PATTERNS OF DATA MODELING- P36 ppt

Upon request, the service finds the translation given a source Phrase, target Language, and target AbbreviationType.. languageDiscrim languageID Language languageName AK1.1 languageDiscr

The translation service is dedicated software that runs apart from client applications.

The translation database stores corresponding Phrases for various Languages and Abbrevi-ationTypes (A person must populate the translation database.) Upon request, the service finds the translation given a source Phrase, target Language, and target AbbreviationType.

Figure 12.4 shows a sample application table that could be subject to the translation mechanism The phrase–to–phrase approach has a language bias For example, the source data may be stored in English and converted to another language only upon translation mapping Architecturally, a language bias is undesirable because users may detect the favored language

The pseudocode in Figure 12.5 illustrates the logic for finding a translation (The pseudocode is written using the UML’s Object Constraint Language [Warmer-1999].) The

basic logic is to first look for an exact match to the target language Otherwise, if a Dialect

is specified, look for the corresponding MajorLanguage If that fails, then make one more try to look for the AllLanguage record.

Figure 12.3 Phrase–to–phrase translation: IDEF1X model

languageDiscrim

languageID

Language

languageName (AK1.1) languageDiscrim

MajorLanguage

majorLanguageID (FK) allLanguageID (FK)

Dialect

dialectID (FK)

majorLanguageID (FK)

AllLanguage

allLanguageID (FK)

phraseID

Phrase

string

phraseEquivalenceID (FK) (AK1.1)

languageID (FK) (AK1.2)

abbreviationTypeID (FK) (AK1.3)

abbreviationTypeID

AbbreviationType

abbreviationTypeName (AK1.1) phraseEquivalenceID

PhraseEquivalence

Person

personalName birthdate birthPlace_English familyName

hairColor_English eyeColor_English height

weight

Figure 12.4 Phrase–to–phrase translation: Person model

12.4 Language–Neutral Translation

Figure 12.6 and Figure 12.7 show a model for a language–neutral translation service This ap-proach separates the multiple meaning of words and phrases for a clean translation However, you replace translatable strings with concepts IDs, limiting this approach to new applications

A Phrase is a string with a specific Language and AbbreviationType The Language for

a string can be a Dialect, a MajorLanguage, or AllLanguage A MajorLanguage is a natural language, such as French, English, and Japanese A Dialect is a variation of a MajorLan-guage, such as UK English, US English, and Australian English AllLanguage has a single

record for strings that do not vary across languages

Each Phrase has an AbbreviationType which is the maximum length for a string For

example, a name may be short (5 characters), medium (10 characters), long (20 characters), and extra long (80 characters) Abbreviations are especially handy for reports and forms

Phrase::getTargetPhrase (aTargetAbbrevType, aTargetLanguage) RETURNS aPhrase /* Start with all possible phrases for a source Phrase */

aSetOfPhrases := self.PhraseEquivalence.Phrase

/* Second, restrict phrases to matching the target abbreviation type */

aSetOfPhrases := aSetOfPhrases->SELECT (abbreviationType == aTargetAbbrevType) /* Third, look for an exact match to the target language */

card := Cardinality (aSetOfPhrases=>SELECT(language==aTargetLanguage));

IF card == 1 THEN RETURN aSetOfPhrases->SELECT(language == aTargetLanguage) ELSEIF card > 1 THEN RETURN "Error: Ambiguous"

ENDIF

/* Otherwise for a dialect look for a major language */

IF TypeOf(aTargetLanguage) == "Dialect" THEN RETURN

self.getTargetPhrase (aTargetAbbrevType, aTargetLanguage.MajorLanguage); ENDIF

/* Otherwise for a major language look for a default */

IF TypeOf(aTargetLanguage) == "MajorLanguage" THEN RETURN

self.getTargetPhrase (aTargetAbbrevType, aTargetLanguage.AllLanguage);

ENDIF

/* Else failure Could not find a translation */

RETURN "Error: No translation found"

END getTargetPhrase

Figure 12.5 Phrase–to–phrase translation: Pseudocode for finding a phrase

Figure 12.6 Language–neutral translation: UML model Consider for

new applications that require a robust translation approach

Language

name {unique}

* 1

* 1

Dialect MajorLanguage AllLanguage

Phrase

TranslationConcept

*

1

AbbreviationType

name {unique}

*

1

ConceptEquivalence

*

1 0 1

1 preferredConcept

{TranslationConcept + AbbreviationType + Language is unique.}

Figure 12.7 Language–neutral translation: IDEF1X model

languageDiscrim

languageID

Language

languageName (AK1.1) languageDiscrim

MajorLanguage

majorLanguageID (FK) allLanguageID (FK)

Dialect

dialectID (FK)

majorLanguageID (FK)

AllLanguage

allLanguageID (FK)

phraseID

Phrase

string

translationConceptID (FK) (AK1.1)

languageID (FK) (AK1.2)

abbreviationTypeID (FK) (AK1.3)

abbreviationTypeID

AbbreviationType

abbreviationTypeName (AK1.1) translationConceptID

TranslationConcept

conceptEquivalenceID (FK)

conceptEquivalenceID

ConceptEquivalence

preferredConceptID (FK) (AK1.1)

A TranslationConcept is the idea in a person’s mind that underlies a group of related

Phrases The premise of language–neutral translation is that an idea can be precisely ex-pressed in any Language Of course, this assumption is not exactly true as each language has

its nuances However, it is a good approximation for translating short phrases such as those that occur in user interface screens and reports The translation service is not intended for long passages such as those in documents and books

Table 12.2 shows a simple example A person has the concept “truck” in mind with a

translationConceptID of 2054.

• A MajorLanguage of English and long AbbreviationType yields a Phrase of “truck.”

• A MajorLanguage of French and long AbbreviationType yields a Phrase of “camion.”

• A MajorLanguage of English and short AbbreviationType yields a Phrase of “trk.”

• A Dialect of British English and long AbbreviationType yields a Phrase of “lorry.”

In practice, many persons could populate data and define redundant TranslationConcepts.

Multiple definitions are undesirable but difficult to avoid These multiple definitions ripple throughout application databases and are difficult to consolidate

ConceptEquivalence provides a cross reference for synonymous TranslationConcepts

and effects a logical merge (See Chapter 11.) The application tables store translationCon-ceptIDs ConceptEquivalence serves only as a cross-reference and is not referenced by ap-plication tables (See the Symmetric relationship antipattern in Chapter 8.) Each occurrence

of ConceptEquivalence has a preferred TranslationConcept.

The translation service is dedicated software that runs apart from client applications To

use the service, an application database substitutes a translationConceptID for each translat-able phrase For each TranslationConcept, the translation database stores the corresponding Phrases for the pertinent Languages and AbbreviationTypes (A person must populate the translation database.) Upon request, the service finds the Phrase for the specified Transla-tionConcept, Language, and AbbreviationType.

Figure 12.8 shows a sample application table that is subject to language–neutral trans-lation The use of concept IDs works well for a new application But it would be disruptive for an existing application to change strings to IDs

The pseudocode in Figure 12.9 illustrates the logic for finding a phrase, given a Trans-lationConcept, AbbreviationType, and Language The basic logic is to first look for an exact

translationConceptID Language AbbreviationType Phrase

Table 12.2 Language–Neutral Translation: Sample Phrases

match to the target language Otherwise, if a Dialect is specified, look for the corresponding MajorLanguage If that fails, then make one more try to look for the AllLanguage record.

Person

personalName birthdate birthPlace_conceptID familyName

hairColor_conceptID eyeColor_conceptID height

weight

Figure 12.8 Language–neutral translation: Person model

Figure 12.9 Language–neutral translation: Pseudocode for finding a phrase

TranslationConcept::getPhrase (aTargetAbbrevType, aTargetLanguage)

RETURNS aPhrase

/* Start with all possible phrases for a TranslationConcept */

aSetOfPhrases := self.ConceptEquivalence.preferredConcept.Phrase

/* Second, restrict phrases to matching the target abbreviation type */

aSetOfPhrases := aSetOfPhrases->SELECT (abbreviationType == aTargetAbbrevType) /* Third, look for an exact match to the target language */

card := Cardinality (aSetOfPhrases=>SELECT(language==aTargetLanguage));

IF card == 1 THEN RETURN aSetOfPhrases->SELECT(language == aTargetLanguage) ELSEIF card > 1 THEN RETURN "Error: Ambiguous"

ENDIF

/* Otherwise for a dialect look for a major language */

IF TypeOf(aTargetLanguage) == "Dialect" THEN| RETURN

self.getPhrase (aTargetAbbrevType, aTargetLanguage.MajorLanguage);

ENDIF

/* Otherwise for a major language look for a default */

IF TypeOf(aTargetLanguage) == "MajorLanguage" THEN RETURN

self.getPhrase (aTargetAbbrevType, aTargetLanguage.AllLanguage);

ENDIF

/* Else failure Could not find a translation */

RETURN "Error: No translation found"

END getPhrase