Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models took off in the eighties and early nineties alongside object-oriented models. Their influence gradually died out with the emergence of other database models, in particular geographical, spatial, semistructured, and XML. Recently, the need to manage information with graph-like nature has reestablished the relevance of this area. The main objective of this survey is to present the work that has been conducted in the area of graph database modeling, concentrating on data structures, query languages, and integrity constraints.

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Survey of graph database models

Most common database management systems represent information in a simple record-based format. Semantic modeling provides richer data structuring capabilities for database applications. In particular, research in this area has articulated a number of constructs that provide mechanisms for representing structurally complex interrelations among data typically arising in commercial applications. In general terms, semantic modeling complements work on knowledge representation (in artificial intelligence) and on the new generation of database models based on the object-oriented paradigm of programming languages.This paper presents an in-depth discussion of semantic data modeling. It reviews the philosophical motivations of semantic models, including the need for high-level modeling abstractions and the reduction of semantic overloading of data type constructors. It then provides a tutorial introduction to the primary components of semantic models, which are the explicit representation of objects, attributes of and relationships among objects, type constructors for building complex types, ISA relationships, and derived schema components. Next, a survey of the prominent semantic models in the literature is presented. Further, since a broad area of research has developed around semantic modeling, a number of related topics based on these models are discussed, including data languages, graphical interfaces, theoretical investigations, and physical implementation strategies.

Semantic database modeling: survey, applications, and research issues

DAPLEX is a database language which incorporates:a formulation of data in terms of entities;a functional representation for both actual and virtual data relationships;a rich collection of language constructs for expressing entity selection criteria;a notion of subtype/supertype relationships among entity types.This paper presents and motivates the DAPLEX language and the underlying data model on which it is based.

The functional data model and the data languages DAPLEX

This paper discusses the advantages of a functional approach to data modelling. This approach forms the basis for DAPLEX, a high-level language for data description and manipulation. We describe the DAPLEX language and the functional data model and examine the benefits they provide as tools for expressing and solving problems in database management.

The functional data model and the data language DAPLEX

Keywords: Knowledge representation ; semantic data models ; conceptual modelling Reference Record created on 2005-06-20, modified on 2016-08-08

On Conceptual Modelling

This paper deals with the history and definitions common to data-base technology It delimits the objectives of data-base management systems, discusses important concepts, and defines terminology for use by other papers in this issue, traces the development of data-base systems methodology, gives a uniform example, and presents some trends and issues

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Evolution of Data-Base Management Systems

The Data Base Management System is now a well established part of information systems technology, but the many architectures and their plethora of data models are confusing to both the practitioner and researcher. In the past, attempts have been made to compare and contrast some of these systems, but the greatest difficulty arises in seeking a common basis. This paper attempts to show how a generalized data system (GDS), represented by two different models, could form such a basis; it then proposes that data policy definitions can restrict the GDS to a specialized model, such as a relational or DBTG-like model. Finally, it proposes that this concept forms a better basis for data structure design of specific system applications.

Data architecture and data model considerations

: A data definition language is a declarative computer language for specifying data structures. Most data definition languages concentrate on the declaration of logical data structures with little concern for how these structures are physically realized on a computer system. However, the need for data definition languages which describe both the logical and physical aspects of data is increasingly apparent. Such languages will be a key element in the translation of data among computer systems, as well as in advanced data management systems and distributed data bases. The paper reviews past work in the data definition language for describing both logical and physical aspects of data. Applications of these generalized data definition languages are also discussed. (Author)

A data definition and mapping language

I should like to thank Elliott Organick for inviting me to serve as Guest Editor for this issue of COMPUTING SURVEYS, which has become our industry's most effective educational journal. I believe this issue deals with the most important topic in computing today: data-base technology. Here we attempt an integrated approach to a very disoriented field. Problems abound: differences in terminology, differences in modeling, and •differences in implementation confuse the potential user, who is faced with almost unanswerable questions, such a s : • Should I wait for the dust to settle, or start to use data-base technology now?; and • If I go data-base, which type of system do I choose? Such problems are found in any evolving technology, especially when it is associated with a fast developing industry, such as computing; while some problems appear more philosophical than real, others arise from a poor understanding of new concepts. Here we shall try to answer some of the questions and reduce the confusion. But obviously, one issue of COMPUTING SURVEYS cannot be all encompassing; data technology is a field which already boasts hundreds of articles, and textbooks by the dozen. Thus, this issue confines itself to an explanation of various models of data-base systems, showing their differences and similarities, while trying .to relate the models to their implementation in current conunercial and experimental systems. We were faced with one major problem in trying to provide an integrated issue: every model uses its own terminology. We therefore decided to attempt to use both a common terminology-:and a single• example wherever possible. This is. b y no means a simple task; we must not only define t~#rminology and apply it to descriptions of various models, but also show haw these terms differ f rom those used by others in discussing the same ideas. There is no standard, and we are forced to be arbitrary: It was not possible to discuss every model or implementation in one issue; in fact; it is difficult to deal with anything but basic concepts. We admit to errors and omissions, and apologize for them; a real attempt was made to solicit aid from a wide variety of experts. We issue them a blanket vote of thanks and apologize for inadvertent omissions:

Edgar H. Sibley

Papers

Evolution of Data-Base Management Systems

Data architecture and data model considerations

A data definition and mapping language

Guest Editor's Introduction: The Development of Data-Base Technology

Data architecture and data model considerations