• Aucun résultat trouvé

Presentation

N/A
N/A
Protected

Academic year: 2022

Partager "Presentation"

Copied!
23
0
0

Texte intégral

(1)

Conceptual Modeling for XML - A Survey

Martin Necasky MFF UK

april, 2006

(2)

Synopsis

introduction

requirements for conceptual models for XML

existing approaches

(3)

Why Conceptual Model for XML?

recently, XML is frequently applied as a logical database model

we have several XML schema languages

however, these languages are unsuitable to use on the conceptual level:

too weak (DTD) or too complex syntax for designers (XML Schema)

(4)

Special Features of XML

hierarchical structure

irregular structure

ordering

mixed content

(5)

Requirements for conceptual

models for XML

(6)

General Requirements (1)

independence on XML schema languages

formal foundations

user-friendly graphical notation

logical level mapping

(7)

General Requirements (2)

support for hierarchical views

different users with different requirements accessing the modeled data

each of the users may require different hierarchical organization of the same data – hierarchical views

we need to model the hierarchical views on the conceptual level

(8)

General Requirements (3)

integration with semantic web technologies

a translation from the conceptual level to the

semantic web level where the structures from the conceptual level are described using OWL

automatic publication of internally represented data on the semantic web

automatic integration of data from the semantic web to the internal representation

(9)

Modeling Constructs Requirements

classical features:

many-to-many rel. types

n-ary rel. types, attributes of rel. types

special features:

hierarchical structure

ordering, mixed content

irregular structure

(10)

Approaches

(11)

Two main approaches (1)

extension of the classical E-R model to be suitable for the modeling of special XML features

problem with the modeling of hierarchical structures and different hierarchical views of the same data

how to model ordering?

how to model data mixed with text values?

(12)

Two main approaches (2)

hierarchical approach

schemes are modeled as trees

allows to model hierarchical structures and different hierarchical views

problem with the modeling of many-to-many

relationship types, n-ary relationship types, and attributes of relationship types

(13)

EReX -

An E-R based conceptual model

for XML

(14)

EReX (1)

allows to model irregular structure and ordering:

categorization of entity types

similar to IS-A hierarchies in E-R

total/exclusive coverage constraints

E1 + E2 + E3 = E

E1 | E2 | E3

(15)

EReX (2)

ordering specified on an entity type E participating in a relationship type R

for each entity e from E the set of relationships from R having e as a participant is linear ordered

(16)

EReX (3)

Student + Professor = Person Student | Professor

(17)

ORA-SS -

A hierarchical conceptual model for

XML

(18)

ORA-SS (1)

n-ary relationship types and attributes of relationship types

cardinality constraints for both participants of relationship types

ordering

disjunction

(19)

ORA-SS (2)

(20)

ORA-SS (3)

(21)

ORA-SS (4)

(22)

Conclusion

poor support of the specific XML features

utilization of conceptual models for the data integration and for the integration with the

semantic web has not been studied enough yet

there is no conceptual model combining the

advantages of the both approaches (E-R and

hierarchical)

(23)

Thank you for your attention!

Références

Documents relatifs

In the following, we consider the so-called generalized inverse Gaussian (GIG) distribution, denoted by GIG(ρ, φ, ω) with parameters ρ, φ and ω defined in [35] as hierarchical prior

In particular, examination of the celebrated chromatin remodeling system of the mouse mammary tumor virus, in which the binding of transcription factors opens the way to other

We presented a new method to fit the signed distance function of a given boundary curve of surface by using a simple but efficient PHT-spline (bi- and tricubic piece- wise

Proposition 5 (i) Soit P = MU un sous-groupe parabolique semi-standard de G et O l’orbite inertielle d’une repr´esentation lisse unitaire irr´eductible et cuspidale de M.. On

We introduced an artefact helping the user to momentarily lessen the visual impact of lower level subsidiaries, in order to see higher level strategies form be- fore digging down

Glaciers as top attraction of the Alps and in the 21st century disappearing from sight The second half of the 19th and the early 20th centuries were characterized by a growing

We compare the use of the classic estimator for the sample mean and SCM to the FP estimator for the clustering of the Indian Pines scene using the Hotelling’s T 2 statistic (4) and

from the main server pages and to luster only the researh teams topis for. theme 3