A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information about the real world. A special diagrammatic technique is introduced as a tool for database design. An example of database design and description using the model and the diagrammatic technique is given. Some implications for data integrity, infor-mation retrieval, and data manipulation are discussed. The entity-relationship model can be used as a basis for unification of different views of data: t,he network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented. Key Words and Phrases: database design, logical view of data, semantics of data, data models, entity-relationship model, relational model, Data Base Task Group, network model, entity set

Modeling the storage structures of a DBMS is a prerequisite to understanding and optimizing database performance. Previously, such modeling was very difficult because the fundamental role of conceptual-to-internal mappings in DBMS implementations went unrecognized. In this paper we present a model of physical databases, called the transformation model, that makes conceptual-to-internal mappings explicit. By exposing such mappings, we show that it is possible to model the storage architectures (i.e., the storage structures and mappings) of many commercial DBMSs in a precise, systematic, and comprehendible way. Models of the INQUIRE, ADABAS, and SYSTEM 2000 storage architectures are presented as examples of the model’s utility. We believe the transformation model helps bridge the gap between physical database theory and practice. It also reveals the possibility of a technology to automate the development of physical database software.

This paper presents in tutorial fashion the concepts, notation, aud data-base languages that were defined by the CODASYL Data Description Language and Programming Language Committees. Data structure diagram notation is ex-plained, and sample data-base definition is developed along with several sample programs. " Advanced features of the languages are discussed, together with examples of their use. An extensive bibliography is included.

. We have begun a long-term project to build a new kind of database and its enhanced, supporting database management system (DBMS) for international neuroscience research. Because brain research occurs world-wide, our database will be distributed, encouraging rapid, open dissemination of results to a broad audience of neuroscientists. It will conjoin information and experimental results from many disciplines. We envision a zoomable database of the brain tissue itself, in large part embedded in three dimensions (3D), through which one can "fly." Within this coarse structure, the database will also organize fine-structural, functional and behavioral data. As often as possible, the database will express experimental data in its purest, least analyzed form, so that expensive raw data can be analyzed and reanalyzed by researchers worldwide. We believe that our project will profoundly effect the way in which neuroscience is done, while providing key areas for database research and distribute...

We present a conceptual framework in which a database’s intra- and interrecord set access require-ments are specified as a constrained assignment of abstract characteristics (“evaluated, ” “indexed,” “clustered, ” ‘I well-placed”) to logical access paths. We derive a physical schema by choosing an available storage structure that most closely provides the desired access characteristics. We use explicit replication of schema objects to reduce the access cost along certain paths, and analyze the trade-offs between increased update overhead and improved retrieval access. Finally, we give an algorithm to select storage structures for a CODASYL 78 DBTG schema, given its access requirements specification.