Call number: LD2668 .T4 1984 S93 / Master of Science

Call number: LD2668 .T4 1985 W547 / Master of Science

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

The concept of a generalized model management System (GMMS) and its role in a decision support system are discussed. A paradigm for developing a GMMS which integrates artificial intelligence techniques with data management concepts is presented. The paradigm views a GMMS as a knowledge-based modeling system (KBMS) with knowledge abstractions as the vehicle of knowledge and model representation. Knowledge abstractions are introduced as a hybrid of the predicate calculus, semantic network, and frame representations in artificial intelligence (AI) embodied in an equivalent of a programming language data abstraction structure. As a result, models represented by knowledge abstractions are not only subject to the powerful problem reduction and inference techniques available in the AI domain but are also in a form conducive to model management. The knowledge abstraction in its most general form is seen as a frame which serves as a template for generating abstraction instances for specific classes of models. The corollaries of an abstraction-based GMMS with current data management concepts are explored. A CODASYL implementation of an abstraction-based GMMS for the class of linear programming models is described and demonstrated.

A global schema is an integrated view of heterogeneous databases used to support data sharing among independent, existing databases. Global schema design complexities arise from the volume of details, design choices, potential conflicts, and interdependencies among design choices. The methodology described provides a framework for efficient management of these critical dimensions in generating and evaluating alternative designs. The methodology contains three major steps. First, differences due to the varying local data models are resolved by converting each local schema to an equivalent schema in a unifying data model. Second, the entity types of the local schemas in the unifying model are grouped into clusters called common areas. All the entity types in a common area can possibly be merged via generalization. For each common area, semantic information is defined that drives the merging process. Third, each common area is integrated into the global schema by applying a set of generalization operators. Mapping rules are then defined to resolve differences in the representations of equivalent attributes. Th integration of the local schemas is based on equivalence assertions. Four types of attribute equivalences are defined: two attributes may be locally or globally equivalent, and they can be key or non-key. Strategies for handling each of these cases are proposed and evaluated. The global schema design methodology includes several algorithms which may assist a designer. One algorithm analyzes a set of equivalence assertions for consistency and completeness including resolution of transitively implied assertions. A second algorithm performs an interactive merge of a common area by presenting the possible generalization actions to the designer. It supports the theme that many generalization structures can be possible, and the appropriate structure often depends on designer preferences and application requirements. The methodology is evaluated for several cases involving real databases. The cases demonstrate the utility of the methodology in managing the details, considering many alternatives, and resolving conflicts. In addition, these cases demonstrate the need for a set of computer-aided tools; for even a relatively small case, the number of details and design choices can overwhelm a designer.

No description available.

Approved for public release; distribution is unlimited. / Traditional database development has been done for a specific, self-contained purpose with no plan to share or merge the data with other databases in the future. As these systems have matured, users have realized a requirement exists to share their data. Finding common attributes among databases is a time consuming task. However, it is one that is necessary as more and more corporations and agencies consolidate operations. In terms of DoD, the requirement to consolidate systems has come about, as the various data systems used by DoD agencies and our allies need to communicate with each other for a well-coordinated operation. One alternative for achieving the desired interconnectivity is to specify the requirement for interoperability in new systems. A more practical, less costly process is to merge existing systems and consolidate the common components. This paper proposes a process for consolidating portions of data dictionaries of two existing databases. The proposed method uses commercial-off-the-shelf software in finding common attributes between multiple databases and represents an improvement in accuracy and time over previous methods.

There have been a number of different versioning models proposed. The research in this area can be divided into two categories: object versioning and schema versioning. In this dissertation, both problem domains are considered as a single unit. This dissertation describes a unifying version model (UVM) for maintaining changes to both objects and schema. UVM handles schema versioning operations by using object versioning techniques. The result is that the UVM allows the OODBMS to be much smaller than previous systems. Also, programmers need know only one set of versioning operations; thus, reducing the learning time by half. This dissertation shows that UVM is a simple but semantically sound and powerful version model for both objects and schema.