Friday, August 19, 2011

On temporal databases and DB2

I have written before about temporal databases in this Blog, and in general I worked on temporal databases around 15 years ago, and co-edited the book whose cover is shown here in 1998.  Temporal database is noted by the fact that academic people tried to drive standard in this area: TSQL2 lead by Rick Snodgrass and his colleagues. At that time it did not succeed since none of the DBMS vendors had an interest to see it as a high priority, these were the days were the Internet emerged along with XML, and the DBMS vendors had many other things to worry about.  However over the time  some DBMS vendors have adopted temporal capabilities within their products. Oracle already implemented temporal extensions supporting TSQL in its DBMS product.  Recently IBM produced its own version of temporal database within DB2.  It seems that there is now traction for temporal databases in various industries.    Today my colleague Guy Sharon attracted my attention to a new article on IBM DeveloperWorks entitled "going back in time" describing the DB2 temporal capabilities,  the traditional dimensions in temporal databases: transaction time and valid time got converted to new names:  system time and business time  (I make a note to write a post about the overuse of the term "business" ).     These two dimensions enable to ask queries like: what was the value of a certain attribute in 7/7/2011, as observed from 8/8/2011.  This can have different answer from the observation time of different days, since the knowledge about the past is changed in time.  While the title of that article talks about "going back in time", and indeed using temporal databases is typically viewed about recording the past, temporal databases can also be used about recording the future, this was noted in a work published in 1994 by Arie Segev, Avi Gal and myself entitled "retroactive and proactive database processing"  (I don't think that online version is available).  Since we are dealing over the last year in proactive event-driven processing, the issue of looking at predicted future events that can be revised with time is very useful, and we are indeed looking on temporal database techniques for that.  More on that - later.

No comments: