To remove multi-valued fields/remove repeating group
• To establish the entities
• To establish which attributes belong in each entity
• To establish relationships between entities
• To avoid update/deletion/insertion anomalies (Max 1 mark)
• To overcome the limitations of flat files (including prevent data
duplication/data inconsistency errors)
• Identify both primary and foreign keys (1 mark needs both primary and foreign)
In the normalising metal treatment process, the metal is cooled slowly and gradually while in quenching metal treatment process the metal is called very fast and abruptly.
Data recovery is missing,not having proper database ar ethe main reasons.
If data doesn't support a hypothesis, something is being overlooked. The most likely possibility is that the hypothesis is false. The second possibility is that some variable hasn't been properly accounted for, in which case something is being overlooked.
Qualitative data refers to non-numeric information that helps to understand behaviors, opinions, and experiences. It is gathered through methods like interviews, observations, and focus groups to explore underlying reasons, motivations, and understandings of a phenomenon.
basically the data collection is a very defficult work in the field oceanography because the human have not being able to reach the approach of deep sea that contains a great biological diversity and other hand they couldn,t drill the deep oceanic crust to obtain geological data,s. so these main problem being faced by human.
In the normalising metal treatment process, the metal is cooled slowly and gradually while in quenching metal treatment process the metal is called very fast and abruptly.
Some of the reasons why it may become impossible to recover some data is if the data is corrupted or if there is malicious deleting. Read and write errors are common reasons why data can get corrupted.
Re-scaling or (for selected vales of the same number) normalising.
Data marts are created for various reasons. They're easy to access, easy to create, create collective views for groups, is not cluttered, and cost less to implement than a data warehouse.
Two reasons why data might not support a hypothesis are that the experiment had a flaw or was not repeated enough times. This happens a lot.
Two reasons why data might not support a hypothesis are that the experiment had a flaw or was not repeated enough times. This happens a lot.
Two reasons why data might not support a hypothesis are that the experiment had a flaw or was not repeated enough times. This happens a lot.
The answer depends onthe total marks available,normalising procedure, andgrade boundaries.
2 reasons: - To obtain more free spaces. - To remove away unwanted data & information.
Hi, There are many reasons for need Data warehouse for any company.if company want to improve their performance ,operation and want to grow with large data of them, all of the reasons are create requirement of Data warehouse for any company. By Anaya, The Cheesy Animation factory(India, Ahmedabad)
1.fast data processing 2.fast data retrieval
It happens for many reasons like if their was a data fault and a loose fuse