Everything written on my blog has been tested on my local environment, Please test before implementing or running in production. You can contact me at amit. View my complete profile. Few days ago when I was trying to start my database , I faced this error. Solution :- There are two solutions to the above problem. If data in this file is not that much important that remove this file information from database and open your database.
If data in this file is important and you have backup available for this file and if your database is in archivelog mode than you can recover the data in this file upto the last commit. Hi Tom, If control file has all the info , do we have a reason to use the catalog? We are currently using catalog but exploring an option of eliminating, do you see any issues if we do not use catalog?
A control file has a maximum keep time. Usually set to 7 days. If you decide not to keep a repository. You will want to set this parameter high enough so that your oldest backup control file record is not overwritten. The larger you set this value the bigger your control files will get.
Thanks for the response, We have around databases and we were using catalog and performance was slow when we tried to sync, we opened up tar with oracle but not much use. I understand we will not have central repository if we do not use the catalog, but do we loose any recovery functionality if we do not use repository assuming we do regular control file backups. In other words is there any recovery scenario possible with repository and not with control file.
I am a regular visitor of your website, and appreciate your insight and time. Thank you so much. March 15, - am UTC. Not sure what your "SLA" has to do with it, but you would of course configure everything as per your need - yes - of course.
Suggestion would be to use the recovery catalog in order to meet any sort of SLA, when you go to recover, you want it to be easy, smooth, 'simple'. Doing everything standalone will be none of the above. Dear Tom, We are using oracle 9. I had taken backup on disk through rman and moved backup set to different location reason behind this is to know RMAN inform me which backup files he required to recover my database.
He is just showing message as below. March 24, - am UTC. One thing that hasn't been mentioned as yet, is that if you use a repository you can store your RMAN scripts in it.
You don't have this functionality if you just use the control file. AIO enabled. Currently RMAN backup is taking hrs to complete. Currently there is no multiplexing enabled for backup sets. My query is how to decide 'what should be the degree of multiplexing', if it needs to be enabled.
If higher degree of multiplexing is opted, will it impact the performance of recovery?? April 23, - pm UTC. As such, a larger value of the membership function indicates a higher degree of membership. Fuzzy sets are a generalizations of classical set theory and allow elements to belong to many sets at the same time. Fuzzy sets have been widely applied in data mining and machine learning, leading to fuzzy data mining.
This term can be understood in two different ways. Either the datasets are fuzzy or the mining itself, the model building, is fuzzy. Instead, every observation comes with a membership value. Subsequently the fuzzy data can be analysed with extended versions of standard data mining techniques or the analyses can be carried out in fuzzy spaces. The second approach to fuzzy data mining is to employ the principle of fuzziness in the model building itself.
Various application exist and we will discuss a few. Fuzzy frameworks are abstract mathematical entities which have been successfully applied in various data mining applications.
It is an active field of research in machine learning and AI since fuzzy sets gives a more representative description of the world we live in, where not everything is either black or white.
Bart Baesens Prof. Seppe vanden Broucke. Clustering : k -means clustering is a common unsupervised data mining technique.
This method assigns each observation to one of the predefined k clusters which has the closest mean. Thus, each instance of the dataset belongs to only one cluster and there are crisp boundaries between them.
In fuzzy clustering, an observation can belong to many clusters, to a certain extent. This partial belonging is described with the membership function.
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