable or insufficient because of lack of local and global schema structures. In these application domains, data integration often occurs manually in that researchers collect data and categorize them using "semantic indexing", in the most simple case through local bookmarking, which leaves them without appropriate data query, sharing, and management mechanisms.

Portals offer relevant information often with improved and customizable search. However, most portals concentrate on locating documents as opposed to enhancing their use. We present superimposed schematics which serve to enrich documents by adding a structured, conceptual guide for their contents.

Abstract. In this paper, we introduce a collaborative learning environment called Virtual Tutor for exploiting valuable digital libraries for teaching and learning. Virtual Tutor facilitates the building of online lectures and their provision. It also supports collaborative and explorative learning. We describe the architecture of the system and its functionality. A summative evaluation is conducted on the system. The paper concludes by comparing the Virtual Tutor functionality with similar approaches and highlighting the strengths of the Virtual Tutor design. 1