Conceptual vs. logical modeling, part II

Integrity

In the previous post, a somewhat detailed inspection was made of possible approaches for specifying, in a modeling language, some database structure, highlighting differences in informational value between those modeling languages, as well as pointing out some pieces of relevant information the specificiation of which is typically left completely unsupported by any of them.

But a full formal spec of a database is not only about its structure, it is also about any additional rules that apply to the constituent components of that structure.  This observation holds, regardless of whether the database is a relational one (and its constituent components are what TTM calls "relation variables", "tables" in SQL) or a graph-based or hierarchical one (and its constituent components are nodes and edges).  I'll be speaking of relvars (TTM abbreviation for relation variables) in what follows, but keep in mind that the same should apply as well, mutatis mutandis, to hierarchical and "graph-ical" models.

While the aspect of 'structure' can reasonably well be modeled in "graphical" languages (such as the various ER dialects and UML), that is much less the case with the aspect of the integrity constraints between the components of that structure.  How come ?

The essential reason is that the nature of an integrity rule/constraint can really be just anything at all.  Its "structure" is constrained only by the fact that it must be expressed exclusively in terms of the relvars that make up the database structure.  At the logical level, where all the formal details of the relvars have been fully specced out in some given language, this is achieved "easily" enough using some language based on/inspired by mathematics.  Just spell out the predicate that makes a violation a violation.  But how to devise a language that supports expressing "anything at all" at the conceptual level ?  The answer is you can't.  The only thing you can do is try to taxonomize the set of all possible constraints in certain well-defined "classes" that might indeed be epxressible.  That is (sort of) exactly what has happened in database modeling land (*).  From ER modeling over IDEFIX to Halpin ORM : the set of all possible constraints is subsetted according to certain chosen criteria, and for each "identifiable" subset, a notation is devised to facilitate documenting constraints belonging to that subset.  A modeling language such as ER leaves it at that, Halpin's "Big Brown Book" explicitly adds a (fourteenth, I believe) category "others", the leftovers that still aren't expressible using the modeling language's symbols that are available.

Anyway.  The fact alone that a powerful modeling approach such as ORM still has this category "leftovers" in the constraints realm, should already suffice to show that _complete_ and fully formal specifications are in fact inachievable without a mathematical language.  For the typical categories of constraints other than those "leftovers", however, the belief seems to be fairly widespread, and firm, that current mainstream notations suffice to document all the stuff we need to know/convey about our databases.  That belief is not entirely warranted, imo, and in this post I'll be illustrating a couple of issues relating to the (common) class of uniqueness constraints.  A subsequent post will do the same for referential integrity/foreign key constraints.

(*) the sad byproduct of this state of affairs is that if one uses the word "constraint" in a database discussion, SQL practitioners will often think you must be talking either of a UNIQUE constraint or a foreign key, overlooking the fact that "there are more types of constraint between heaven and earth than are expressible in ER, and supported by SQL".



Uniqueness

The example case from the previous post is not very well suited to illustrate issues with documenting uniqueness rules.  None of the entities in that example are directly suitable for illustrating the points I want to make, so for the sake of this discussion, I'm somewhat forced to resort to a totally different example, which is likely to look ridiculous in the eyes of business modelers, but I won't mind that for the time being.

Let's say we want to model the operation of numeric addition - if you really can't bear the thought, imagine you are Euclid or Pythagoras, that arithmetic has not been invented yet and you are in the process of doing exactly that, using the latest database design technology (and with apologies upfront for my ascii modeling) :

+---------------+
! ADDITION      !
+---------------+
! N1     number !
! N2     number !
! SUM    number !
+---------------+

(Yes, and the table is indeed like

+----+----+-----+
! N1 ! N2 ! SUM !
+----+----+-----+
!  1 !  1 !   2 !
!  1 !  2 !   3 !
!  ...          !
!  2 !  1 !   3 !
!  2 !  2 !   4 !
!  ...          !
+---------------+

!!!!!!!!)



I'm pretty sure if I'd ask you what the key is here, you'd reply with "N1 and N2 combined, of course".  You get 33% from me for that answer.  There are three keys here : {N1 N2}, {N1 SUM}, and {N2 SUM}.  Well, granted of course that the matter is also important of which ones you ACTUALLY WANT ENFORCED (that's what you're referring to if you wanted to argue that these latter two "do not identify an addition").  If we wanted to "enforce" the obvious consistencies/equivalences between expressions of addition and expressions of subtraction, we would indeed need to model and enforce all three (hehe.  settled that.).

Now how would you document all three of them in your model of "annotated rectangles" ?  You're in trouble !  (In fact, I think it is precisely _because_ of this notational problem to document multiple composite keys inside one single rectangle, that the notion of "primary" key (as distinct from "secondary / ternary / auxiliary / ..." key ????) has become so widespread as it has, and furthermore that the practice of ID-ifying really just everything has become as popular and widespread as it has.  I leave it for you to ponder whether that's a case of putting the cart before the horse or not - or one of redefining/retrofitting the problem such as to suit the most desirable solution.)



Anyway.  Supposing we do want to enforce all three keys.  How can we document this in our drawing ?  Observe in particular that each key consists of >1 attribute and each attribute effectively participates in >1 key.  The only way I can imagine to convey all the key information in our rectangle is like this :

+---------------+
! ADDITION      !
+---------------+-------+
! N1     number ! K1,K2 !
! N2     number ! K1,K3 !
! SUM    number ! K2,K3 !
+---------------+-------+

Very much like the approach of putting a P in front of the attributes, but it takes attentive and careful deciphering to read the drawing and capture the keys information correctly !  (And the sad byproduct of omitting the full keys information, e.g. for readability sake, in diagrams such as these, is indeed that typically not all keys are properly identified, let alone enforced.)

In fact, the most readable way to convey all of this information about uniqueness rules, seems to be exactly by just using syntax very similar to declarative DDL, or the declarative portion of a D language :

UNIQUE {N1,N2} , UNIQUE {N1,SUM} , UNIQUE {N2,SUM} or
UNIQUE { {N1,N2} {N1,SUM} {N2,SUM} }

And here again we are seemingly headed toward a similar conclusion : if you want to be precise _AND_ complete in what you are stating about the nature of the database that you are documenting, then of necessity you MUST resort to a language that has a much higher expressiveness than the ones you typically have available when modeling at a "higher" level of abstraction.

Incidentally, in notations such as Data Vault (a "hub" to represent the entity and separate rectangles connected to the "hub" for each attribute), the problem of documenting keys is even worse.  The only graphical way(s) I can imagine to document the existence of some meaningful "grouping" of attributes, such as their belonging to the same key, will invariably make the diagram equally unreadable because of extraneous line bloat.  Whether you try to do it by surrounding them with dotted lines or so, or by creating a new symbol for documenting the existence of a key (three extra symbols for the ADDITION Data vault) and connecting each attribute with them as appropriate (six extra connections on the diagram), it's always going to turn your beautiful neatly organized DV diagram into more of a spider web.  Fortunately, Data Vault diagrams are typically used only in DW contexts, not to document keys in the operational source systems they're concerned with, but it still goes to show that whatever conceptual notation you use, it only goes as far as it goes and _something_ will always be "missing" from it.



Uniqueness bis

Managing temporal data is somewhat of a long-standing problem in database land.  A thorough analysis of the _nature_ of the problem (and what is needed to address it) can be found in "Temporal Data & the Relational Model", pages 1-857, so I'm not going to re-iterate all of that here, but one particular problem dealt with is "temporal uniqueness".  (Aside : if you haven't yet read the book but are interested to do so, don't go order or search it now.  Updated and revised edition is to appear within a couple of months.)

Say you have

+-----------------+
! ASSET_VALUATION !
+-----------------+
! ASSET_ID     ID !
! FROM       date !
! TO         date !
! VALUE    number !
+-----------------+

or

+-----------------+
! MARRIED_TO      !
+-----------------+
! PERSON1_ID   ID !
! PERSON2_ID   ID !
! FROM       date !
! TO         date !
+-----------------+

and you want to enforce a constraint "no single date giving >1 distinct values for same ASSET_ID", or "no one married to >1 other person on same date".

The "traditional" interpretation of what a key is, will not allow you to express this.  No "traditional", equality-based, key will ever prevent overlaps between various FROM-TO combinations for the same person/asset/...  So perhaps you might be inclined to conclude "that is not a real key".  Interestingly, The "Temporal Data" book proposes to rearrange matters a bit so that expressing the constraint does become possible, and indeed in the form of "specifying a key" at that :

+-------------------+
! ASSET_VALUATION   !
+-------------------+
! ASSET_ID       ID !
! DURING date_range !
! VALUE      number !
+-------------------+

or

+-------------------+
! MARRIED_TO        !
+-------------------+
! PERSON1_ID     ID !
! PERSON2_ID     ID !
! DURING date_range !
+-------------------+

... WHEN UNPACKED ON (DURING) THEN KEY {ASSET_ID DURING} ...
... WHEN UNPACKED ON (DURING) THEN KEY {PERSON1_ID DURING} KEY {PERSON2_ID DURING} ...


The graphical languages such as ER that we typically use for information modeling, still let us down, somewhat, in the case we'd want to specify that level of detail.  In addition to the composite nature of the key, we'd also need to express the semantics of the "WHEN UNPACKED ON" part : namely that the values for the DURING attribute must be interpreted in a "for each individual date value covered by the range" kind of way.  The closest we could come to denoting that might be something like this :

+-------------------+
! MARRIED_TO        !
+-------------------+-----------+
! PERSON1_ID     ID ! K1        !
! PERSON2_ID     ID ! K2        !
! DURING date_range ! K1_T,K2_T !
+-------------------+-----------+

Of course, the notational problem with denoting multiple keys to the relvar has not disappeared, nor has the possible participation of a single attribute in >1 key, just an extra little bit of codification has been added (suffixing _T to a key name on the lines for the attributes where it applies) to denote the extra bit of semantics covered.  It will be clear that while such tricks are indeed possible, and potentially helpful in denoting modeled solutions to a problem that is indeed very common, once again such solutions can only go as far as they do, and taking things further will ultimately result only in making the models we draw ultimately unreadable.  Specifically in the context of temporal data management and temporal keys, observe for example that it is not necessarily the case that all range-valued attributes will _always_ have the _T "interpretation" for _all_ the keys in which they participate :

+----------------------------------+
! NOT_BEST_OF_EXAMPLES_BUT_ANYWAY  !
+----------------------------------+------+
! PRESIDENTIAL_TERM     year_range ! K1   !
! DURING                date_range ! K1_T !
! PRESIDENT_NAME               ... !      !
+----------------------------------+------+

( think   70-74 : 70-73 : NIXON   &&   70-74 : 74-74 : FORD )

Once again the conclusion seems warranted that extending expressiveness/notational support beyond current common practices, will quickly result in making the models more unreadable and thus less informative, rather than more informative.



Uniqueness ter

Another variation on the theme of uniqueness rules, is the problem of enforcing uniqueness on only a (proper) subset of all occurrences of an entity type.  Say you have

+----------------------------------+
! CAR_LICENSE_PLATE                !
+----------------------------------+------+
! CAR_CHASSIS_ID               ... ! K1   !
! TAX_LICENSE_PLATE            ... ! K2   !
! CAR_STILL_IN_ACTIVE_USE     bool !      !
+----------------------------------+------+

and for the purpose of re-using license plate numbers, you want to enforce license plate uniqueness (key K2) only for those cars that are still in active use (there are admittedly better solutions to this problem than the one modeled here, which I sometimes call the ultra-poor man's historical database, but it does serve to illustrate my point).

The aspect of the problem that makes the "key" "not documentable", is precisely the subsetting rule, i.e. the fact that the "key" is not to be enforced on the whole CAR_LICENSE_PLATE entity, but just on the subset of it that, once the database is implemented in SQL, could be found by issuing SELECT ... FROM CAR_LICENSE_PLATE WHERE CAR_STILL_IN_ACTIVE_USE == true;

If we absolutely wanted to be able to document the existence of this key, using the available means of adding a "Kn" annotation in the rectangles, we'd have to add a separate rectangle for the subsetted CAR_LICENSE_PLATE entity, and then we'd have shifted the problem to documenting the definitional connect/dependency of this new rectangle with/on the "original" one, the "full" entity.  That is, we've transformed the problem into one of conceptually documenting "view definitions" (and that very idea is probably seriously questionable in itself already because including the "definitional connect" smacks quite a bit of conflating conceptual/logical).  Once again, our modeling language will let us go only as far as it goes.



(still to be continued)

Conceptual vs. Logical modeling (once again)

http://www.databaseanswers.org/data_models/assets/index.htm

was presented to me as an example case in a discussion on [data] modeling at

https://www.linkedin.com/groups/data-model-SAP-Bill-Material-2357895.S.270612382

(group membership is required to view) and in particular as a case for fleshing out the distinctions between what I call 'conceptual' and 'logical' modeling.  That distinction being that "conceptual is informal, logical is formal", or "conceptual is typically incomplete, logical is always complete".  "Complete" in the sense of "full disclosure of all relevant information".  This post intends to clarify further what I mean by that, exactly.

One model being incomplete and another one being complete means there are differences in "informational value".  What & where are those differences ?  The discussion will be split covering two distinct aspects : one of structure and one of integrity (and the integrity part will be kept for a later post).

Structure.

Take a look at a single rectangle in the example model, say "Assets".  Disregard the mentions of PK/FK for the time being, they're related to integrity, not to structure per se.

What information is present in the rectangle ?

• a rectangle heading telling us that the rest of the rectangle informs us further of the nature of a concept called "Assets".

• a rectangle body telling us that the concept "Assets" is defined to have properties named "Asset_ID", "Asset_Name", ...

What information is _not_ present in the rectangle ?

• Most notably, the type information for each property.  Now, I've seen many similar models that _did_ include this information.  Usually limited to the set of type names that were known to be supported by the DBMS that the system was known to be going to be implemented on (so far for DBMS agnostic modeling !).  Sometimes the set of type names used would include things such as "COORDINATE".  Indicating that some domain/type of that name is supposed to exist, and supposed to be known and understood correctly by the reader.  And it's the double "supposed" in there that makes such models informal/incomplete still.

• A very nasty one, and very hideous : optionality !!!  Take a look at the Date_Disposed_of property.  Is that property going to have an "assigned value" for each and every occurrence/instance of an "Assets" type ???  Presumably not.  While it is not invalid per se to introduce some kind of concept of "nullability" at the conceptual level (of entity attributes), the thing is : the logical level's "full disclosure" requirement implies that the diagrams must then show that information !!!  (I've seen at least one dialect that used '#'/'*' symbols on the left side in the rectangles to achieve this.)  And the second thing is : diagramming languages such as E/R and UML already have a notion of optionality (albeit not for attributes but between entities).  And of necessity, it will introduce multiple ways of saying the same thing.  Singling out the 'disposed_of' attribute in its own "Assets_Disposed" entity (with the obvious one-to-at-most-one relationship) will do the job, but is often considered "poor practice" because of the "entity bloat" it creates (and the corresponding reduction of opportunity to "inspect just once and get the whole picture").  Otoh, it is precisely what would _need_ to be done to achieve a relational version of the logical information model, since the relational model does not allow [values for] attributes to be missing.

• Also not incorporated in the model shown by this example, but the notion does exist : the distinction between "weak" E/R entities and "strong" E/R entities.  There are modeling dialects that would _NOT_ include the "Asset_ID" attribute in the "Asset_Valuations" entity, and the reader is supposed to infer, somehow, that "Asset_Valuation" is indeed a "weak" entity and additionally requires its "parent entity"'s primary key before "an occurrence of it can come into existence".  This particular approach induces interpretation ambiguities in two cases : (a) the parent entity has >1 identifying key (solvable only by introducing yet other artefacts such as distinctions between "primary" and "secondary" keys), and (b) the child entity has two relationships (think bill-of-material structures) to the same parent entity (there will have to be two distinctly named attributes, so you can't assume "same name as the primary key attributes in the parent", but the modeling approach of leaving them unmentioned means you can't specify which will be which ...  This actually belongs more in the discussion on constraint specification, so I'll pick the subject up there again if I don't forget it.

• Also quite hideous as well as nasty : the meaning of the damn thing !!!  Will drawing a rectangle and labeling it "Assets" ensure that the understanding derived from it by some individual, will be _exactly_ the same as that derived by some other individual ?  _sufficiently_ the same ?  And even if so, how will you know any differences in understanding ?  Posing these questions is answering them.  Looking at the drawing, all readers will just be nodding yes because with sufficient details abstracted away, your beautiful drawing simply matches their perception of reality.  I used to have a catchphrase "After you've abstracted away all the differences, whatever remains is identical."

Conclusion : while it is OK for conceptual models to leave out information such as the things mentioned (note that I do not claim completeness for the list of things I mentioned), a fully formal logical model will always have to include _all_ the pieces of the "puzzle" :

• All the type information.  To begin, that's a complete and fully specced inventory of all the data types that will be used in the rest of the model.  And "fully specced" really means "fully specced" here, e.g., just saying "INTEGER" will _not_ be sufficient if there is any risk that any reader might be misinterpreting the range of numbers "covered" by this name.  Sometimes it _is_ interesting for a user to know that 100000 is not an INTEGER (because >32767), or to know that -1 is not an INTEGER (because only positive numbers were anticipated).  For a central bank deciding to introduce negative interest rates, it might be interesting to know that some of their IT systems had not anticipated this and defined the domain for interest rates something like the range from 0.0000 to 99.9999 ...  And for types such as "coordinate", there is nothing in the name to suggest whether these are 2D or 3D coordinates ( (x,y) pairs vs (x,y,z) triples ).  Formal completeness requires one to state something like :
  
  COORDINATE : {(x,y,z) | x IN FLOAT && y IN FLOAT && z IN FLOAT}
  
  This definition itself depends on a definition for a thing called FLOAT.  This one in turn could be defined as
  
  FLOAT : { (m,e) | m IN NN && e in NN && e>=-128 && e<=127 && m>=... && m<=...}
  
  Now we depend on a definition for NN.  It will be clear that somewhere somehow, something inevitably has "got to be given".  Fortunately, that something can be as simple as "the set of Natural Numbers" that everyone should know from 2nd grade math classes, or thereabouts.  And if misunderstandings and/or communication problems boil down to a lack of agreement/common understanding of what the set of natural numbers is, well then there will be very little any modeling language/methodology could possibly to address that.

• Assuming we are defining a fully formal logical structure according to the relational model (as distinct from "the graph-based model", which may be conceivable/imaginable, but has unfortunately never been elaborated/formally spelled out the same way the RM has been), _all_ the attributes of the relational structures, plus the type they're of (those types having been formally defined in the previous step).
  
  concrete example :
  
  ASSETS : { (Asset_ID, Asset_Category_Code, Asset_Type_Code, Asset_Name, Asset_Description, Date_Acquired, Date_Disposed_of, Other_Details) |

            Asset_ID in ... &&
            Asset_Category_Code IN ... &&
            Asset_Type_Code IN ... &&
            Asset_Name IN ... &&
            Asset_Description IN ... &&
            Date_Acquired IN GREG_CAL_DATE &&
            Date_Disposed_of IN GREG_CAL_DATE &&
            Other_Details IN ... }

• Still assuming we are defining a fully formal logical structure according to the relational model (such that attributes cannot ever be null), the relational structures will be split out in separate parts whenever some attributes of a conceptual entity are optional.
  
  concrete example :
  
  ASSETS : { (Asset_ID, Asset_Category_Code, Asset_Type_Code, Asset_Name,
                      Asset_Description, Date_Acquired, Date_Disposed_of, Other_Details) |
              Asset_ID in ... &&
              Asset_Category_Code IN ... &&
              Asset_Type_Code IN ... &&
              Asset_Name IN ... &&
              Asset_Description IN ... &&
              Date_Acquired IN GREG_CAL_DATE &&
              Other_Details IN ... }
  ASSETS_DISPOSED : { (Asset_ID, Date_Disposed_of) |
               Asset_ID in ... &&
               Date_Disposed_of IN GREG_CAL_DATE }

• And it will also have to include a precise statement of the so-called "external predicate" for each relational structure so defined.  Don't underestimate the importance of this.  An SQL table has a very precise intended meaning, and too often I've seen maintenance developers think "oh I can reuse this table for my current purpose", looking exclusively at its structure and ignoring/denying/disregarding the precise, current intended meaning completely.  It is in fact because of this associated intended meaning that "re-using existing database tables" is, in principle, the WORST POSSIBLE IDEA a db designer can have.  Except if he is 100% certain that the current external predicate matches _exactly_ with the "external predicate" he has to "introduce" into the database for achieving "his current purpose".  This is most unlikely to be the case, except if the table is an EAV table, and I've already dealt with why that approach sucks (in 99.9999% of the cases).
  
  (Incidentally, if you were struck by a remarkable resemblance between the way the data type definitions were stated in the foregoing point, and the way the relational structures were stated in the last, it is exactly this aspect of their being related or not to such an "external predicate" that makes the difference.  Data type definitions are just that, formal ways to define _values_ that are usable in the _relational structures that will make up the database_ to represent _meaning_.  Values in data types do not carry meaning, the relational structures that make up the database do.  E.g. "The asset identified by §Asset_ID§ has been disposed of on §Date_Disposed_of§".  Note the placeholders in between §§ marks, and that the placeholders correspond 1-1 with the attribute names.  Such a phrase could be the "external predicate" for the ASSETS_DISPOSED relational structure, and since it defines the logical meaning of the content that will be held in the concerned relational structure, it should always be an integral part of the logical model defining the database.)

Next question : and what kind of modeling language supports all of this ?  There are several.  Or none at all.  Depending on what you're willing to call a "modeling language".

To begin with a language of pure maths.  One such as used in "Applied Mathematics for Database Professionals".  Very commendable reading, BTW, even if I have to add the footnote that the book doesn't really bother with formal type definitions, contenting itself to rely on a type system such as that offered by Oracle (this had mainly to do with the 9 different DBMS's the authors had been using the most throughout their careers : Oracle 4, Oracle 5, Oracle 6, Oracle 7, Oracle 8, Oracle 9, Oracle 10, Oracle 11 & Oracle 12 - end of jocular sidenote).

Anyways.  Math-like language offers everything we need to express precise type definitions and precise definitions of relational structures such as :

FLOAT : { (m,e) | m IN NN && e in NN && e&gt;=-128 && e&lt;=127 && m&gt;=... && m&lt;=...}
COORDINATE : {(x,y,z) | x IN FLOAT && y IN FLOAT && z IN FLOAT}

ASSETS_DISPOSED : { (Asset_ID, Date_Disposed_of) | ... }

But we're left in a bit of trouble when wanting to express external predicates for our relational constructs in such a language.  Of necessity, of course, the thing is termed "external" for good reason (external = external to the system of mathematical computation that is the DBMS, hence it's a bit contradictive to expect them to be expressible in math language) !



And those not well versed in using math formulae, will of course quibble that they don't see themselves manipulating models expressed in such a language, and that they want an alternative.  Such an alternative exists in the form of a subset of the statements of a programming language such as Tutorial D, in particular, the set of statements in that language that most developers will be inclined to label "declarative" statements (following examples are only loosely inspired by, and not 100% valid, Tutorial D) :

TYPE FLOAT (M INT, E INT) CONSTRAINT E>=-128 && E<=127 && M>=... && M<=... ;
TYPE GREG_CAL_DATE (D INT, M INT, Y INT) CONSTRAINT ........................................... ;

VAR ASSETS_DISPOSED RELATION {Asset_ID ... , Date_Disposed_of GREG_CAL_DATE } ;

Documenting the external predicate for all the VARs (= the relational structures to make up the database, "tables" in SQL) is a matter of adding comments, or (better), some kind of PREDICATE subclause in syntaxes such as the one used here as an example.

The nice thing about such an approach is that these kinds of formal spec are parseable.  In a language such as Tutorial D, it means the logical definition of the database structure could be made known to any program by a mere "import logical_model" directive.  In environments using other languages, it means that stuff such as Hibernate classes can be generated 100% automagically.

And just to show that the syntax (the "how") of the language is, actually, completely irrelevant, and the only thing that matters is the meaning of the information it conveys (the "what"), here's an example of how the same information could be expressed in a (hypothetical) language that is much more OO-like :

immutable class FLOAT {
  int m;
  int e;
  constraint e&gt;=-128 && e&lt;=127 && m&gt;=... && m&lt;=... ;
}
immutable class COORDINATE {
  FLOAT x;
  FLOAT y;
  FLOAT z;
}
database class ASSETS_DISPOSED {
  ... Asset_ID;
  GREG_CAL_DATE Date_Disposed_of;
  predicate "The asset identified by §Asset_ID§ has been disposed of on §Date_Disposed_of§";
}

(If you don't recognize this immediately as an OO-style syntax, imagine "public" in front of every text line in there.)  Some of the "weird-looking" stuff ("immutable class"/"database class"/"constraint"/"predicate") in this example is deliberately intended to illustrate the kind of things that currently existing OO languages are typically still lacking in order to make them more suitable for management of data that resides in a DB, or at least for cleaner and better integration between the programming side of things and the data side of things.



To conclude on the matter of "structure" :

Any language can exist to epxress an information model.  The degree in which such a language allows to express EVERY POSSIBLE RELEVANT ASPECT of [the content of] an information model, is what determines its degree of suitability for expressing information models that can legitimately be regarded as fully formal logical models.  That scale of suitability is a continuum, but no existing modeling languages/dialects actually achieve the full 100% of the expressiveness that is needed.



I'll be leaving anything to do with integrity for a next post (hopefully).