Bitemporal data has always been important. But it was not until 2011 that the ISO released a SQL standard that supported it. Currently, among major DBMS vendors, Oracle, IBM and Teradata now provide at least some bitemporal functionality in their flagship products. But to use these products effectively, someone in your IT organization needs to know more than how to code bitemporal SQL statements. Perhaps, in your organization, that person is you.
To correctly interpret business requests for temporal data, to correctly specify requirements to your IT development staff, and to correctly design bitemporal databases and applications, someone in your enterprise needs a deep understanding of both the theory and the practice of managing bitemporal data. Someone also needs to understand what the future may bring in the way of additional temporal functionality, so their enterprise can plan for it. Perhaps, in your organization, that person is you.
This is the book that will show the do-it-yourself IT professional how to design and build bitemporal databases and how to write bitemporal transactions and queries, and will show those who will direct the use of vendor-provided bitemporal DBMSs exactly what is going on "under the covers" of that software.
- Explains the business value of bitemporal data in terms of the information that can be provided by bitemporal tables and not by any other form of temporal data, including history tables, version tables, snapshot tables, or slowly-changing dimensions
- Provides an integrated account of the mathematics, logic, ontology and semantics of relational theory and relational databases, in terms of which current relational theory and practice can be seen as unnecessarily constrained to the management of nontemporal and incompletely temporal data
- Explains how bitemporal tables can provide the time-variance and nonvolatility hitherto lacking in Inmon historical data warehouses
- Explains how bitemporal dimensions can replace slowly-changing dimensions in Kimball star schemas, and why they should do so
- Describes several extensions to the current theory and practice of bitemporal data, including the use of episodes, "whenever" temporal transactions and queries, and future transaction time
- Points out a basic error in the ISO’s bitemporal SQL standard, and warns practitioners against the use of that faulty functionality. Recommends six extensions to the ISO standard which will increase the business value of bitemporal data
- Points towards a tritemporal future for bitemporal data, in which an Aristotelian ontology and a speech-act semantics support the direct management of the statements inscribed in the rows of relational tables, and add the ability to track the provenance of database content to existing bitemporal databases
- This book also provides the background needed to become a business ontologist, and explains why an IT data management person, deeply familiar with corporate databases, is best suited to play that role. Perhaps, in your organization, that person is you
|Product dimensions:||7.40(w) x 9.10(h) x 0.90(d)|
About the Author
Dr. Tom Johnston is the Chief Scientist at Asserted Versioning, LLC, which has developed a middleware product which supports the standard theory of bitemporal data, and which also implements the Asserted Versioning extensions to that standard theory. He is the co-author of Managing Time in Relational Databases (Morgan-Kaufmann, 2010). He lives in Atlanta, Georgia.
Table of Contents
1. Basic Concepts.
Part I. Theory 2. Time and Temporal Terminology 3. The Relational Paradigm: Mathematics 4. The Relational Paradigm: Logic 5. The Relational Paradigm: Ontology 6. The Relational Paradigm: Semantics 7. The Allen Relationships 8. Temporal Integrity Concepts 9. Temporal Entity Integrity 10. Temporal Referential Integrity
Part II. Practice 11. Temporal Transactions 12. Basic Temporal Queries 13. Advanced Temporal Queries 14. Future Assertion Time 15. Temporal Requirements 16. Bitemporal Data and the Inmon Data Warehouse 17. Semantic Integration via Messaging 18. Bitemporal Data and the Kimball Data Warehouse 19. The Future of Relational Databases