Terence M. Barron

Improving database design through the analysis of relationships

Much of the work on conceptual modeling involves the use of an entity-relationship model in which binary relationships appear as associations between two entities. Relationships involving more than two entities are considered rare and, therefore, have not received adequate attention. This research provides a general framework for the analysis of relationships in which binary relationships simply become a special case. The framework helps a designer to identify ternary and other higher-degree relationships that are commonly represented, often inappropriately, as either entities or binary relationships. Generalized rules are also provided for representing higher-degree relationships in the relational model. This uniform treatment of relationships should significantly ease the burden on a designer by enabling him or her to extract more information from a real-world situation and represent it properly in a conceptual design.

A framework for the design and evaluation of reverse engineering methods for relational databases

Abstract It is often difficult to obtain a good conceptual understanding of a legacy database, especially when there is a lack of documentation. Database reverse engineering attempts to provide solutions for this problem. It is the part of system maintenance work that produces a sufficient understanding of a legacy database and its application domain to allow appropriate changes to be made. However, research on database reverse engineering has largely ignored design and evaluation issues of their methods (i.e., foundations and processes). This research proposes a framework for the design and evaluation of reverse engineering methods of relational databases. This framework consists of eight criteria: 1) the situation chosen as the basis for reverse engineering, 2) the conceptual model chosen to represent the reverse engineering results, 3) the prerequisites of the database to be reverse engineered, 4) the thoroughness of domain semantics acquisition, 5) rules and heuristics employed by the reverse engineering process, 6) performance efficiency of the reverse engineering process, 7) completeness and robustness and 8) validation. These criteria are important to be considered in designing reverse engineering methods, so that they can perform reverse engineering for a broad range of legacy databases at a high level of automation and provide a conceptual schema that is semantically rich and correct.

A complete temporal relational algebra

Abstract. Various temporal extensions to the relational model have been proposed. All of these, however, deviate significantly from the original relational model. This paper presents a temporal extension of the relational algebra that is not significantly different from the original relational model, yet is at least as expressive as any of the previous approaches. This algebra employs multidimensional tuple time-stamping to capture the complete temporal behavior of data. The basic relational operations are redefined as consistent extensions of the existing operations in a manner that preserves the basic algebraic equivalences of the snapshot (i.e., conventional static) algebra. A new operation, namely temporal projection, is introduced. The complete update semantics are formally specified and aggregate functions are defined. The algebra is closed, and reduces to the snapshot algebra. It is also shown to be at least as expressive as the calculus-based temporal query language TQuel. In order to assess the algebra, it is evaluated using a set of twenty-six criteria proposed in the literature, and compared to existing temporal relational algebras. The proposed algebra appears to satisfy more criteria than any other existing algebra.

A conceptual model for the logical design of temporal databases

Although widely advocated as a tool for the conceptual modelling of data, the Entity-Relationship (E-R) model [4] and its extensions are generally lacking in constructs to model the dynamic nature of the real world, making them inadequate for designing temporal databases. This research first extends the E-R model to a Temporal Event-Entity-Relationship Model (TEERM), by introducing events as an additional construct. Second, a method is proposed for mapping this conceptual model into a temporal relational model for the logical design of temporal relational databases with a corresponding set of integrity constraints. The model is illustrated with an example and evaluated using a set of criteria proposed by ^B^a^t^i^n^i^ ^e^t^ ^a^l^.^ ^[^2^]. The model appears to be expressive, simple and easy to use, and should, therefore, aid the temporal database design process significantly.

Approximate dependencies in database systems

Abstract Functional dependencies are the most commonly used approach for capturing real-word integrity constraints which are to be reflected in a database. There are, however, many useful kinds of constraints, especially approximate ones, that cannot be represented correctly by functional dependencies and therefore are enforced via programs which update the database, if they are enforced at all. This tends to make such constraints invisible since they are not an explicit part of the database, increasing maintenance problems and the likelihood of inconsistencies. We propose a new approach, cluster dependencies, as a way to enforce approximate dependencies. By treating equality as a fuzzy concept and defining appropriate similarity measures, it is possible to represent a broad range of approximate constraints directly in the database by storing and accessing cluster definitions. We discuss different interpretations of cluster dependencies and describe the additional data structures needed to enforce them. We also contrast them with an existing approach, fuzzy functional dependencies, which are much more limited in the kind of approximate constraints they can represent.

A decision model for choosing the optimal level of storage in temporal databases

A database allows its users to reduce uncertainty about the world. However, not all properties of all objects can always be stored in a database. As a result, the user may have to use probabilistic inference rules to estimate the data required for his decisions. A decision based on such estimated data may not be perfect. The authors call the costs associated with such suboptimal decisions the cost of incomplete information. This cost can be reduced by expanding the database to contain more information; such expansion will increase the data-related costs because of more data collection, manipulation, storage, and retrieval. A database designer must then consider the trade-off between the cost of incomplete information and the data-related costs, and choose a design that minimizes the overall cost to the organization. In temporal databases, the sheer volume of the data involved makes such a trade-off at design time all the more important. They develop probabilistic inference rules that allow one to infer missing values in spatial, as well as temporal, dimension. They then use the framework for developing guidelines for designing and reorganizing temporal databases, which explicitly includes a trade-off between the incomplete information and the data-related costs.

Optimal Information Structures for the Seller of a Search Good

This paper examines an information system design problem faced by the seller of a search good who sells his product in a competitive market to well-informed consumers. The formulation results in a nonlinear optimization problem having a special structure which can be exploited in solving the first-order conditions. Closed-form solutions and comparative statics results are given in the case of a uniformly-distributed attribute, and we provide a numerical example in the case of a normally-distributed attribute.