Sunday, October 25, 2015

Weekly Update

1. Quote of the Week
The ER notation consists a set of constructs, such as, a rectangle to represent an entity types, an ellipse to represent an attribute a diamond to represent a relationship type, and so on. The RDS is a set of linear relation schemas. A relation schema has a name and is a sequence of attributes of text separated by comas and arranged horizontal. I have also developed an ER-to relational transformation algorithm to transform an ER schema to its corresponding RDS. I wish to implement this project as a CASE tool.
The problem is not the student, but the quality of education he gets at his university.

Wednesday, October 21, 2015

Mathematics & Meaning

My October column @AllAnalytics.

We have seen that the usefulness of the relational data model (RDM) is in its dual theoretical foundation -- first order predicate logic (FOPL) and set theory -- the mathematics guarantees provable logical correctness of query results regardless what meaning is assigned to the database R-tables. But a logically correct result is not necessarily a meaningful result. As a reader commented "If a is Salary and b is Start date, the [Cartesian] product a*b still doesn't have a sensible meaning." It is critical for the analyst to understand the distinction.

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

Sunday, October 11, 2015

Weekly Update (UPDATED)

Housekeeping: Added following to LINKS page:

1. Quote of the Week
"The real definition of Big Data?? Simple: Whatever does not fit in Excel!"

Me: What is--precisely, pls!--the threshold from small to big data? And how do the structural, manipulative and integrity aspects change over the threshold?

HM: Big data := the smallest set of data for which the sensitivity of your transfer function is minimal, and such that the cardinality of this set is too large to implement said transfer function on a single physical machine.Transfer function := though not strictly a function, as in the Lambda calculus, it is more of an operator which ingests data of any size and produces a monetizeable product or service. Sensitivity := the degree to which a perturbation on the input into a transfer function affects the result produced by said function.

Me: Ugh! I'm sure that's exactly what all the overnight data scientists, including those who invented Big Data, had in

Sunday, October 4, 2015

Database Education: Oughts and OughtNot's

From an online exchange in response to A Tiny Intro to Database Systems:
C: As a non-CS grad coming fresh to databases, I found both the entity-relationship, and the object-oriented models confusing. Then I read Date [1] and Codd's [2] books and papers on the relational model, the one from the 1970s that is basically set and type theory applied to data, and found that to be a lot clearer and a more powerful abstraction to deal with your data model. As a non-"full time developer" it amazes me the number of "experienced" developers who are not aware of the relational model and who do not know what a foreign key is, or why referential integrity might be important.

For example, your Relational Model introduction has a discussion of various data types. But arguably, whether your integer is implemented as BIGINT or TINYINT is an implementation decision which should be separate from the model discussion (dixit Date). In other words, that attribute has a type of integer and how that integer is stored is a separate issue, and your RDBMS ought to abstract it away (as, I think, Postgres is pretty good with, and MySQL quite annoying). The beauty of the latest RDBMS developments, particularly in PostgreSQL world, is that the implementation has gotten so good that you don't need to really worry about it like you used to just a decade ago, at least in 95% of use cases.

I think one can teach SQL (and the relational model) to a non-developer in about 2 hours, because it is so declarative and intuitive. One day I'll go write that tutorial, as many clients need it sorely.

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