Temporal Data & the Relational Model (The Morgan Kaufmann Series in Data Management Systems)

There can be no denying, I believe, that insofar as current major Database Management Systems (DBMS) are concerned, temporal data represents a significant problem: it is simply not possible to declaratively constrain the integrity of temporal data in SQL databases (or non-temporal data, even). I am not alone I am sure in having had to deal with duplicate, triplicate, quadruplicate, etc., records covering, or abutting, or overlapping on the same temporal periods. Not to mention the hoops that must be jumped through to reliably manipulate those same data.Date, Darwen, and Lorentzos have produced a formidable work here on applying some badly needed rigid logic to the whole sphere of temporal data within databases. And that rigid logic is afforded by the Relational Model. They consider three variants on the temporal data theme as vehicles for explanation and demonstration: 1. Semitemporal with current data only, 2. Temporal with current and historical data held within the same relvars (tables), 3. Temporal with current and historical data split into separate relvars. Temporal data is a complex area, so this book has, inevitably, had to get 'down and dirty' with the detail, but the authors are clear and comprehensive throughout. A thorough familiarity with the Relational Model will help, as will any previous experience of their Relational language `Tutorial D', though they go though both in the first two introductory chapters.We would indeed be much better off were the DBMS vendors to take serious note of the powerful logical arguments laid forth in this volume, and far from interpreting their reflections (criticisms) on NULLs, etc., as 'political', I would see them only as further reaffirmations of the principles that have led them to invest so much endeavour and thought into the problems and very real deficiencies of data modelling and integrity, specifically in regard to the Relational Model. And for that we should indeed be thankful.