SDM is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them. SDM provides a collection of high-level modeling primitives to capture the semantics of an application environment. By accommodating derived information in a database structural specification, SDM allows the same information to be viewed in several ways; this makes it possible to directly accommodate the variety of needs and processing requirements typically present in database applications. The design of the present SDM is based on our experience in using a preliminary version of it. SDM is designed to enhance the effectiveness and usability of database systems. An SDM database description can serve as a formal specification and documentation tool for a database; it can provide a basis for supporting a variety of powerful user interface facilities, it can serve as a conceptual database model in the database design process; and, it can be used as the database model for a new kind of database management system.

Galileo, a programming language for database applications, is presented. Galileo is a strongly typed, interactive programming language designed specifically to support Semantic Data Model features (classification, aggregation and specialization) as well as abstraction mechanisms of modern programming languages (types, abstract types and modularization). The main contributions of Galileo are: a) the proposal of a flexible type system to model database structure and semantic integrity constraints; b) the inclusion of type hierarchies to support the specialization abstraction mechanism of Semantic Data Models. c) the proposal of a modularization mechanism to structure data and operations into interrelated units; d) the integration of the abstraction mechanisms into an expression based language that allows an interactive use of the database without resorting to a new stand alone query language. Galileo will be used in the immediate future as a tool for database design and, in the long term, as a high level interface for DBMSs. data types; data types and structures; H.2.1 [Database Management]: Logical Design- data models; schema and subschema; H.2.3 [Database Management]: Languages- data description languages (DDL); data manipulation languages (DML); query languages

Knowledge-base management systems (KBMS) based on description logics are being used in a variety of situations where access is needed to large amounts of data stored in existing relational databases. We present the architecture and algorithms of a system that converts most of the inferences made by the KBMS into a collection of SQL queries, thereby relying on the optimization facilities of existing DBMS to gain e#ciency, while maintaining an object-centered view of the world with a substantive semantics and significantly di#erent reasoning facilities than those provided by Relational DBMS and their deductive extensions. We address a number of optimization issues that arise in the translation process due to the fact that SQL queries with di#erent syntax (but identical semantics) are not treated uniformly by current database management systems.

Virtually all semantic or object-oriented data models assume objects have an identity separate from any of their parts, and allow users to define complex object types in which part values may be any other objects. In [20], a more general form of functional dependency is proposed for such models in which component attributes may correspond to descriptions of property paths, called path functional dependencies (PFDs). The main contribution of the reference is a sound and complete axiomatization for PFDs when databases may be infinite. However, a number of issues were left open which are resolved in this paper. We first prove that the same axiomatization remains complete if PFDs are permitted empty left-hand sides, but that this is not true if logical consequence is defined with respect to finite databases. We then prove that the implication problem for arbitrary PFDs is decidable. The proof suggests a means of characterizing an important function closure which is then used to derive an effective procedure for constructing a deterministic finite state automation representing the closure. The procedure is further refined to efficient polynomial time algorithms for the implication problem for cases in which antecedent PFDs are a form of complex key constraint. Index Terms: constraints, functional dependencies, object-oriented data models, complex objects, implication problems

Database design commonly assumes, explicitly or implicitly, that instances must belong to classes. This can be termed the assumption of inherent classification. We argue that the extent and complexity of problems in schema integration, schema evolution, and interoperability are, to a large extent, consequences of inherent classification. Furthermore, we make the case that the assumption of inherent classification violates philosophical and cognitive guidelines on classification and is, therefore, inappropriate in view of the role of data modeling in representing knowledge about application domains. As an alternative, we propose a layered appro...

Data Types New base types can be added to the EXTRA data model via the EXTRA abstract data type facility. To add a new ADT, the person responsible for adding the type begins by writing (and debugging) the code for the type in the E programming language. E is an extension of C++ [Stro86] that was developed as part of the EXODUS project. E serves as the implementation language for access methods and operators for systems developed using EXODUS. It is also the target language for the query compiler, and (most importantly for our purposes here) the language in which base type extensions will be defined. E extends C++ with a number of features to aid programmers in data- 89 base system programming, including "dbclasses" for persistent storage, class generators for implementing "generic" classes and functions, iterators for use as a control abstraction in writing set operations, and built-in class generators for typed files and variable-length arrays [Rich87]. Suppose that we wanted to add...

Object-oriented computing is influencing many areas of computer science including software engineering, user interfaces, operating systems, programming languages and database systems. The appeal of object-orientation is attributed to its higher levels of abstraction for modeling real world concepts, its support for incremental development and its potential for interoperability. Despite many advances, object-oriented computing is still in its infancy and a universally acceptable definition of an object-oriented data model is virtually nonexistent, although some standardization efforts are underway. This report presents the TIGUKAT 1 object model definition that is the result of an investigation of object-oriented modeling features which are common among earlier proposals, along with some distinctive qualities that extend the power and expressibility of this model beyond others. The literature recognizes two perspectives of an object model: the structural view and the behavioral view. ...

Reasoning with terminological logics is a subfield in the area of knowledge representation that evolved from the representation language kl-one. Its main purpose is to automatically determine the location of a a new concept description (or object description) in a partially ordered set of given concepts. It seems to be a promising approach to apply the techniques developed in this area to the development of new object-based database models. The main advantages are a uniform query and database definition language and the utilization of an indexing technique, which we call semantic indexing.

Conceptual modelling refers to the part of system development that involves investigating the problems and requirements of the users community and from that, developing a specification of the desired system. Conceptual modelling addresses two major aspects: the conceptual product (the so-called conceptual schema) and the conceptual process (the modelling process to deliver the conceptual product). Contributions to the field of conceptual modelling have emphasized the product aspect. A large variety of conceptual models have proposed high level concepts and abstraction mechanisms by which systems may be described at a conceptual level. Conceptual models have proved to be extremely useful throughout the information system life cycle and, hence, to be one of the most fundamental tools in the area of information systems engineering. However the growing demand for large and complex information systems calls for the introduction of new and more precise, formal techniques to model reality....

Central to the Model Management (MM) function is the creation and maintenance of a knowledge-based model repository. The Model Knowledge Base (MKB) provides the basis by which information about models can be shared to facilitate consistent and controlled utilization of existing models for decision making, as well as the development of new models. Various schemes for representing individual models have been proposed in the literature. This paper focuses on how best to structure, control, and administer a large MKB to support organization-wide modeling activities. Guided by a recently proposed systems framework for MM, we describe a number of concepts which are useful for capturing the semantics and structural relationships of models in an MKB. These concepts, and the nature of the MMS functions to be supported, are then used to derive specific information management requirements for model bases. Four major requirements are identified: (1) management of composite model configurations; (2) management of model version histories; (3) support for the model consultation and selection functions of an MMS; and (4) support for multiple logical MKBs (private, group, and public). We argue that traditional record-based approaches to data management appear to fall short of capturing the rich semantics present in an MM environment. The paper proposes an architecture for an MMS, focusing on its major component- the MKB Management Subsystem. An implementation of this architecture is briefly described.