Although the concept of roles is becoming a popular research issue in object oriented databases and has been proven to be useful for dynamic and evolving applications, it has only been described conceptually in most of the previous work. Moreover, the important issues such as the semantics of roles (e.g., message passing) are seldom discussed. Furthermore, none of the previous work has investigated the idea of role player qualification, which models the fact that not every object is qualified to play a particular role. We present a data model and the semantics of roles. We discuss each of the above issues and illustrate the ideas with examples. From these examples, we can easily see that the problems we discussed are fundamental and indeed exist in many complex applications.

Tangram, an environment for modeling which is under development at UCLA, is discussed. One of the driving concepts behind Tangram has been the combination of large-scale data access and data reduction with a powerful programming environment. The Tangram environment is based on PROLOG, extending it with a number of features, including process management, distributed database access, and generalized stream processing. The authors describe the Tangram stream processor, the part of the Tangram environment performing query processing on large streams of data. The paradigm of transducers on streams is used throughout this system, providing a database flow computation capability.>

Two algorithms for constructing minimal deduction graphs (MDG) for inferring rules and facts in an extended version of the Horn clause logic are described. A deduction graph (DG) is minimal if the number of arcs in the graph is minimized. Horn clauses (HC) are extended to Horn formulas (HF), such that the head or the body of an HF can be a conjunction of positive literals or a disjunction of the bodies of some rule instances, respectively. Each algorithm constructs an MDG from its source to its sink, whose arcs infer the HF 'if source then sink'. The construction of an MDG is based on a sound and complete set of inference rules of reflexivity, transitivity, and conjunction for HFs which proceeds by expanding a tree rooted at its sink until its source has a successful backtracking to the root. Then the MDG is extracted from the tree. The nodes being expanded in such a tree are classified into seven types, which are assigned by different priorities for their growing into subtrees or for their pruning to reduce the tree space.>