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01 BackTrack



When you enable Aurora Backtrack in the RDS management console, you specify how long to retain data records, and you pay for the space these records use. For example, you could set up Backtrack to allow you to move your database up to 72 hours back. Backtrack is also useful for development and testing, particularly in situations where your test deletes or otherwise invalidates the data. Simply backtrack to the original database state, and you're ready for another test run.




01 BackTrack



You can now hear the entire @BacktrackNYHC flexi 7" Bad To My World b/w Breaking Loose (exclusive B-side) right now on Spotify / iTunes/ Bandcamp / etc! #BadToMyWorld Listen to both songs and if you haven't ordered the 32 page fanzine / flexi preview to the album, grab it at B9Store.com/backtrack shipping next week!


Viewed 1000+ timesYou Asked Hi Tom, I want to backtrack each single record to find its original states. The student table contains the updated values only, however student history contains what was the old value for that updated columns.create table student (id number(6) primary key,name varchar2(50),city varchar2(50),address varchar2(100),createdDateTime date,updatedDatetime date);insert into student values(1,'abc1','abc1','abc1','09-Jan-20','12-Jan-20');insert into student values(2,'pqr','pqr','pqr','09-Jan-20',null);student table-ID Name City Address Create_time UpdatedTime1 abc1 abc1 abc1 09-Jan-20 12-Jan-202 pqr pqr pqr 09-Jan-20 nullcreate table studentHistory(id number(6) ,name varchar2(50),city varchar2(50),address varchar2(100),DatetimeCreated date);insert into StudentHistory values(1,null,'abc',null,'10-Jan-20');insert into StudentHistory values(1,'abc',null,null,'11-Jan-20');insert into StudentHistory values(1,null,null,'abc','12-Jan-20');Student history table-ID Name City Address DatetimeCreated1 null abc null 10-Jan-201 abc null null 11-Jan-201 null null abc 12-Jan-20required output will be each stages of single record-ID Name City Address DatetimeCreated LastUpdated1 abc abc abc 09-Jan-20 null1 abc abc1 abc 09-Jan-20 10-Jan-201 abc1 abc1 abc 09-Jan-20 11-Jan-201 abc1 abc1 abc1 09-Jan-20 12-Jan-202 pqr pqr pqr 09-Jan-20 null and Connor said...SQL> create table student 2 ( 3 id number(6) primary key, 4 name varchar2(50), 5 city varchar2(50), 6 address varchar2(100), 7 createdDateTime date, 8 updatedDatetime date);Table created.SQL>SQL> insert into student values(1,'abc1','abc1','abc1','09-Jan-20','12-Jan-20');1 row created.SQL> insert into student values(2,'pqr','pqr','pqr','09-Jan-20',null);1 row created.SQL>SQL>SQL> create table studentHistory 2 ( 3 id number(6) , 4 name varchar2(50), 5 city varchar2(50), 6 address varchar2(100), 7 DatetimeCreated date 8 );Table created.SQL>SQL> insert into StudentHistory values(1,null,'abc',null,'10-Jan-20');1 row created.SQL> insert into StudentHistory values(1,'abc',null,null,'11-Jan-20');1 row created.SQL> insert into StudentHistory values(1,null,null,'abc','12-Jan-20');1 row created.---- first step is to get all the records we'll need--SQL>SQL> with all_recs as 2 ( -- initial creation 3 select id, name, city, address, createdDateTime, cast(null as date) updatedDatetime 4 from student 5 union all 6 -- all the updates 7 select id, name, city, address, null createdDateTime, DatetimeCreated 8 from studentHistory 9 ) 10 select * 11 from all_recs 12 order by id, updatedDatetime nulls first; ID NAME CITY ADDRESS CREATEDDA UPDATEDDA---------- -------------------- ---------- ---------- --------- --------- 1 abc1 abc1 abc1 09-JAN-20 1 abc 10-JAN-20 1 abc 11-JAN-20 1 abc 12-JAN-20 2 pqr pqr pqr 09-JAN-205 rows selected.---- now I'll use ROW_NUMBER to assign an order based on create/update timestamp--SQL>SQL>SQL> with all_recs as 2 ( -- initial creation 3 select id, name, city, address, createdDateTime, cast(null as date) updatedDatetime 4 from student 5 union all 6 -- all the updates 7 select id, name, city, address, null createdDateTime, DatetimeCreated 8 from studentHistory 9 ) 10 select 11 id, 12 name, 13 city, 14 address, 15 createdDateTime, 16 updatedDatetime, 17 row_number() over ( partition by id order by nvl(createdDateTime,updatedDatetime)) as seq 18 from all_recs 19 order by id, updatedDatetime nulls first; ID NAME CITY ADDRESS CREATEDDA UPDATEDDA SEQ---------- -------------------- ---------- ---------- --------- --------- ---------- 1 abc1 abc1 abc1 09-JAN-20 1 1 abc 10-JAN-20 2 1 abc 11-JAN-20 3 1 abc 12-JAN-20 4 2 pqr pqr pqr 09-JAN-20 15 rows selected.---- then I can use NVL/LAG to "fill in the blanks" as needed--SQL>SQL>SQL>SQL> with all_recs as 2 ( -- initial creation 3 select id, name, city, address, createdDateTime, cast(null as date) updatedDatetime 4 from student 5 union all 6 -- all the updates 7 select id, name, city, address, null createdDateTime, DatetimeCreated 8 from studentHistory 9 ), 10 sequenced_rows as ( 11 select 12 id, 13 name, 14 city, 15 address, 16 createdDateTime, 17 updatedDatetime, 18 row_number() over ( partition by id order by nvl(createdDateTime,updatedDatetime)) as seq 19 from all_recs 20 ) 21 select 22 id, 23 city, 24 address, 25 nvl(createdDateTime, 26 lag(createdDateTime ignore nulls) over ( partition by id order by seq ) ) createdDateTime, 27 updatedDatetime 28 from sequenced_rows; ID CITY ADDRESS CREATEDDA UPDATEDDA---------- ---------- ---------- --------- --------- 1 abc1 abc1 09-JAN-20 1 abc 09-JAN-20 10-JAN-20 1 09-JAN-20 11-JAN-20 1 abc 09-JAN-20 12-JAN-20 2 pqr pqr 09-JAN-205 rows selected.SQL>SQL> Is this answer out of date? If it is, please let us know via a Comment Comment Connor and Chris don't just spend all day on AskTOM. You can also catch regular content via Connor's blog and Chris's blog. Or if video is more your thing, check out Connor's latest video and Chris's latest video from their Youtube channels. And of course, keep up to date with AskTOM via the official twitter account. More to Explore Analytics Analytic SQL got you confused? Check out Connor McDonald's complete video course.


The realization resulted in [3], that any time series may be regarded equivalent to an objective function subject to the factors affecting its price, motivated the research of time series optimization for forecasting. Thus, in [4] the crisis forecasting issue was dealt under the perspective of approximating significant changes of a time series that consists of estimations of the local Lipschitz constant. Simply, the Lipschitz constant held the role of the indicator and the application of the backtrack algorithm enabled its integration with past data. However, a drawback of this backtrack applications is that it is not clarified whether the resulting future point is the actual optimum one, or it is just a point that eventually might lead to an optimum one.


Note, that in this study it is attempted not to focus on statistical issues but concentrate on exploiting the properties of the backtrack optimization technique towards forecasting potential financial crisis periods.


The findings provided in [17] regarding the different convergence region according to the optimization technique used, motivated researching two techniques for the generation of an interval. The concept of the combinatory application of two techniques was further synthesized with the findings of [3] where the accommodation of time-oriented forecasts was succeeded based on the backtrack technique.


However, a major drawback of the backtrack methods is that the estimated future point resulting from their application is not apparent whether it is the actual optimum or just a point that would eventually lead to an optimum. In fact it appears that the estimation of the future optimum is uncertain in means of suggesting a future point rather than a future interval. 350c69d7ab


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