Friday, August 26, 2016

Data, Information, Knowledge Discovery, and Knowledge Representation

My August post @All Analytics

As any true scientist should know, science has a context of discovery and a context of validation (I wonder how many of those who call themselves “data scientists” know the concepts). In the former, experiments are usually done to discover relationships -- a theory (to be validated in the latter, in which further implications thereof can also be derived). So there is no theory/conceptual model/knowledge yet. Data or information can be represented by any database (e.g. a SQL database can represent variables and/or runs as columns and/or rows in tables), docubase and even file -- none is superior or inferior for analytical purposes.

Read it all. (Please comment there, not here)

Monday, August 22, 2016

This Week

1. What's wrong with this picture?
"Can you have 2 tables
with identical structure in a good DB schema (item_id, user_id, time). Some of the records will be identical but their meaning will be different depending on which table they are in. The "views" table is updated any time a user views an item for the first time. The "downloads" table is updated any time a user downloads an item for the first time. Both of the tables can exist without the other.

"I don't think that there is a problem, per se from a E/R modelling point of view, as long as they represent two semantically different entities."
"Are you saying that both tables have an 'item_id' Primary Key? In this case, the fields have the same name, but do not have the same meaning. One is a 'view_id', and the other one is a 'download_id'. You should rename your fields consequently to avoid this kind of misunderstanding."
 "Chris Date and Dave McGoveran formalised the Principle of Orthogonal Design. Roughly speaking it means that in database design you should avoid the possibility of allowing the same tuple in two different relvars. The aim being to avoid certain types of redundancy and ambiguity that could result."

"When designing a DB there are lots of different parameters, and some (e.g.: performance) may take precedence. Case in point: even if the structures (and I suppose indexing) are identical, maybe "views" has more records and will be accessed more often. This alone could be a good reason not to burden it with records from the downloads."
--Can you have 2 tables with identical structure in a good DB schema?,

Monday, August 8, 2016

This Week

1. What's wrong with this picture?
Q: "My understanding has always been that a primary key should be immutable, and my searching since reading this answer has only provided answers which reflect the same as a best practice. Under what circumstances would a primary key value need to be altered after the record is created?"

A: "When a primary key is chosen that is not immutable?"

2. Quote of the Week

"We start with a value. This may be a number, a date, or a chunk of text. Domain refers to the meaning for a value or a set of possible values. When we have a value with a consistently and widely used set of units, value domains, or applications, we call this value a data element. A data element may be a ticket number, a temperature reading, a hair color. (Some modeling approaches omit the notion of data elements or domains.)
In all the techniques and stages of data modeling, the concepts of entity and attribute are universal. An entity is the “thing” that must be manipulated as a data object. Entity represents an indivisible concept that consists of data elements. Each data element in the entity is called an attribute. Conversely, we could say that we build an entity by attributing data elements to it. You create an entity as a whole, and you delete it as a whole." --What a Concept! Is Logical Data Modeling Obsolete? --
Note: In the preface to my PRACTICAL DATABASE FOUNDATIONS series of papers I deplored an author's (considered an expert) reliance on an industry ANSI committee as a starting point in an explanation of conceptual, logical and physical data fundamentals--there is practically 0 chance of soundness and 100% chance of confusion. So when this article started with "In the common usage established by ANSI in 1975, data modeling goes from abstract to concrete in three steps" I knew I would not read very far. Sure enough, I stopped almost immediately, after the above quote. If you don't understand why, I recommend you check out my papers.