An introduction to deductive database languages and systems

One of the most fundamental uses of a computer is to store and retrieve information, particularly when there are a large amount of data to be stored, or there are complex manipulations that must be performed on them. There has been a large amount of research on the most efficient techniques to store and retrieve data, and the associated problems now have satisfactory solutions. However, the problem of understanding and interpreting this large amount of information remains, particularly when the amounts of data belong to complex domains, such as those involving mineral exploration and financial analysis. To tackle this problem, a mechanism for reasoning about the stored information is necessary. Such a mechanism must be able to cope with large amounts of information, as well as to perform sophisticated inferences, and to draw the appropriate conclusions. A framework in which these problems may be attacked is given by the field of deductive databases. Deductive databases not only store explicit information in the manner of a relational database, but they also store rules that enable inferences based on the stored data to be made. This area is an outgrowth of the field of logic programming, in which mathematical logic is used to directly model computational concepts. Together with techniques developed for relational databases, this basis in logic means that deductive databases are capable of handling large amounts of information as well as performing reasoning based on that information. There are many application areas for deductive database technology. One area is that of decision support systems. In particular, the exploitation of an organization's resources requires fi~tbniy sufficient information about the current and future status of the resources themselves, but also a way of reasoning effectively about plans for the future. The present generation of decision support systems are severely deficient when it comes to reasoning about future plans. Deductive database technology is an appropriate solution to this problem. Another fruitful application area is that of expert systems. There are many computing applications in which there are large amounts of information, from which the important facts may be distilled by a simple yet tedious analysis. For example, medical analysis and monitoring can generate a large amount of data, and an error can have disastrous consequences. A tool to carefully monitor a patient's condition or to retrieve relevant cases during diagnosis reduces the risk of error in such