Databases Reference
In-Depth Information
(i) An Asserted Versioning Ontology. A research topic. We have
begun to formalize Asserted Versioning as an ontology by
translating our Glossary into a FOPL axiomatic system.
The undefined predicates of the system are being collected
into a controlled vocabulary. Multiple taxonomies will be
identified as KIND-OF threads running through the ontol-
ogy. Theorems will be formally proved, demonstrating
how automated inferencing can extract useful information
from a collection of statements that are not organized as
a database of tables, rows and columns.
(ii) Asserted Versioning and the Relational Model. A research
topic. Bi-temporal extensions to the SQL language have
been blocked for over 15 years, in large part because of
objections that those extensions violate Codd's relational
model and, in particular, his Information Principle. We will
discuss those objections, especially as they apply to
Asserted Versioning, and respond to them.
(iii) Deferred Transaction Workflow Management and the AVF.
A development topic. When deferred assertion groups are
moved backwards in assertion time, and when isolation
cannot be maintained across the entire unit of work, vio-
lations of bi-temporal semantics may be exposed to the
database user. We are developing a solution that identifies
semantic components within and across deferred asser-
tion groups, and moves those components backwards in
a sequence that preserves temporal semantic integrity at
each step of the process.
(iv) Asserted Versioning and Real-Time Data Warehousing.
A methodology topic. Asserted Versioning supports bi-
temporal tables in OLTP source system databases and/or
Operational Data Stores. It is a better solution to the man-
agement of near-term historical data than is real-time data
warehousing, for several reasons. First, much near-term
historical data remains operationally relevant, and must
be as accessible to OLTP systems as current data is. Thus,
it must either be maintained in ad hoc structures within
OLTP systems, or retrieved from the data warehouse with
poorly-performing federated queries. Second, data ware-
houses, and indeed any collection of uni-temporal data,
do not support the important as-was vs. as-is distinction.
Third, real-time feeds to data warehouses change the
warehousing paradigm. Data warehouses originally kept
historical data about persistent objects as a time-series of
periodic snapshots. Real-time updating of warehouses for-
ces versioning into warehouses, and the mixture of
