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Tools and transformations—rigorous and otherwise—for practical database design

Published:01 June 1994Publication History
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Abstract

We describe the tools and theory of a comprehensive system for database design, and show how they work together to support multiple conceptual and logical design processes. The Database Design and Evaluation Workbench (DDEW) system uses a rigorous, information-content-preserving approach to schema transformation, but combines it with heuristics, guess work, and user interactions. The main contribution lies in illustrating how theory was adapted to a practical system, and how the consistency and power of a design system can be increased by use of theory.

First, we explain why a design system needs multiple data models, and how implementation over a unified underlying model reduces redundancy and inconsistency. Second, we present a core set of small but fundamental algorithms that reaarange a schema without changing its information content. From these reusable components, we easily built larger tools and transformations that were still formally justified. Third, we describe heuristic tools that attempt to improve a schema, often by adding missing information. In these tools, unreliable techniques such as normalization and relationship inference are bolstered by system-guided user interactions to remove errors. We present a rigorous criterion for identifying unnecessary relationships, and discuss an interactive view integrator. Last, we examine the relevance of database theory to building these practically motivated tools and contrast the paradigms of system builders with those of theoreticians.

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  1. Tools and transformations—rigorous and otherwise—for practical database design

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          Riccardo Torlone

          Rosenthal and Reiner describe the tools provided by Data base Design and Evaluation Workbench (DDEW), a system prototype for database design. DDEW supports multiple design methodologies, including techniques for design from scratch, reverse engineering, and integration of schemas. The data model at the conceptual level is a version of the entity-relationship data model called ER+, but at the logical level DDEW uses a relational, network, or hierarchical data model. It is disappointing that ER+ is limited in that it supports only binary relationships with no attributes, and does not support generalization hierarchies. The main features of DDEW are the use of a unified underlying data model (ER+) and of a collection of content-preserving schema transformations (called rearrangements). Specifically, the ER+ model is used to represent the various stages of database design. Thus, the output of the logical design in the relational model is indeed a schema in the relational style of ER+ (in which all the relationships are determined by matches of attribute values). Moreover, the schema transformations needed in the various design phases (refinements, integrations, and so on) can be implemented by using a set of predefined modules that preserve the information content of a schema. The major contribution of this paper is to show how theoretical results in database design have been combined with heuristics, guesswork, and user interactions in the realization of the system. I found s ection 5, in which the authors discuss the contributions and limitations of the theoretical literature on building a practical database design system, particularly interesting. The authors also provide general suggestions to theoreticians who want to make their work more useful to system builders. The paper is well written and easy to read for anyone with some knowledge of database design.

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