Terms such as `data processing system', `management information system', `decision support system', `expert system' and `executive support system' are widely used. However, there appears to be no generally accepted way to distinguish among these classes of information systems due to the vagueness that has existed in traditional classification schemes of information systems. The purpose of this research is to establish an analytical framework based on a theoretical foundation, called Semiotics (the theory of signs) to understand, classify and compare information systems of various generations. Such a framework should simplify subsequent discussions and considerations in building new generations of information systems with complicated functions and capabilities. The proposed semiotics framework consists of 10 features that can be used to characterize the relationships between an information system and its users, and represent and organize the system's contents. The framework is applied to analyze and compare the semiotics features of information systems. Its relevance to system development, particularly system planning and requirements determination, is also discussed.

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 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.

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.

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.