The concept of object technology has been well accepted. Using the grouping of data provided by object-oriented data structuring provides advantages in
terms of efficient programming and program maintenance. However, current object-oriented systems are far from solving all the problems that exist in
computing. Scalability is a particular concern. The modest acceptance of object-oriented databases is evidence of that problem. In this paper we survey the
state of object data management, and current research. In particular we assesses multi-user and semantic scalability the object-oriented data paradigm.

Rather than rejecting the use of objects due to the problems discovered we describe an algebraic approach which permits the generation of data objects
from relational data, based on the knowledge captured in a formal model. Now objects can be created that satisfy a variety of particular views, as long as
the hierarchies represented by the views are subsumed in the network represented by the overall structural model. 

In truly large systems new problems arise, namely that not only multiple views will exist, but also that the domains to be covered by the data will be
autonomous and hence heterogeneous. We extend concepts used in object-based structural algebras to an algebra that can handle differences in
terminology, suitable for information systems that span multiple domains. Using such a knowledge-based algebra, the domain knowledge can be partitioned
for maintenance. Only the articulation points, where the integration intersects, have to be agreed upon. This to be achieved defined by matching rules which
define the shared knowledge. The principal operations in the algebras are simple and provide for selection from the objects in the data space and composing
them into new structures that represent the desired information.

At both levels, scaling is achieved by moving beyond hierarchical structures. The underlying concepts are based on the observation that integrated,
multi-purpose data, and the knowledge that describes the data forms complex webs of information, while effective processing and its algorithms require
hierarchical processing of the application problem-specific subsets.