Are all relations in bcnf provide lossless join or can any relation be bcnf but not lossless join at the same tiem?
Is there a way to show that a relation that is not in bcnf but NOT lossless join.
There are schemas in which no decomposition to 3.5NF is dependency-preserving. See (Tsou 1982) for an example.
Related
Please tell me what is difference between hierarchical, network and relational data models?
Hierarchical model
1.One to many or one to one relationships.
2.Based on parent child relationship.
3.Retrieve algorithms are complex and asymmetric
4.Data Redundancy more
Network model
1.Many to many relationships.
2.Many parents as well as many children.
3.Retrieve algorithms are complex and symmetric
4.Data Redundancy more
Relational model
1.One to One,One to many, Many to many relationships.
2.Based on relational data structures.
3.Retrieve algorithms are simple and symmetric
4.Data Redundancy less
As we know, the referential constraints are not enforced by Redshift. Should we still opt for dimensional modeling ?
If so, how do we get around the limitations and maintain data integrity of our datawarehouse.
Yes, dimensional modelling is feasible and strongly encouraged on Redshift. Redshift is even optimized for star schema queries
Optimizing for Star Schemas and Interleaved Sorting on Amazon Redshift
Refer this Is dimensional modeling feasible in Amazon RedShift?
I have an SQL request:
SELECT table1.nr1 / NULLIF(table2.nr2, 0) as percentage
and I want to write this in relational algebra.
Is it possible to represent arithmetic division in relational algebra?
According to this course of the University of Rochester relational algebra can be defined as
a formal system for manipulating relations
Operands of this algebra are relations.
Operations of this algebra include the usual set operations (since relations are sets of tuples), and special operations defined for relations
selection
projection
join
It's an algebra on relations and there is no representation of numbers. If you want to use arithmetic on numbers you have to use an extended formalism such as Safe Database Queries with Arithmetic Relations.
Topic modeling identifies distribution of topics in a document collection, which effectively identifies the clusters in the collection. So is it right to say that topic modeling is a technique to do document clustering?
A topic is quite different from a cluster of docs, after all, a topic is not composed of docs.
However, these two techniques are indeed related. I believe Topic Modeling is a viable way of deciding how similar documents are, hence a viable way for document clustering.
In representing each document as a topic distribution (actually a vector), topic modeling techniques reduce the feature dimensionality from number of distinct words appeared (in a corpus) to the number of topics. Similarity between docs' Topic distributions can be calculated using Cosine metrics and many other metrics, which reflect the similarity of the docs themselves in terms of the topics/themes they cover. Based on this quantified similarity measure, many clustering algorithms can be applied to group the documents.
And in this sense, I think it is right to say that topic modeling is a technique to do document clustering.
The relation between clustering and classification is very similar to the relation between topic modeling and multi-label classification.
In single-label multi-class classification we assign just one label per each document. And in clustering we put each document in just one group. The fact is that we can't define the clusters in advance as we define labels. If we ignore this fact, grouping and labeling are essentially the same thing.
However, in real world problems flat classification is not sufficient. Often documents are related to multiple categories/classes. Thus we leverage the multi-label classification. Now, we can see the topic modeling as the unsupervised version of multi-label classification as we can put each document under multiple groups/topics. Here again, I'm ignoring the fact that we can't decide what topics to use as labels in advance.
I have this dependency diagram I'm trying to make into 2NF and then 3NF. I'm not sure if I'm doing it right:
Here's the dependency:
This is how I tried to model it:
If I understand the diagram correctly, your "1b" is the right decomposition. All the tables in "1b" are in at least 5NF, though.
The notion that you can normalize to 2NF and no higher, or to 3NF and no higher, is a common misunderstanding of how normalization works. It's quite common to start with a relation that's in 1NF, and in a single step end up with all the relations in 5NF.