documents de recherche working papers – n° 01021

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documents de recherche working papers – n° 01021
CENTRE
DE RECHERCHE
RESEARCH CENTER
DOCUMENTS DE RECHERCHE
WORKING PAPERS
– N° 01021 –
From UML to ROLAP Multidimensional Databases Using a Pivot Model
Jacky AKOKA
CEDRIC –CNAM & INT
Isabelle COMYN-WATTIAU
Université de Cergy & ESSEC
Nicolas PRAT
ESSEC
July 2001
GROUPE ESSEC
CERNTRE DE RECHERCHE / RESEARCH CENTER
AVENUE BERNARD HIRSCH - BP 105
95021 CERGY-PONTOISE CEDEX FRANCE
TÉL. : 33 (0) 1 34 43 30 91
FAX : 33 (0) 1 34 43 30 01
Mail : [email protected]
GROUPE ESSEC,
ÉTABLISSEMENTS PRIVÉS D'ENSEIGNEMENT SUPÉRIEUR,
ASSOCIATION LOI 1901,
ACCRÉDITÉ AACSB - THE INTERNATIONAL ASSOCIATION
FOR MANAGEMENT EDUCATION,
AFFILIÉ A LA CHAMBRE DE COMMERCE ET D'INDUSTRIE
DE VERSAILLES VAL D'OISE - YVELINES.
WEB : WWW.ESSEC.FR
FROM UML TO ROLAP MULTIDIMENSIONAL DATABASES USING A PIVOT
MODEL
Jacky AKOKA1, Isabelle COMYN-WATTIAU2 and Nicolas PRAT3
Abstract
Effective data warehouse design requires a conceptual modeling phase. This paper describes a method for data
warehouse design. The method relies on the three modeling levels that have been defined for transactional
systems design : conceptual, logical and physical. The conceptual phase creates an object-oriented schema which
is represented using the UML notation. To this end, UML is enriched with concepts relevant to multidimensional
systems. The logical phase consists in mapping the enriched UML schema into a multidimensional one. We
propose a set of rules facilitating the design process. The rules are based on a multidimensional model unifying
the main decisional concepts as provided by ROLAP tools. The physical phase allows the designer to map the
multidimensional schema into a physical schema, depending on the target decisional tool. We illustrate our
approach on a case study and compare it to the state-of-the-art.
Keywords
Data warehouse, multidimensional system, design method, UML, ROLAP.
Résumé
Pour être efficace, la conception d’entrepôts de données doit s’appuyer sur une phase de modélisation
conceptuelle. Ce document de recherche décrit une méthode des conception d’entrepôts de données. La méthode
s’appuie sur les trois niveaux de modélisation qui ont été définis pour la conception de systèmes transactionnels:
les niveaux conceptuel, logique et physique. La phase conceptuelle crée un schéma orienté-objet représenté avec
le formalisme UML. A cet effet, UML est enrichi de concepts propres aux systèmes multidimensionnels. La
phase logique consiste à transformer le schéma UML enrichi en schéma multidimensionnel. Nous proposons un
ensemble de règles qui facilitent le processus de conception. Les règles se fondent sur un modèle
multidimensionnel qui unifie les principaux concepts du décisionnel, tels que les concepts mis en œuvre dans les
outils ROLAP. La phase physique permet au concepteur de transformer le schéma multidimensionnel en un
schéma physique, en fonction de l’outil décisionnel cible. Nous illustrons notre approche par une étude de cas et
la situons par rapport à l’état de l’art.
Mots clés
Entrepôt de données, système multidimensionnel, méthode de conception, UML, ROLAP.
1
CEDRIC-CNAM & INT, 292 rue Saint-Martin, 75141 Paris cedex 03, France. Email: [email protected]
2
Université de Cergy & ESSEC, 2 rue Chauvin, 95302 Pontoise cedex, France. Email: [email protected]
3
ESSEC, Department of Information and Decision Systems, avenue Bernard Hirsch, BP 105, 95021 Cergy
cedex, France. Email: [email protected]
1. Introduction
The data warehousing market is growing rapidly with an estimated average growth rate of 30% over the last four
years. It is very likely to approach 5 billion dollars by 2003, compared to 1 billion in 1996 [OLAP report, 2001].
Like the relational database market at its beginning, the OLAP market has no dominant players. However, in the
near future, analysts expect this market to be consolidated. All these factors contribute to suggest a strong
analogy between the data warehousing field and the relational database one in the early eighties.
OLAP design process is described by tool vendors as much easier than classical database design. We claim that
this process is at least as difficult as the design of transactional databases. Since data warehouses are developed
to provide managers with data on which they will build queries depending on their constantly evolving needs, the
design process is crucial.
Each OLAP tool is based on a specific underlying logical model. Although the various logical models rely on
very similar concepts, no real normalization was provided nor agreed on. We claim that a standardization of the
multidimensional model will allow both the users to better understand the underlying concepts and the tool
editors to adopt a unified view. Moreover, companies using several OLAP tools will profit from having a unique
logical description of their decisional data.
Unlike the database world which has taken some time to adopt the three levels of abstraction recommended by
ANSI/X3/SPARC, the data warehouse actors should rapidly divide the modeling task according to the three
conceptual, logical and physical levels. The conceptual level allows the data warehouse designer to build a high
level abstraction of decisional data independently from implementation issues. The logical level maps this
abstraction into a standard multidimensional representation. Finally, taking into account the target tool, the
physical step aims at building a schema to be implemented on a specific platform.
The main objective of this paper is to propose a four-step design method for data warehouse development,
including rules for systematic mapping between each step. In order to address several target tools, we define a
unified multidimensional model at the logical level and build our process on it. The paper is organized as
follows. Section 2 describes the unified multidimensional model. Section 3 presents the four-step design method.
A case study illustrating the process is presented in Section 4. Section 5 compares our approach with the state-ofthe-art. Finally, Section 6 concludes and describes further research.
2
2. A Unified Multidimensional Model
In contrast with the relational model, there is no standard multidimensional database model. More precisely, there
is no commonly accepted formal multidimensional data model. As a consequence, many multidimensional
models have been proposed in the literature [Agrawal et al, 1997 ; Blaschka et al, 1998 ; Cabibbo & Torlone,
1998 ; Chaudhuri & Dayal, 1997 ; Golfarelli et al, 1998 ; Gyssens & Lakshmanan, 1997 ; Kimball, 1996 ; Li &
Wang, 1996; Pedersen & Jensen, 1999 ; Vassiliadis & Sellis, 1999]. The concepts used vary depending on the
authors and some concepts, e.g. the notion of “fact”, are employed with various meanings. Furthermore, there is
no consensus concerning the level of the multidimensional model (physical, logical or conceptual). The star and
snowflake models presented in [Kimball, 1996] have often been considered to be at the physical level, since the
choice between stars and snowflakes is based on performance considerations (trade-off between query
performance and optimization of disk space). More recent publications have placed the multidimensional model
at the logical level [Vassiliadis, 1999] or even at the conceptual level [Golfarelli et al, 1998 ; Hüsemann et al,
2000].
Our strong belief is that the multidimensional model belongs to the logical level. Even though there is no
consensus on this model, it clearly exists independently of physical implementations. However, the
multidimensional model should not be situated at the conceptual level since the concepts of this model (e.g. the
concept of dimension) are not as close to reality as concepts like the object (used in conceptual object models
like UML [OMG, 2001] for example). There is indeed a strong parallel between the relational model and the
multidimensional model - e.g. the definitions or attempts to define an associated query language and a
normalization theory. This is the reason why we argue that both models should be considered as belonging to the
same level, i.e., the logical level.
In the multidimensional model, data are organized in (hyper)cubes. Although the detailed concepts of this model
vary depending on the authors, we can describe multidimensional semantics using four concepts which appear
recurrently, namely the notions of measure, dimension, hierarchy and attribute. Our model is composed of these
four concepts, which are illustrated in Figure 1.
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PRODUCT
CI
TY
RE
GI
ON
DAY
MONTH
YEAR
QUARTER
Quantity sold
product name
unit price
LEGEND:
Measure
DIMENSION
Attribute
Hierarchy
CATEGORY
Figure 1 : Multidimensional representation of data
The key concept of the multidimensional model is the notion of measure. A measure is typically a quantitative
data, a numeric value of interest for the analysis. Examples of measures are the quantity sold and the total dollar
amount by product, date and city. A measure needs not to be of numeric type, as long as its values are totally
ordered. For example, it can be an enumeration type. Thus, the satisfaction of customers with a product may be
measured on a four-value scale (unsatisfied – mitigated – satisfied – enthusiastic). In a cube, the measures
correspond to the content of the cells.
The dimensions form the edges of the cube. Each measure is associated with one or several dimensions, which
specify the context of the measure. In the example of Figure 1, the quantity sold is dimensioned by the
dimensions product, day and city, in other words, by “quantity sold”, we mean the quantity sold for a particular
product at a particular date in a particular city. Note that a dimension is represented by its identifier (e.g. the
values of the dimension product are product codes).
Sometimes, we need to represent an event linking several dimensions without having any measure associated with
this event [Kimball, 1996]. For this purpose, we use a specific type of measure, called dummy measure. Consider
for example the relationship “reservation” linking a borrower, a book and a reservation date, without any specific
attributes characterizing the reservation. The dummy measure “reservation” will serve to indicate which books
have been reserved by which borrowers and at which date.
The dimensions are organized in hierarchies. A hierarchy is an aggregation path between dimensions. In Figure
1, “city->region” and “day->month->quarter->year” are examples of hierarchies. A hierarchy is oriented from the
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lower to the upper abstraction levels (here, from city to region and from day to year). The arrow between two
successive dimensions may be interpreted as a functional dependency. Hierarchies are of paramount importance
in the multidimensional model since they are used to drill up and down measures. For example, the total quantity
sold by product, quarter and region may be computed by aggregating the quantities sold along the day and city
dimensions. Due to the ubiquity of time in data warehouses, time hierarchies are very frequent and even
necessary in multidimensional models.
Dimensions may be described by attributes. For example, the dimension product (i.e. product code) is described
by the product name and its unit price. Attributes are not the object of multidimensional analysis, as opposed to
measures. In other words, if a dimension is described by a feature that is a measure of interest, this feature should
be defined as a one-dimensional measure associated with this dimension.
To specify a multidimensional model, we use the following notation :
•
A dimension is specified with the key word “dimension”.
•
A measure is specified with the key word “measure” and followed by the dimensions which provide the
context for the measure (i.e. the edges of the cube). Among these dimensions, the ones that do not
functionally determine the measure are represented between parentheses. For example, consider the dollar
amount paid for a car rental. This measure is dimensioned by the hour of rental, the registration number of the
car rented and the code of the customer renting the car. However, since the same car cannot be rented at the
same time by different customers, the hour and the car registration number are sufficient to determine the
rental. Therefore, in the specification of the measure “dollar amount”, the dimension “customer code” shall be
indicated between parentheses. Dummy measures are preceded by the key word “dummy”. We need to
distinguish explicitly dummy measures from other measures since they will be implemented differently
depending on the target decisional tool.
•
A hierarchy is specified with the key word “hierarchy”, followed by the aggregation path.
•
An attribute is specified with the key word “attribute”, followed by the dimension it characterizes.
Following this notation, the multidimensional schema illustrated in Figure 1 is specified as follows :
measure quantity sold [product code, day, city]
dimension day
dimension month
dimension quarter
hierarchy day->month->quarter->year
dimension year
hierarchy product code->category
dimension product code
hierarchy city->region
dimension category
dimension city
attribute product name [product code]
dimension region
attribute unit price [product code]
5
A graphical representation is also proposed (Figure 2). Measures are stored in trapezoidal boxes and linked to
dimensions in rectangle boxes. Hierarchical links between dimensions are represented by arrows. Finally,
attributes are stored in dimension boxes.
Category
Product
Code
quantity sold
City
Region
Product name
Unit price
Day
Month
Quarter
Year
Figure 2 : A graphical representation of a multidimensional schema
Our unified multidimensional model is generic and can be easily mapped into the usual multidimensional models
that can be found in the literature and/or in decisional tools. This model is used as a pivot model in our design
method, as described in the next section.
3. The Design Method
Starting from user requirements, our method is based on the three usual abstraction levels : conceptual, logical
and physical (Figure 3). It is therefore decomposed into four phases :
- In the conceptual phase, the designer represents the universe of discourse using the UML notation [OMG,
2001] along with the associated approach of development[Jacobson et al, 1999] (step 1) ; the UML schema
is then enriched and transformed to take into account the specific features of multidimensional modeling
(step 2).
- In the logical phase, the enriched and transformed UML schema is mapped into our unified
multidimensional model, using mapping rules.
6
- The physical phase allows the designer to convert the multidimensional schema into a physical schema,
depending on the target decisional tool. In this paper, we focus on ROLAP tools (tools using the star model,
the snowflake model, a combination of the star and snowflake models…). A specific set of mapping rules
from the logical to the physical model is defined for each type of tool.
- The data confrontation phase consists in mapping the physical schema data elements with the data sources.
It leads to the definition of queries for extracting the data corresponding to each component in the physical
schema. This is a very complex problem, going beyond the scope of this paper. However, it is important to
mention this crucial phase in the data warehouse design process.
U n iverse of discourse
con c e p t u a l m odelin g
U M Lsch e m a
CONCEPTUAL
DESIGN
enrichm e n t/transformation
Enriched/transform ed U M L sch e m a
Logical m apping
LOGICAL
DESIGN
U n ified m ultidim ensional schem a
PHYSICAL
DESIGN
P h ysical m a p p ing
MOLAP
sch e m a
ROLAP
star
sch e m a
R O L A P snowflake
sch e m a
…
Source confrontation
DATA
CONFRONTATION
D a ta W a r ehouse M etadata
Figure 3 : The four phases of the design method
3.1. Conceptual design
OLAP systems are emerging as the dominant approach in data warehousing. OLAP allows designers to model
data in a multidimensional way as hyper-cubes. ROLAP snowflakes and stars as well as MOLAP cubes do not
7
offer a visualization of data structures independently from implementation issues. Therefore, they do not ensure a
sound data warehouse conceptual design.
Our design method uses the Unified Modeling Language (UML) at the conceptual level. This choice can be
justified along at least three considerations :
- The UML is now a well-known language for software engineers.
- It provides simple basic constructs to describe at a high level of abstraction the important concepts of the
application domain.
- It can be easily mapped to relational as well as to multidimensional logical models.
Due to these considerations, many authors use the UML notation at a first step of the transactional database
design. To the best of our knowledge, it has not been used in ROLAP systems.
Our design method consists of a two-step conceptual design :
- The first step leads to a UML schema, more precisely to a class diagram without operations.
- The second step enriches and transforms this schema in order to facilitate its automatic mapping to the
multidimensional model. Four types of operations are conducted : the determination of identifying attributes,
the determination of attributes representing measures, the migration of association attributes and the
suppression of generalizations.
a) Determination of identifying attributes
In contrast to the ER model or the relational model, the notion of identifying attribute is not defined in the
standard UML notation; this notion is replaced by the concept of object identity. However, we need to determine
the identifying attributes of classes in order to define the dimensions of the multidimensional model at the logical
level. Note that since an association class is identified by the n-uple of identifiers of the participating classes, the
determination of identifiers is necessary only for the other classes (called ordinary classes). For each ordinary
class of the UML schema, the user and the data warehouse designer have to decide which attribute or
combination of attributes identify the class. If necessary, a specific attribute is created in order to identify the
class. Identifying attributes are specified using the UML construct of tagged value: the suffix {id} is added to
each identifying attribute, as in [Morley et al, 2000]. This process can be synthesized by the following rule :
Rule Rcc1: Each attribute of an ordinary class is either an identifying attribute or not.
8
b)
Determination of attributes representing measures
We differentiate between attributes representing measures, and attributes which can be defined as qualitative
values. As described in the previous section, this distinction is not based on data types even if, generally,
measures are numerical and qualitative attributes are not. Therefore this differentiation cannot be performed
automatically. The user and the data warehouse designer have to decide which attributes must be considered as
measures. Note that this does not deal with identifying attributes determined previously, since an identifying
attribute cannot be a measure. In the UML schema, attributes representing measures are specified by the tagged
value {meas}. This process can be synthesized by the following rule :
Rule Rcc2: Each attribute is either a measure or not.
c) Migration of association attributes
This step is concerned with 1-1 and 1-N associations having specific attributes (these associations are actually
association classes, since an ordinary association cannot bear attributes in UML). Let us mention that this case is
rarely encountered. If specific attributes are present in these associations, the designer has first to check the
validity of this representation. Even if their presence cannot be questioned, they cannot be mapped into
multidimensional models by using hierarchies. The reason is that, in multidimensional models, these hierarchies
do not contain information. Therefore, they must migrate from the association to the participating class on the N
side. In case of 1-1 association, they can indifferently migrate into one of the two classes. After migrating the
attributes of a 1-N or 1-1 association, the latter is transformed into an ordinary association unless it is connected
to other associations or classes. The rules for migrating association attributes are expressed as folows :
Rule Rcc3 : Each attribute belonging to a 1-1 association is transferred to one of the classes
involved in the association.
Rule Rcc4 : Each attribute belonging to a 1-N association is transferred to the N-class, i.e. the
class involved several times in the association.
d) Suppression of generalizations
The inheritance links of the UML notation cannot be mapped directly to hierarchies in the multidimensional
model, since the semantics of hierarchies in object-oriented models and multidimensional model differ. However,
we want to preserve the information contained in UML generalizations and transform these hierarchies to enable
their correct mapping to multidimensional hierarchies in the logical phase. To this end, we transform the
generalizations into aggregations and classes following the proposal of [Moody & Kortink, 2000] for ER models.
9
We have adapted this rule to UML and extended it to consider the different cases of partial specialization and/or
overlapping specialization. The corresponding rule is informally described below :
Rule Rcc5 : For each level i of specialization of a class C, a class named Type-C-i is created.
The occurrences of these classes define all the specializations of C. In case of overlapping
between specializations, a special value is created for each overlapping between two or more
sub-classes of C. In case of partial specialization, the special value “others” is created. A N-1
aggregation is created between the classes C and Type-C-i.
CONCEPTUAL
RULES
Attribute
Rcc2
X
X
1-1 association attribute
Rcc3
Rcc4
Rcc5
X
1-N association attribute
X
Generalization
X
Identifying attribute
CONCEPTUAL
Rcc1
X
Attribute - measure
X
Attribute - not a measure
X
Class attribute
X
X
Class
X
N-1 aggregation
X
Figure 4: Transformation of the UML schema
These rules are sketched in Figure 4. Thanks to these transformations, the resulting UML schema can then be
automatically mapped into a logical multidimensional schema, as described in the following section.
3.2. Logical design
The aim of the logical design phase is to convert the enriched UML conceptual schema to a logical one expressed
with the multidimensional concepts of our unified model. This schema is generated using specific rules
represented as production rules (Figure 5).
10
RULES
Rcl1 Rcl2 Rcl3 Rcl4 Rcl5 Rcl6
CONCEPTUAL
Identifier of an ordinary class
X
Non-identifying attribute of an ordinary class, which
is not a measure of interest
Non-identifying attribute of an ordinary class, which
is a measure of interest
X
X
Attribute of an association class
X
Path formed by N-1 associations
X
N-M or N-ary association without at least one
attribute that is always defined
LOGICAL
Dimension
X
X
Measure
X
X
Dummy measure
X
Dimension attribute
X
Hierarchy
X
Figure 5 : Conceptual UML towards logical multidimensional concepts mapping rules
Figure 5 presents the main rules to be used. Needless to say that mapping an enriched UML schema to a
multidimensional schema requires more rules. The rules map first the ordinary classes – classes that are not
association classes – and their attributes (rules Rcl1 to Rcl3) and then the associations – association classes or
ordinary associations – and their attributes (rules Rcl4 to Rcl6). The ordinary classes of the conceptual UML
model are mapped by rule Rcl1:
Rule Rcl1: The identifier of each ordinary class is mapped into a dimension in the multidimensional model.
If an identifier is composed of several attributes in the UML model, it is mapped into a single dimension, formed
by concatenating the attributes composing the identifier. The attributes of ordinary classes are mapped by rules
Rcl2 and Rcl3:
Rule Rcl2: The non-identifying attributes of each ordinary class are mapped into dimension attributes in
the multidimensional model if these non-identifying attributes are not measures of interest.
The multidimensional attributes are associated with the dimension obtained by mapping the identifier of the
ordinary class (rule Rcl1).
Rule Rcl3: The non-identifying attributes of each ordinary class are mapped into measures in the
multidimensional model if these non-identifying attributes are measures of interest.
The measures are associated with the dimension obtained by mapping the identifier of the ordinary class (rule
Rcl1). The attributes of association classes are mapped using rule Rcl4 :
11
Rule Rcl4: The attributes of each association class are mapped into measures, associated with dimensions
obtained by mapping the identifiers of the ordinary classes directly or indirectly participating in the
association class (rule Rcl1).
If the association class bearing the attributes has one (or several) participating class(es) with a maximal
cardinality of 1, the dimension(s) obtained by mapping the identifier(s) of this (these) class(es) should be
indicated between parentheses in the specification of the measures, to express the fact that the dimension(s) are
not necessary to functionally determine the measures. Finally, associations are mapped using rules Rcl5 (for
binary N-1 associations) and Rcl6 (for other associations) :
Rule Rcl5: A path formed by N-1 associations is mapped into a hierarchy in the multidimensional model.
Very often, N-1 associations are aggregations or compositions. Therefore, the hierarchies of the multidimensional
model generally correspond to aggregation/composition paths in the UML model.
Rule Rcl6: Every N-M or N-ary association without at least one attribute that is always defined is mapped
into a dummy measure, associated with dimensions obtained by mapping the identifiers of the ordinary
classes directly or indirectly participating in the association (rule Rcl1).
Note that if an N-M association or an N-ary association has attributes, these attributes have already been mapped
into measures (rule Rcl4). If one of these attributes is always defined, the corresponding measure is also always
defined, making the definition of a “dummy measure” unnecessary.
At the end of the logical design phase, the universe of discourse is described through a unified multidimensional
schema. Depending on the decisional tool to be used, this schema must be implemented, i.e. mapped into
physical concepts.
3.3. Physical design
The physical design phase depends heavily on the target system. ROLAP systems implement the
multidimensional schema in a relational database system (RDBMS). This category of systems may be subdivided
depending on the model used to implement the multidimensional schema in the RDBMS. The models used are
typically the star model, the snowflake model, or any combination or extension of these two models.
For each type of target system i.e. for each physical model, a specific set of mapping rules from the logical
multidimensional model has to be defined. In this paper, we consider two types of target systems : ROLAP star
and ROLAP snowflake. Figure 6 describes the mapping rules, assuming the star model is used for
implementation.
12
LOGICAL
RULES
Rls1
Dimension which dimensions at least one measure
X
Non-dummy measure
Rls2
Rls3
Rls4
Rls5
X
Dummy measure
X
Hierarchy
X
PHYSICAL
Dimension attribute
X
Dimension table
X
Fact table
X
Fact table attribute
X
Dimension table attribute
X
X
X
Figure 6 : Logical multidimensional towards physical ROLAP star concepts mapping rules
The dimensions of the logical model are mapped by rule Rls1:
Rule Rls1: Every dimension dimensioning at least one measure is mapped into a dimension table and an
associated primary key.
Note that the logical dimensions that do not dimension any measure, i.e. the ones that only participate in
hierarchies, will be taken care of by rule Rls4. Measures are mapped using rules Rls2 and Rls3:
Rule Rls2: Every non-dummy measure is mapped into a fact table attribute (i.e. a fact) in table T, whose
foreign keys correspond to the logical dimensions of the measure and whose primary key corresponds to
the subset of these dimensions which are not indicated in parentheses. If table T does not exist, it is defined
when mapping the measure.
Note that the logical dimensions indicated in parentheses in the specification of a measure are the ones that are
functionally determined by the others. Therefore, they are not used in the definition of the primary key of table T.
Rule Rls3: Every dummy measure is mapped into a fact table whose foreign keys correspond to the logical
dimensions of the measure and whose primary key corresponds to the subset of these dimensions which are
not indicated in parentheses.
The fact tables generated by rule Rls3 are thus fact tables with no fact [Kimball, 1996]. The hierarchies of the
logical model are mapped using rule Rls4:
Rule Rls4: Every hierarchy D1->D2->…->Dn of the logical model is mapped by considering all the subhierarchies Dj->Dj+1…->Dn where 1<=j<n and Dj dimensions at least one measure. A sub-hierarchy Dj>Dj+1…->Dn is mapped in the physical model by defining in the dimension table identified by Dj an
attribute corresponding to each of the Di (where j<i<=n).
Dimension attributes are mapped with rule Rls5:
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Rule Rls5: Every attribute of every dimension Di of the logical model is mapped into a dimension table
attribute, in all the dimension tables which possess an (identifying or non-identifying) attribute
corresponding to Di.
Figure 7 describes the mapping rules, assuming the snowflake model is used for implementation. The difference
between the star and snowflake models lies in the explicit representation of hierarchies in the snowflake model.
As a result, all the dimensions of the logical model are mapped into dimension tables in the physical model (rule
Rlf1); the link between any consecutive dimensions (Di,Di+1) in any hierarchy is mapped into a foreign key
(dimension table attribute + referential integrity constraint) in the dimension table corresponding to Di (rule
Rlf4); each attribute of each dimension Di of the logical model is mapped into a dimension table attribute in the
dimension table corresponding to Di (rule Rlf5).
RULES
Rlf1
LOGICAL
Dimension
Rlf2
Rlf3
Rlf4
X
Non-dummy measure
X
Dummy measure
X
Hierarchy
X
Dimension attribute
PHYSICA
Rlf5
X
Dimension table
X
Fact table
X
Fact table attribute
X
Dimension table attribute
Referential integrity constraint
X
X
X
X
Figure 7 : Logical multidimensional towards physical ROLAP snowflake concepts mapping rules
The rules proposed above allow the designer to map a multidimensional logical model to the most standard
ROLAP physical models. The next section illustrates this design method through an example.
4. Case study
A firm is faced with the definition of an optimal media-planning system. The company wishes to launch
advertising campaigns for its products using several types of media (radio, TV, newspapers, magazines, etc.). Its
objective is to maximize the number of consumers being exposed to the advertising campaign. To support the
decision-making process, we need to define a multidimensional model with all the data relevant to the mediaplanning problem.
14
4.1. Conceptual design
The conceptual schema is represented in Figure 8. It contains data related to the products concerned by the
advertising campaigns. The consumers are represented as targets located at different regions. The consumers are
defined according to their purchase behavior over time which is strongly influenced by the advertising
campaigns. The consumers are exposed to several types of media. This exposure is measured over time. The
schema includes all the key information about the media shareholding. The real schema has been simplified in
order to be more readable.
Product_type
Media_type
1..*
product_type
product_unit
*
media_type
insertion
may_be_advertised_in
1
1
*
*
Product
Media
Region
product_code
product_name
region
number_of_inhabitants
percentage_of_region
1..*
1..*
media_name
advertising_price
gets
*
*
1
for
*
*
1
exposure
consumption
1..*
*
product_consumption
Target
*
*
*
target_code
status
minimum_age
maximum_age
sex
is_strongly_influenced_by
main_shareholder
media_exposure
1
Shareholder
shareholder_name
*
Advertising_campaign
Year
campaign_code
year
*
*
Private_shareholder Public_shareholder
1
during
*
1
public_shareholder_level
1..*
Quarter
*
quarter
1
in
Person
1..*
Date
Company
manager_name
*
dd_mm_yy
Figure 8 : The UML conceptual schema for the media-planning example
The UML schema is then enriched by determination of identifying attributes {id}, attributes representing
measures {meas}, migration of association attributes and suppression of generalizations. Two classes Typeshareholder-1
and
Type-shareholder-2
are
created
and
renamed
as
Shareholder_type
and
Private_shareholder_type. The set of occurrences of Shareholder_type is {private, public, both}. The set of
occurrences of Private_shareholder_type is {person, company, others}. The attribute percentage_of_region is
transferred to the class Target (Rule Rcc4). The result is represented in Figure 9.
15
Product_type
Media_type
1..*
product_type {id}
product_unit
*
media_type {id}
insertion
may_be_advertised_in
1
1
*
*
Product
Media
Region
product_code {id}
product_name
region {id}
number_of_inhabitants {meas}
*
1..*
*
1
*
*
*
1
for
main_shareholder
1..*
consumption
Target
product_consumption {meas}
target_code {id}
status
minimum_age
maximum_age
sex
percentage_of_region {meas}
*
is_strongly_influenced_by
*
*
exposure
media_exposure {meas}
1
Shareholder
shareholder_name {id}
public_shareholder_level
manager_name
*
Advertising_campaign
Year
campaign_code {id}
year {id}
*
*
1..*
media_name {id}
advertising_price
gets
*
*
1
during
*
1
1..*
Quarter
*
quarter {id}
1
Shareholder_type
1
in
shareholder_type {id}
1
Private_shareholder_type
private_shareholder_type {id}
1..*
Date
*
dd_mm_yy {id}
Figure 9 : Enriched/transformed UML schema
4.2. Logical design
We give below the unified multidimensional representation resulting from the application of the mapping rules
(Figure 10). Measures are either in trapezoidal boxes or in italics inside dimension boxes. Dummy measures are
named using relationship names. A dotted line between the trapezoidal box of a measure and the rectangle box of
a dimension indicates that the dimension is not necessary to functionally determine the measure (i.e., the
dimension is represented between parentheses in the textual specification of the measure).
16
PRODUCT_
TYPE
product_type
product_unit
Dummy_may_be_advertised_in
MEDIA_TYPE
media_type
insertion
REGION
region
number_of_inhabitants
PRODUCT
Dummy_gets
MEDIA
product_code
product_name
media_name
advertising_price
TARGET
Dummy_is_
strongly_
influenced_
by
ADVERTISING_
CAMPAIGN
product_consumption
target_code
status
minimum_age
maximum_age
sex
percentage_of_region
YEAR
year
media_exposure
Dummy_main_shareholder
SHAREHOLDER
campaign_code
shareholder_name
public_shareholder_level
manager_name
QUARTER
quarter
Dummy_in
DATE
dd_mm_yy
SHAREHOLDER_TYPE
PRIVATE_SHAREHOLDER_TYPE
shareholder_type
private_shareholder_type
Figure 10 : Media-planning multidimensional schema
This graphical representation allows the designer and the users to immediately recognize and differentiate the
dimensions, measures and hierarchies.
4.3. Physical design
The mapping rules described in Section 3.3 can also be applied to derive ROLAP physical schemas. Figure 11
presents the result of the application of the mapping rules in case the star model is used. Note that since many
dimension tables are shared by different fact tables, Figure 11 actually represents a constellation of stars.
17
PRODUCT_
TYPE
product_type
product_unit
REGION
PRODUCT
product_code
product_name
product_type
product_unit
CONSUMPTION
FK product_code
FK target_code
FK quarter
product_consumption
ADVERTISING_
CAMPAIGN
campaign_code
quarter
year
product_code
product_name
product_type
product_unit
region
MAY_BE_ADVERTISED_IN
MEDIA_TYPE
FK product_type
FK media_type
media_type
insertion
MEDIA
REGION_FIGURES
GETS
FK region
number_of_inhabitants
FK region
FK media_name
TARGET
target_code
status
minimum_age
maximum_age
sex
region
media_name
advertising_price
media_type
insertion
TARGET_FIGURES
FK target_code
percentage_of_region
EXPOSURE
FK media_name
FK target_code
FK quarter
media_exposure
QUARTER
quarter
year
SHAREHOLDER
shareholder_name
public_shareholder_level
manager_name
shareholder_type
private_shareholder_type
DATE
dd_mm_yy
quarter
year
MAIN_SHAREHOLDER
FK media_name
FK dd_mm_yy
FK shareholder_name
IS_STRONGLY_INFLUENCED_BY
FK product_code
FK target_code
FK quarter
FK campaign_code
IN
FK campaign_code
FK media_name
Figure 11 : Media-planning ROLAP star physical schema
After the definition of the physical schema, the data confrontation phase is performed in order to map the
physical schema data elements with the data sources. Since it is beyond the scope of this paper, this confrontation
phase is not performed.
5. State-of-the-Art
The deficit in data warehouse design is real. Very few methods have been proposed until now. Let us mention
[Akoka & Prat, 1997 ; Golfarelli et al., 1998 ; Cabibbo & Torlone, 1998 ; Moody & Kortink, 2000].
Unlike papers describing design methods, as stated by [Sapia et al., 1998], a fair number of publications is
available concerning multidimensional data modeling but with very few recognizing the importance of the
separation of conceptual, logical and physical issues. Moreover, even if the three levels are considered, some
18
confusion exists between, on the one hand, conceptual and logical models and, on the other hand, logical and
physical models. A real confusion also seems to exist between the conceptual and physical aspects. As an
example, the multidimensional modeling manifesto of Kimball is inadequate for conceptual modeling [Kimball,
1997]. His approach tends to include physical design issues, especially with his propositions of star and
snowflake schemas which appear to be not independent from implementation issues.
a) Conceptual-logical models
[Cabibbo & Torlone, 1998] focus on logical design issues and propose a logical model for OLAP systems. They
assume that there exists an integrated ER schema of the operational data sources. They provided a methodology
to transform the ER schema into a dimensional graph. [Golfarelli & Rizzi, 1998] proposed a conceptual model
called Dimensional-Fact schema. They provided a methodology to transform the ER model of the data sources
into a Dimensional-Fact model. Note that their approach is not based on a formal data model. [Lehner et al.,
1998] proposed a conceptual multidimensional model which includes some mechanism to structure qualifying
information. Note that no formal graphical notation is provided. [Sapia et al., 1998] presented a specialization of
the ER model called Multidimensional Entity-Relationship Model (MER) expressing the multi-dimensional
structure of the data by means of two specialized relationship sets and a specialized entity set. Their approach
models user requirements independently from the structure of the data sources. [Gyssens & Laskshmanan, 1997]
proposed a multi-dimensional database model providing the functionalities necessary for OLAP-based
applications. They made a clear separation between the structural aspects and the contents, allowing them to
define data manipulation languages in a transparent way. They defined an algebra and a calculus.
b) Logical-physical models
An interesting survey of logical models for OLAP databases can be found in [Vassiliadis & Sellis, 1999]. The
main features of these models are that they offer systematically a logical view of data to be queried by a set of
operators and, usually, a set of implementation mechanisms. Among these models, let us mention [Agrawal et al.,
1997] who provide a logical model in which dimensions and measures are treated in a symmetric way and where
multiple hierarchies of dimensions allow ad hoc aggregates. [Pedersen & Jensen, 1999] propose a multidimensional logical data model for complex data, justifying nine requirements to be satisfied in order to support
complex data. They showed that their model covers the nine requirements in a better way than previous models
19
[Rafanelli & Shoshani, 1990 ; Agrawal et al, 1997 ; Gray et al, 1996 ; Kimball, 1996 ; Li & Wang, 1996 ;
Gyssens & Laskshmanan, 1997 ; Datta & Thomas, 1997 ; Lehner, 1998]. [Harinarayan et al, 1996] investigated
mainly physical design and implementation issues.
Finally, our method is an attempt to define a generic framework based on the following principles :
-
it makes a clear distinction between the classical steps of database design (conceptual, logical, physical),
-
it unifies the different multidimensional concepts into a single and generic model,
-
it can be adapted to all OLAP vendor engines,
-
it capitalizes on the existing UML schemas,
-
it is based on well-established UML concepts.
6. Conclusion and Further Research
We have described a method for designing and developing data warehouses. Capitalizing on database design
techniques, we proposed a conceptual design phase based on UML notation, followed by an
enrichment/transformation of this schema. This enrichment/transformation allows the designer to automatically
convert this conceptual representation into a logical multidimensional model. At this step, we proposed a generic
multi-dimensional model independent from implementation issues and unifying ROLAP and MOLAP concepts.
Using mapping rules, this generic logical schema can be mapped to any physical multi-dimensional platform. A
case study was described to illustrate the main features of the method.
Several questions still remain open. A formal foundation for the unifying multidimensional model needs to be
developed. The different mapping rules sets have to be enriched and efficiently implemented. A reverse
engineering approach taking into account existing data warehouses and/or data marts must be developed [Akoka
& Comyn-Wattiau, 1999]. An intensive real life case study should be undertaken and used to validate the
approach. We are currently working on these issues.
7. References
[Agrawal et al, 1997] R. Agrawal, A. Gupta, S. Sarawagi, “Modeling multidimensional databases”, 13th
International Conference on Data Engineering (ICDE ’97), Birmingham, UK, April.
[Akoka & Comyn-Wattiau, 1999] J. Akoka, I. Comyn-Wattiau, “Rétro-conception des « datawarehouses » et des
systèmes multidimensionnels”, 17ème Congrès INFORSID, La Garde, June.
[Akoka & Prat, 1997] J. Akoka, N. Prat, “Modélisation logique des données dans les Systèmes Multidimensionnels d'Aide à la Décision : la méthode MAP”, Revue des Systèmes de Décision, Vol 6(2), June.
20
[Blaschka et al, 1998] M. Blaschka, C.Sapia, G.Höfling, B.Dinter, “Finding your way through multidimensional
data models”, DEXA Workshop on Data Warehouse Design and OLAP Technology (DWDOT ’98), Vienna,
Austria.
[Cabibbo & Torlone, 1998] L. Cabibbo, R. Torlone, “A Logical Approach to Multidimensional Databases”,
Proceedings of 6th International Workshop on Extending Database Technology (EDBT’1998), Valencia (Spain),
March.
[Chaudhuri & Dayal, 1997] S.Chaudhuri, U.Dayal, “An overview of data warehousing and OLAP Technology”,
SIGMOD Record, volume 26, number 1, March.
[Datta & Thomas, 1997] A. Datta, H. Thomas, “A Conceptual Model and Algebra for On-Line Analytical
Processing in Decision Support Databases”, Proceedings of WITS.
[Golfarelli et al, 1998] M.Golfarelli, D.Maio, S.Rizzi, “Conceptual design of data warehouses from E/R
schemes”, 31st Hawaii International Conference on System Sciences, Hawaii, USA, January.
[Golfarelli & Rizzi, 1998] M.Golfarelli, S.Rizzi, “A methodological framework for data warehousing design”,
ACM workshop on data warehousing and OLAP.
[Gray et al, 1996] J.Gray et al, “Data Cube : A Relational Aggregation Operator Generalizing Group-By, CrossTab and Sub-Totals”, Proceedings of ICDE.
[Gyssens & Lakshmanan, 1997] M.Gyssens, L.V.S.Lakshmanan, “A foundation for multi-dimensional databases”
23rd VLDB Conference, Athens, Greece.
[Harinarayan et al, 1996] V. Harinarayan, A. Rajaraman, J.D. Ullman, “Implementing Data Cubes Efficiently”,
Proceedings of SIGMOD conference.
[Hüsemann et al, 2000] B.Hüsemann, J.Lechtenbörger, G.Vossen, “Conceptual data warehouse design”, 2nd
International Workshop on Design and Management of Data Warehouses (DMDW 2000), Stockholm, Sweden,
June.
[Jacobson et al, 1999] I. Jacobson, G. Booch, J. Rumbaugh, “The Unified Software Development Process”,
Addison Wesley Publishing Company.
[Kimball, 1996] R.Kimball, “The data warehouse toolkit”, John Wiley & Sons.
[Kimball, 1997] R. Kimball, “A Dimensional Modeling Manifesto”, DBMS on-line, http://www.dbmsmag.com/.
[Lehner, 1998] W. Lehner, “Modeling Large Scale OLAP Scenarios”, Proceedings of EDBT.
[Lehner et al, 1998] W. Lehner, J. Albrecht, H. Wedekind, “Normal Forms for Multidimensional Databases”,
Proceedings 10th SSDBM conference, Italy, July.
[Li & Wang, 1996] C.Li, X.S.Wang, “A data model for supporting on-line analytical processing”, Proceedings
Conference on Information and Knowledge Management (CIKM ’96), Baltimore, USA, November.
[Moody & Kortink, 2000] D.L. Moody, M.A.R. Kortink, “From Enterprise Models to Dimensional Models : A
Methodology for Data Warehouse and Data Mart Design”, 2nd International Workshop on Design and
Management of Data Warehouses (DMDW 2000), Stockholm, Sweden, June.
[Morley et al, 2000] C. Morley, J. Hugues, B. Leblanc, "UML, pour l'analyse d'un système d'information",
Informatique Dunod, Paris.
[OLAP report, 2001] The OLAP Report – Market share analysis, http://www.olapreport.com/Market.htm
[OMG, 2001] Unified Modeling Language, http://www.omg.org/technology/documents/formal
[Pedersen & Jensen, 1999] T.B.Pedersen, C.S.Jensen, “Multidimensional data modeling for complex data”, 15th
International Conference on Data Engineering (ICDE ’99), Sydney, Australia, March.
[Rafanelli & Shoshani, 1990] M. Rafanelli, A. Shoshani, “STORM : A Statistical Object Representation Model”,
Proc. of SSDBM.
[Sapia et al, 1998] C. Sapia, M. Blaschka, G. Höfling, B. Dinter, “Extending the E/R Model for the
Multidimensional Paradigm”, International Workshop on Data Warehousing and Data Mining in conjunction
with ER98, Singapore, 1998.
[Vassiliadis, 1999] P. Vassiliadis, “Gulliver in the land of data warehousing : practical experiences and
observations of a researcher”, Proceedings of the International Workshop on Design and Management of Data
Warehouses (DMDW’2000), Stockholm, June.
[Vassiliadis & Sellis, 1999] P.Vassiliadis, T.Sellis, “A survey of logical models for OLAP databases”, SIGMOD
Record, volume 28, number 4, December.
21
ESSEC
CE NTRE
DE RECHERCHE
LISTE DES DOCUMENTS DE RECHERCHE DU CENTRE DE RECHERCHE DE L’ESSEC
(Pour se procurer ces documents, s’adresser au CENTRE DE RECHERCHE DE L’ESSEC)
LISTE OF ESSEC RESEARCH CENTER WORKING PAPERS
(Contact the ESSEC RESEARCH CENTER for information on how to obtain copies of these papers)
[email protected]
1997
97001 BESANCENOT D., VRANCEANU Radu
Reputation in a Model of Economy-wide Privatization.
97002 GURVIEZ P.
The Trust Concept in the Brand-consumers Relationship.
97003 POTULNY S.
L’utilitarisme cognitif de John Stuart Mill.
97004 LONGIN François
From Value at Risk to Stress Testing: The Extreme Value Approach.
97005 BIBARD Laurent, PRORIOL G.
Machiavel : entre pensée du pouvoir et philosophie de la modernité.
97006 LONGIN François
Value at Risk: une nouvelle méthode fondée sur la théorie des valeurs extrêmes.
97007 CONTENSOU François, VRANCEANU Radu
Effects of Working Time Constraints on Employment: A Two-sector Model.
97008 BESANCENOT D., VRANCEANU Radu
Reputation in a Model of Exchange Rate Policy with Incomplete Information.
97009 AKOKA Jacky, BRIOLAT Dominique, WATTIAU Isabelle
La reconfiguration des processus inter-organisationnels.
97010 NGUYEN. P
Bank Regulation by Capital Adequacy and Cash Reserves Requirements.
97011 LONGIN François
Beyond the VaR.
97012 LONGIN François
Optimal Margin Level in Futures Markets: A Method Based on Extreme Price Movements.
97013 GROUT DE BEAUFORT Viviane
Maastricth II ou la copie à réviser.
97014 ALBIGOT J.G., GROUT DE BEAUFORT V., BONFILLON P.O., RIEGER B .
Perspectives communautaires et européennes sur la réduction du temps de travail.
Page 1
97015 DEMEESTERE René, LORINO Philippe, MOTTIS Nicolas
Business Process Management: Case Studies of Different Companies and Hypotheses for Further
Research.
97016 PERETTI Jean-Marie, HOURQUET P.G., ALIS D.
Hétérogénéité de la perception des déterminants de l’équité dans un contexte international.
97017 NYECK Simon, ROUX Elyette
WWW as a Communication Tool for Luxury Brands: Compared Perceptions of Consumers and
Managers.
97018 NAPPI-CHOULET Ingrid
L’analyse économique du fonctionnement des marchés immobiliers.
97019 BESANCENOT D., ROCHETEAU G., VRANCEANU Radu
Effects of Currency Unit Substitution in a Search Equilibrium Model.
97020 BOUCHIKHI Hamid
Living with and Building on Complexity: A Constructivist Perspective on Organizations.
97021 GROUT DE BEAUFORT V., GRENOT S., TIXIER A . TSE K.L
Essai sur le Parlement Européen.
97022 BOULIER J.F., DALAUD R., LONGIN François
Application de la théorie des valeurs extrêmes aux marchés financiers.
97023 LORINO Philippe
Théorie stratégique : des approches fondées sur les ressources aux approches fondées sur les processus.
97024 VRANCEANU Radu
Investment through Retained Earnings and Employment in Transitional Economies.
97025 INGHAM M., XUEREB Jean-Marc
The Evolution of Market Knowledge in New High Technology Firms: An Organizational Learning
Perspective.
97026 KOENING Christian
Les alliances inter-entreprises et la coopération émergente.
97027 LEMPEREUR Alain
Retour sur la négociation de positions : pourquoi intégrer l’autre dans mon équation personnelle ?
97028 GATTO Riccardo
Hypothesis Testing by Symbolic Computation.
97029 GATTO Riccardo , JAMMALAMADAKA S. Rao
A conditional Saddlepoint Approximation for Testing Problems.
97030 ROSSI (de) F.X., GATTO Riccardo
High-order Asymptotic Expansions for Robust Tests.
97031 LEMPEREUR Alain
Negotiation and Mediation in France: The Challenge of Skill-based Learnings and Interdisciplinary
Research in Legal Education.
97032 LEMPEREUR Alain
Pédagogie de la négociation : allier théorie et pratique.
97033 WARIN T.
Crédibilité des politiques monétaires en économie ouverte.
97034 FRANCOIS P.
Bond Evaluation with Default Risk: A Review of the Continuous Time Approach.
97035 FOURCANS André, VRANCEANU Radu
Fiscal Coordination in the EMU: A Theoretical and Policy Perspective.
Page 2
97036 AKOKA Jacky, WATTIAU Isabelle
MeRCI: An Expert System for Software Reverse Engineering.
97037 MNOOKIN R. (traduit par LEMPEREUR Alain)
Surmonter les obstacles dans la résolution des conflits.
97038 LARDINOIT Thierry, DERBAIX D.
An Experimental Study of the Effectiveness of Sport Sponsorship Stimuli.
97039 LONGIN François, SOLNIK B.
Dependences Structure of International Equity Markets during Extremely Volatile Periods.
97040 LONGIN François
Stress Testing : application de la théorie des valeurs extrêmes aux marchés des changes.
1998
98001 TISSOT (de) Olivier
Quelques observations sur les problèmes juridiques posés par la rémunération des artistes interprètes.
98002 MOTTIS Nicolas, PONSSARD J.P.
Incitations et création de valeur dans l’entreprise. Faut-il réinventer Taylor ?
98003 LIOUI A., PONCET Patrice
Trading on Interest Rate Derivatives and the Costs of Marking-to-market.
98004 DEMEESTERE René
La comptabilité de gestion : une modélisation de l’entreprise ?
98005 TISSOT (de) Olivier
La mise en œuvre du droit à rémunération d’un comédien ayant « doublé » une œuvre audiovisuelle
er
(film cinématographique ou fiction télévisée ) avant le 1 janvier 1986.
98006 KUESTER Sabine, HOMBURG C., ROBERTSON T.S.
Retaliatory Behavior to New Product Entry.
98007 MONTAGUTI E., KUESTER Sabine, ROBERTSON T.S.
Déterminants of « Take-off » Time for Emerging Technologies: A Conceptual Model and Propositional
Inventory.
98008 KUESTER Sabine, HOMBURG C .
An Economic Model of Organizational Buying Behavior.
98009 BOURGUIGNON Annick
Images of Performance: Accounting is not Enough.
98010 BESANCENOT D., VRANCEANU Radu
A model of Manager Corruption in Developing Countries with Macroeconomic Implications.
98011 VRANCEANU Radu, WARIN T.
Une étude théorique de la coordination budgétaire en union monétaire.
98012 BANDYOPADHYAU D. K.
A Multiple Criteria Decision Making Approach for Information System Project Section.
98013 NGUYEN P., PORTAIT Roland
Dynamic Mean-variance Efficiency and Strategic Asset Allocation with a Solvency Constraint.
98014 CONTENSOU François
Heures supplémentaires et captation du surplus des travailleurs.
98015 GOMEZ M.L.
De l’apprentissage organisationnel à la construction de connaissances organisationnelles.
Page 3
98016 BOUYSSOU Denis
Using DEA as a Tool for MCDM: some Remarks.
98017 INDJEHAGOPIAN Jean-Pierre, LANTZ F., SIMON V.
Dynamique des prix sur le marché des fiouls domestiques en Europe.
98019 PELISSIER-TANON Arnaud
La division du travail, une affaire de prudence.
98020 PELISSIER-TANON Arnaud
Prudence et qualité totale. L’apport de la philosophie morale classique à l’étude du ressort psychologique
par lequel les produits satisfont les besoins de leurs utilisateurs.
98021 BRIOLAT Dominique, AKOKA Jacky, WATTIAU Isabelle
Le commerce électronique sur Internet. Mythe ou réalité ?
98022 DARMON René
Equitable Pay for the Sales Force.
98023 CONTENSOU François, VRANCEANU Radu
Working Time in a Model of Wage-hours Negociation.
98024 BIBARD Laurent
La notion de démocratie.
98025 BIBARD Laurent
Recherche et expertise.
98026 LEMPEREUR Alain
Les étapes du processus de conciliation.
98027 INDJEHAGOPIAN Jean-Pierre, LANTZ F., SIMON V.
Exchange Rate and Medium Distillates Distribution Margins.
98028 LEMPEREUR Alain
Dialogue national pour l’Europe. Essai sur l’identité européenne des français.
98029 TIXIER Maud
What are the Implications of Differing Perceptions in Western, Central and Eastern Europe for Emerging
Management.
98030 TIXIER Maud
Internal Communication and Structural Change. The Case of the European Public Service: Privatisation
And Deregulation.
98031 NAPPI-CHOULET Ingrid
La crise des bureaux : retournement de cycle ou bulle ? Une revue internationale des recherches.
98032 DEMEESTERE René
La comptabilité de gestion dans le secteur public en France.
98033 LIOUI A., PONCET Patrice
The Minimum Variance Hedge Ratio Revisited with Stochastic Interest Rates.
98034 LIOUI A., PONCET Patrice
Is the Bernoulli Speculator always Myobic in a Complete Information Economy?
98035 LIOUI A., PONCET Patrice
More on the Optimal Portfolio Choice under Stochastic Interest Rates.
98036 FAUCHER Hubert
The Value of Dependency is Plant Breeding: A Game Theoretic Analysis.
98037 BOUCHIKHI Hamid, ROND (de) Mark., LEROUX V.
Alliances as Social Facts: A Constructivist of Inter-Organizational Collaboration.
98038 BOUCHIKHI Hamid, KIMBERLY John R.
Page 4
In Search of Substance: Content and Dynamics of Organizational Identity.
98039 BRIOLAT Dominique, AKOKA Jacky, COMYN-WATTIAU Isabelle
Electronic Commerce on the Internet in France. An Explanatory Survey.
98040 CONTENSOU François, VRANCEANU Radu
Réduction de la durée du travail et complémentarité des niveaux de qualification.
98041 TIXIER Daniel
La globalisation de la relation Producteurs-Distributeurs.
98042 BOURGUIGNON Annick
L’évaluation de la performance : un instrument de gestion éclaté.
98043 BOURGUIGNON Annick
Benchmarking: from Intentions to Perceptions.
98044 BOURGUIGNON Annick
Management Accounting and Value Creation: Value, Yes, but What Value?
98045 VRANCEANU Radu
A Simple Matching Model of Unemployment and Working Time Determination with Policy Implications.
98046 PORTAIT Roland, BAJEUX-BESNAINOU Isabelle
Pricing Contingent Claims in Incomplete Markets Using the Numeraire Portfolio.
98047 TAKAGI Junko
Changes in Institutional Logics in the US. Health Care Sector: A Discourse Analysis.
98048 TAKAGI Junko
Changing Policies and Professionals: A Symbolic Framework Approach to Organizational Effects on
Physician Autonomy.
98049 LORINO Philippe
L’apprentissage organisationnel bloquée (Groupe Bull 1986-1992) : du signe porteur d’apprentissage au
Piège de l’habitude et de la représentation-miroir.
98050 TAKAGI Junko, ALLES G.
Uncertainty, Symbolic Frameworks and Worker Discomfort with Change.
1999
99001 CHOFFRAY Jean-Marie
Innovation et entreprenariat : De l’idée… au Spin-Off.
99002 TAKAGI Junko
Physician Mobility and Attidudes across Organizational Work Settings between 1987 and 1991.
99003 GUYOT Marc, VRANCEANU Radu
La réduction des budgets de la défense en Europe : économie budgétaire ou concurrence budgétaire ?
99004 CONTENSOU François, LEE Janghyuk
Interactions on the Quality of Services in Franchise Chains: Externalities and Free-riding Incentives.
99005 LIOUI Abraham, PONCET Patrice
International Bond Portfolio Diversification.
99006 GUIOTTO Paolo, RONCORONI Andrea
Infinite Dimensional HJM Dynamics for the Term Structure of Interest Rates.
99007 GROUT de BEAUFORT Viviane, BERNET Anne-Cécile
Les OPA en Allemagne.
Page 5
99008 GROUT de BEAUFORT Viviane, GENEST Elodie
Les OPA aux Pays-Bas.
99009 GROUT de BEAUFORT Viviane
Les OPA en Italie.
99010 GROUT de BEAUFORT Viviane, LEVY M.
Les OPA au Royaume-Uni.
99011 GROUT de BEAUFORT Viviane, GENEST Elodie
Les OPA en Suède.
99012 BOUCHIKHI Hamid, KIMBERLY John R.
st
The Customized Workplace: A New Management Paradigm for the 21 Century.
99013 BOURGUIGNON Annick
The Perception of Performance Evaluation Criteria (1): Perception Styles
99014 BOURGUIGNON Annick
Performance et contrôle de gestion.
99015 BAJEUX-BESNAINOU Isabelle, JORDAN J., PORTAIT Roland
Dynamic Asset Allocation for Stocks, Bonds and Cash over Long Horizons.
99016 BAJEUX-BESNAINOU Isabelle, JORDAN J., PORTAIT Roland
On the Bonds-stock Asset Allocation Puzzle.
99017 TIXIER Daniel
La logistique est-elle l’avenir du Marketing ?
99018 FOURCANS André, WARIN Thierry
Euroland versus USA: A Theoretical Framework for Monetary Strategies.
99019 GATTO Riccardo, JAMMALAMADAKA S.R.
Saddlepoint Approximations and Inference for Wrapped α-stable Circular Models.
99020 MOTTIS Nicolas, PONSSARD Jean-Pierre
Création de valeur et politique de rémunération. Enjeux et pratiques.
99021 STOLOWY Nicole
Les aspects contemporains du droit processuel : règles communes à toutes les juridictions et procédures
devant le Tribunal de Grande Instance.
99022 STOLOWY Nicole
Les juridictions civiles d’exception et l’étude des processus dans le droit judiciaire privé.
99023 GATTO Riccardo
Multivariate Saddlepoint Test for Wrapped Normal Models.
99024 LORINO Philippe, PEYROLLE Jean-Claude
Enquête sur le facteur X. L’autonomie de l’activité pour le management des ressources humaines et pour
le contrôle de gestion.
99025 SALLEZ Alain
Les critères de métropolisation et les éléments de comparaison entre Lyon et d’autres métropoles
françaises.
99026 STOLOWY Nicole
Réflexions sur l’actualité des procédures pénales et administratives.
99027 MOTTIS Nicolas, THEVENET Maurice
Accréditation et Enseignement supérieur : certifier un service comme les autres…
99028 CERDIN Jean-Luc
International Adjustment of French Expatriate Managers.
Page 6
99029 BEAUFORT Viviane, CARREY Eric
L’union européenne et la politique étrangère et de sécurité commune : la difficile voie de la construction
d’une identité de défense européenne.
99030 STOLOWY Nicole
How French Law Treats Fraudulent Bankruptcy.
99031 CHEVALIER Anne, LONGIN François
Coût d’investissement à la bourse de Paris.
99032 LORINO Philippe
Les indicateurs de performance dans le pilotage organisationnel.
99033 LARDINOIT Thierry, QUESTER Pascale
Prominent vs Non Prominent Bands: Their Respective Effect on Sponsorship Effectiveness.
99034 CONTENSOU François, VRANCEANU Radu
Working Time and Unemployment in an Efficiency Wage Model.
99035 EL OUARDIGHI Fouad
La théorie statistique de la décision (I).
2000
00001
CHAU Minh, LIM Terence
The Dynamic Response of Stock Prices Under Asymetric Information and Inventory Costs: Theory and
Evidence
00002
BIBARD Laurent
Matérialisme et spiritualité
00003
BIBARD Laurent
La crise du monde moderne ou le divorce de l’occident.
00004
MATHE Hervé
Exploring the Role of Space and Architecture in Business Education.
00005
MATHE Hervé
Customer Service: Building Highly Innovative Organizations that Deliver Value.
00006
BEAUFORT (de) Viviane
L’Union Européenne et la question autrichienne, ses conséquences éventuelles sur le champ de révision
de la CIG.
00007
MOTTIS Nicolas, PONSSARD Jean-Pierre
Value Creation and Compensation Policy Implications and Practices.
00009
BOURGUIGNON Annick
The Perception of Performance Evaluation Criteria (2): Determinants of Perception Styles.
00010
EL OUARDIGHI Fouad
The Dynamics of Cooperation.
00011
CHOFFRAY Jean-Marie
Innovation et entrepreneuriat : De l’Idée…au Spin-Off. (Version révisée du DR 99001).
00012
LE BON Joël
De l’intelligence économique à la veille marketing et commerciale : vers une nécessaire mise au point
conceptuelle et théorique.
00013
ROND (de) Mark
Reviewer 198 and Next Generation Theories in Strategy.
00014
BIBARD Laurent
Amérique latine : identité, culture et management.
Page 7
00016
BIBARD Laurent
Les sciences de gestion et l’action.
00017
BEAUFORT (de) V.
Les OPA au Danemark.
00018
BEAUFORT (de) V.
Les OPA en Belgique.
00019
BEAUFORT (de) V.
Les OPA en Finlande.
00020
BEAUFORT (de) V.
Les OPA en Irlande.
00021
BEAUFORT (de) V.
Les OPA au Luxembourg.
00022
BEAUFORT (de) V.
Les OPA au Portugal.
00023
BEAUFORT (de) V.
Les OPA en Autriche.
00024
KORCHIA Mickael
Brand Image and Brand Associations.
00025
MOTTIS Nicolas, PONSSARD Jean-Pierre
L’impact des FIE sur les firmes françaises et allemandes : épiphénomène ou influence réelle ?
00026
BIBARD Laurent
Penser la paix entre hommes et femmes.
00027
BIBARD Laurent
Sciences et éthique (Notule pour une conférence).
00028
MARTEL Jocelyn, C.G. FISHER Timothy
Empirical Estimates of Filtering Failure in Court-supervised Reorganization.
00029
MARTEL Jocelyn
Faillite et réorganisation financière : comparaison internationale et évidence empirique.
00030
MARTEL Jocelyn, C.G. FISHER Timothy
The Effect of Bankruptcy Reform on the Number of Reorganization Proposals.
00031
MARTEL Jocelyn, C.G. FISHER Timothy
The Bankruptcy Decision: Empirical Evidence from Canada.
00032
CONTENSOU François
Profit-sharing Constraints, Efforts Output and Welfare.
00033
CHARLETY-LEPERS Patricia, SOUAM Saïd
Analyse économique des fusions horizontales.
00034
BOUYSSOU Denis, PIRLOT Marc
A Characterization of Asymmetric Concordance Relations.
00035
BOUYSSOU Denis, PIRLOT Marc
Nontransitive Decomposable Conjoint Measurement.
00036
MARTEL Jocelyn, C.G. FISHER Timothy
A Comparison of Business Bankruptcies across Industries in Canada, 1981-2000.
Page 8
2001
01001
DEMEESTERE René
Pour une vue pragmatique de la comptabilité.
01003
EL OUARDIGHI Fouad, GANNON Frédéric
The Dynamics of Optimal Cooperation.
01004
DARMON René
Optimal Salesforce Quota Plans Under Salesperson Job Equity Constraints.
01005
BOURGUIGNON Annick, MALLERET Véronique, NORREKLIT Hanne
Balanced Scorecard versus French tableau de bord : Beyond Dispute, a Cultural and Ideological
Perspective.
01006
CERDIN Jean-Luc
Vers la collecte de données via Internet : Cas d’une recherche sur l’expatriation.
01007
LADHARI Riahd, MORALES Miguel, NYECK Simon, Pons Franck
Profils d’attitudes par rapport aux loisirs culturels : L’exemple des spectacles cinématographiques.
01008
NYECK Simon, PARADIS Sylvie, de COSTER Louis, BOURDEAU Laurent
Profils de satisfaction des consommateurs par rapport aux événements culturels :
L’exemple du festival de jazz.
01009
NYECK Simon, Pons Franck
Orientation des consommateurs par rapport aux événements sportifs (oes) : Proposition et validation
d’un outil de mesure .
01010
NYECK Simon
Représentations masculines des produits cosmétiques : Etude exploratoire auprès
de la population "gay".
01011
LADHARI Riahd, MORALES Miguel, NYECK Simon
Assessment of SERVQUAL Validity : An Evaluation of 10 Years of Use of the Measurement of the
Quality.
01012
VRANCEANU Radu, CERNAT Lucian
Globalisation and Growth: New Evidence from Central and Eastern Europe.
01013
BIBARD Laurent
De quoi s’occupe la sociologie ?
01014
BIBARD Laurent
Introduction aux questions que posent les rapports entre éthique et entreprise.
01015
BIBARD Laurent
Quel XXIème siècle pour l’humanité ?
01016
MOTTIS Nicolas, PONSSARD Jean-Pierre
Value-based Management at the Profit Center Level.
01017
BESANCENOT Damien, HUYNH Kim, VRANCEANU Radu
Public Debt : From Insolvency to Illiquidity Default.
01018
BIBARD Laurent
Ethique de la vie bonne et théorie du sujet : nature et liberté, ou la question du corps.
01019
INDJEHAGOPIAN Jean-Pierre, JUAN S . LANTZ F., PHILIPPE F.
La pénétration du Diesel en France : tendances et ruptures.
01020
BARONI Michel, BARTHELEMY Fabrice, MOKRANE Mahdi
Physical Real Estates: Risk Factors and Investor Behaviour.
01022
BESANCENOT Damien, VRANCEANU Radu
Quality Leaps and Price Distribution in an Equilibrium Search model
Page 9
01023
BIBARD Laurent
Gestion et Politique
01024
BESANCENOT Damien, VRANCEANU Radu
Technological Change, Acquisition of Skills and Wages in a search Economy
01025
BESANCENOT Damien, VRANCEANU Radu
Quality Uncertainty and Welfare in a search Economy
01026
MOTTIS N. , PONSARD J.P.,
L’impact des FIE sur le pilotage de l’entreprise
01027
TAPIERO Charles, VALOIS Pierre
The inverse Range Process in a Random Volatibility Random Walk
01028
ZARLOWSKI Ph., MOTTIS N.
Making Managers into Owners An Experimental Research on the impact of Incentive Schemes on
Shareolder Value Creation
01029
BESANCENOT Damien, VRANCEANU Radu
Incertitude, bien-être et distribution des salaires dans un modèle de recherche d’emploi
01030
BOUCHIKHI H.
De l’entrepreneur au gestionnaire et du gestionnaire à l’entrepreneur
01031
TAPIERO A., SULEM A.
Inventory Control wth Supply Delays, On Going Order and Emergency Supplies
01032
ROND (DE) M., MILLER A.N.
The playground of Academe : The Rhetoric and Reality of Tenure and Terror
01033
BIBARD L.
Décision et écoute
01034
GEHRKE I., HORVATH P.
Implementation of Performance Measurement. A comparative Study of French and German
Organizations
01035
NAPPI-CHOULET I.
The Recent Emergence Of Real Estate Education in French Business Schools : The Paradox of the
French Experience
Page 10
2002
Page 11