TEMPORAL DATABASES (TEMPORAL EXTENSIONS)

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Throughout the 1990s, and perhaps even a little before that, a large community of academics spent a huge amount of time conducting research into so-called temporal databases. A temporal database supports some aspect of time. We might think that all modern RDBMSs support time because of the DATE/TIME data type. This is not what temporal databases are about. The DATE/TIME data type is a kind of user -defined time support. A real temporal database has time support within the structure of the DBMS. The schema tables, the query processor, etc., all have to have a fundamental understanding of time. We need to be able to ask questions like:

How many people move to a larger house within one year of their salary exceeding $50,000 for the first time?

The issues surrounding time are thoroughly documented in Chapter 4 of this book. The importance of time cannot be overstated in data warehousing. Data warehouses are temporal databases but without the support of any real temporal DBMS. The research community has failed, after many years of research and thousands of papers, to come up with any meaningful solution. Part of the problem, in my view, is a failure in principle to accept that time is a fundamental attribute, another dimension. Almost all the research seems to be focused on how we might modify the relational model to accommodate time. On each occasion that some luminary presents a paper describing how the relational model might be adapted , another presents a counterargument showing how that adaptation violates one or more of the rules that comprise relational theory.

There are also proposals regarding potential extensions to the SQL language. These often include the incorporation of a WHEN clause, so we can phrase a query to return rows, not only where a condition is true but also, when it was true. So far, no major RDBMS vendor has implemented these changes. There are some third-party produced packages available for some products that purport to provide some of these facilities, but their efficacy and scalability are unproven.

Perhaps the main problem is that relational tables are two dimensional. Time adds another dimension, and any attempt to force fit a solution is bound to fail. Oddly enough, I believe there may be a link between the multidimensional nature of data warehouses and the need to incorporate time as a dimension in other types of database, especially given the fact that data warehouses are temporal. It may be that the data warehouse techniques we use today provide the key to this seemingly intractable problem.

What we need is not a modified version of an RDBMS. We need a new model, as different from relational databases as relational databases are from network databases or object-oriented databases. It is to be hoped that this problem will be resolved in the not too distant future, but don't hold your breath .

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