Very similar concepts that apply to different areas of object oriented world.
Each one acts like a boundary to define one universe.
Sorry, if you're not a fan of SAT, but let me try this--
Namespace : Dataset = Classes & Attributes : Class Instances (or Data)
or
Namespace : Dataset = Class Definition : Class Data
or
Namespace : Dataset = World in which data's structure is defined, named:
World in which actual data is stored
In other words, when you talk about two different namespaces, A and B, you
are talking about two separate and independent worlds in which you can
define entities (definition of your data's structure) separately. The
namespaces are independent of each other in the sense that you can define an
entity, say "MyClassName", regardless of whether this name is already
defined in the other or not. Because they are separate worlds, for the
purpose of naming entities (entities=classes and attributes).
When you talk about two datasets...you are talking about two separate
containers to represent two different versions (i.e. data) of the same real
world object(s). For example, your "Production" dataset for CIs in CMDB
contain official CI details as they are recognized now. Your "Discovered"
dataset will have information about the same CIs, but as discovered by some
tool. They may or may not match.
In both datasets, you may have a an instance of a CI (.i.e. data about its
state at some point), say your laptop123. In production dataset, data
indicates that laptop123 is a healthy system. In discovered dataset, the
same laptop123 may be represented as a screwed up system on which your dog
last weekend spilled coffee and when you tried punching the dog, you broke
the CD drive, instead.
Note that the structure definition for both datasets (i.e. the class and
attributes definitions) are identical, so they would fall under the same
namespace.
I feel like writing more, so here it goes. :)
My Class Definitions (i.e. definition of my data's structure)
=================
Namespace: MyPlayNamespace
Class: Person
Class Attributes:
Name
Attractiveness
Money
Cool Factor
Namespace: MySeriousNamespace
Class: Person (same name is allowed, because this is a different namespace)
Class Attributes:
Ethnicity Name
Average Volume
Average # of Drinks Before Passing Out
My Class Instances (i.e. my actual data)
=============================
Namespace: MyPlayNamespace
Class: Person
Dataset: "young" (this is one version of the real world entities, the
people; so "young" represents the "version number" for this collection of
data)
Data (this dataset has 3 class instances):
Name: Rabi
Attractiveness: High
Money: Low
Cool Factor: High
Name: Labi
Attractiveness: High
Money: High
Cool Factor: Superhigh
Name: Babi
Attractiveness: Low
Money: Low
Cool Factor: Superlow
Dataset: "old" (this is another version of the same real world entities)
Data (3 class instances):
Name: Rabi
Attractiveness: Low
Money: Medium
Cool Factor: Medium
Name: Labi
Attractiveness: Low
Money: Low
Cool Factor: Low
Name: Babi
Attractiveness: Low
Money: High
Cool Factor: Medium
----------
The other namespace "MySeriousNamespace" that also has a class with the same
name, "Person", can similarly have its data defined in more than one
datasets, with any names. Note that in this namespace, the "Person"
instances happens to represent different real world entities. It really
defines defines human ethnicities, not individuals like the "Person" class
in "MyPlayNamespace"
So, namespaces define totally separate universes. Within each namespace,
for a given class(es), you can have one or more datasets, each dataset
representing data about the different versions (perhaps at different times,
or from different experiments etc) of the classes's real world objects.
I wrote too much. Now I want to stop.
Drew Shuller wrote:
>
> Hello everyone,
>
> I'm a little confused as to the difference between a dataset and a
> namespace. Any help? Thanks a bunch!
>
> Drew
> Tulsa, OK
>
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