Galaxy-Gen

A Tool for Building Galaxy Model from XML Documents

Ines Ben Messaoud

, Jamel Feki

and Gilles Zurfluh

Laboratory Mir@cl, University of Sfax, Sfax, Tunisia

Laboratory IRIT, University of Toulouse 1, Toulouse, France

Keywords: Document Warehouse, XML Document, Multidimensional Modeling, Galaxy Model.

Abstract: A galaxy model is a multidimensional model dedicated for XML document warehouses. It can be seen as a

network of entities (i.e., dimensions) connected via nodes. After giving an overview of our four-steps semi-

automated method for the generation of galaxy models which aims to build data marts from XML

documents. This paper focuses on the software tool, called Galaxy-Gen that implements the proposed

method. We illustrate the Galaxy-Gen functionalities and make its first assessment through two

experiments. The first experiment is applied to a set of twenty XML documents taken from the academic

domain. The second one addressed a set of 1691 XML documents issued from the Clef-2007 collection. The

assessment is performed by comparing manual design galaxy models with those produced by the Galaxy-

Gen tool. The results are very promising.

1 INTRODUCTION

The organization’s documents help decision makers

to understand how corporate data evolve over time.

Thereby, these documents represent an important

volume that should be incorporated into the decision

support system. However, so far, decisional analyses

are based on multidimensional databases which

mainly store numeric business indicators issued

from OLTP (On-Line Transaction Processing)

systems. In practice, these numeric data represent

less than the quarter of the whole volume of data

that could be useful for decision makers. Time is

coming to focus on non-numeric data stored in

documents; these data are important for the decision

making process. Consequently, during this process

some relevant documents may be ignored while

some non pertinent documents can be considered by

intuition. The final result can be defective since the

decision is based on incomplete information.

Consequently, documents should be integrated into

the decision support system (Tseng and Chou,

2006). In other terms, as advocated by the authors of

(McCabe and al., 2000) and (Sullivan, 2001), these

documents should be warehoused. Thus, the

document warehouse (DocW) has emerged; it is

defined as a collection of documents issued from

internal and external data sources. Its main objective

is to organize documents for effective analysis or

feature extraction to enable distilled and fruitful

business intelligence (Tseng and Chou, 2006).

In practice, there are several formats of

documents such as XML format (eXtensible Markup

Language) which allows the exchange of a wide

variety of data on the Web. More accurately, there

are two types of XML documents: data-centric and

document-centric XML documents (Fuhr and al,

2001) (Kamps and Marx, 2004). Data-centric

documents contain structured data (e.g., order,

invoice) as data strored or issued from databases.

While, the document-centric XML documents are

text-rich and then less structured (e.g., scientific

articles, company reports). Furthermore, an XML

document is generally compliant to a generic

grammar called DTD ”Document Type Definition”

or XSD “XML Schema Definition”. In our work, we

are interested in XML document-centric documents.

For this latter, there are two categories of approaches

for document warehousing: contextualization of the

data warehouse with XML documents (Pérez and al.,

2008), and construction of data mart from the

metadata of documents (Krouf, 2004) (Tseng and

Chou, 2006).

In general, even if they belong to the same

domain, XML documents may have different

structures. Consequently, a step to unify these

Ben Messaoud I., Feki J. and Zurﬂuh G..

Galaxy-Gen - A Tool for Building Galaxy Model from XML Documents.

DOI: 10.5220/0005082300840095

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2014), pages 84-95

ISBN: 978-989-758-049-9

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

structures is required in order to produce a global

view describing a large document set. To use this

global view in their decisional processes, decision

makers need a multidimensional model. Therefore,

the multidimensional modeling of documents is

compulsory. In addition, it provides the user with

operators of the multidimensional algebra; thus, it

inhibits him to write complicated queries (e.g., using

XQuery). To alleviate these difficulties, we

presented in (Feki and al, 2013) an approach to build

a DocW; this approach is made up of two methods:

(i) Unification of XML document structures (Ben

Messaoud and al, 2011a) (Ben Messaoud and al,

2012), and (ii) Multidimensional modeling of

documents (Ben Messaoud and al., 2011b) (Feki and

al., 2013). In this paper, we focus on the second

method that produces a multidimensional galaxy

model for the XML DocW. More precisely, we

tackle the experiments and evaluation of this method

on academic and medical collections, through our

developed software tool called Galaxy-Gen (Galaxy

Generation).

This paper is organized as follows: In Section 2,

we discuss related works that treat multidimensional

modeling of documents. In Section 3, we give an

overview of our approach for building the schema of

the XML document warehouse. Then, our

multidimensional modeling method is described in

Section 4; while Section 5 shows the functionalities

of the Galaxy-Gen software tool. Finally, in Section

6, we conclude the paper and address future works.

2 RELATED WORKS

Let us remember that the multidimensional

modeling aims to design multidimensional models

that support OLAP (On-line Analytical Processing)

analyses.

In the literature, there are two types of works

addressing the multidimensional modeling of XML

documents: works related data-centric XML

documents (Hümmer and al., 2003) (Boussaid and

al., 2006) (Hachaichi and al., 2010), and others

related to document-centric XML documents. The

remaining of this paper concerns document-centric

XML documents. Firstly we present the most

relevant works related to that area where some

researchers have proposed methods to model the

DocW as a star model. As examples of these works

we can cite (McCabe and al., 2000), (Krouf, 2004),

(Tseng and Chou, 2006) and (Ravat and al., 2007).

Other works such as (Tournier, 2007) and (Pujolle

and al., 2011) propose the galaxy model. Secondly,

we compare the literature works according to a set of

criteria we have specifically established for this

comparison.

Figure 1 presents an example of a star model

designed for the analysis of sales. It is composed of

a central fact called “Sales” composed of two

indicators (i.e., measures) namely Quantity and

Amounts. These measures could be analyzed (i.e.,

aggregated using Sum, Avg…functions) according

to the three axes: Retail_Outlet, Date and Product.

For example, with this star model we can analyze

sales amounts per product and year.

Quantity

Amount

Sales

Product

Date

Retail_Outlet

City

Country

Year

Month

Sub_Category

Category

Id_Date

Sale_Weak

Id_Product

Id_Manager

Manager_Name

Retail_Name

Product_Name

Hiertarchy of

Parameters

Figure 1: An example of Star model for analyzing Sales.

The authors of (McCabe and al., 2000) suggest a

retrieval method in text collections; to do so, they

model the global view of the documents set as a star

model. In their star multidimensional model, they

distinguish five types for the dimension concept

namely: Localization, Time, Term, Document and

Category. The measure concept is the number of

each term occurrences within documents.

In (Krouf, 2004), a process to analyze documents

of the DocW was proposed. This process relies on

this star model. First, the decision maker indicates

the analysis components: fact, dimensions and an

aggregate function. Secondly, a document mart is

generated and instantiated. Finally, the result is

displayed as a multidimensional table. Nevertheless,

during the DocW design phase the determination of

multidimensional elements is manual. Indeed, the

authors do not propose rules or algorithms to

identify fact and dimensions.

As far as, authors of (Tseng and Chou, 2006)

elect the star model in order to analyze documents.

Their star model distinguishes three types of

dimensions: Ordinary dimension containing

keywords extracted from the document, Metadata

dimension which describes the document with title,

author, etc., and Category dimension that contains

keywords external to the document; i.e., issued from

Wordnet. The result star model enables counting the

number of documents according to these

dimensions.

Galaxy-Gen-AToolforBuildingGalaxyModelfromXMLDocuments

The result star model of (McCabe and al, 2000),

(Khrouf, 2004) and (Tseng and Chou, 2006) perform

only quantitative analyses because their measures

are numeric. Moreover, analyses are limited since

the analyses subject (i.e., the fact) is defined a priori,

at the design time of the star model but not at the

query time.

The authors of (Ravat and al., 2007) propose to

revise the constellation modeling for documents;

they suggest adding a new textual measure and two

new dimensions called Structure and

Complementary. In fact, a textual measure can be a

word, a paragraph or a whole document. The

Structure dimension describes the structure of

documents whereas the Complementary dimension is

determined from complementary data sources (e.g.,

data from the curriculum vitae of authors).

Nevertheless, the authors did not propose rules or

algorithms to assist the DocW designer elaborating

the constellation schema: identification of facts,

dimensions, hierarchies...

In (Tournier, 2007) and (Pujolle and al., 2011),

the authors propose a hybrid design process to build

a document warehouse from document-centric XML

documents. Their process combines a top-down

approach (i.e., starting from user requirements) and

a bottom-up approach (i.e., relying on the source

data model). In addition, they suggest a new

multidimensional conceptual model called Galaxy.

This model can be defined as a set of entities, where

each entity is presented like a dimension; several

dimensions could be linked by a node and then are

said compatible dimensions for analyses. However,

the main drawback of this work is that the authors

do not define rules to assist the design phase of a

galaxy model.

In order to summarize and highlight the pros and

cons of the literature approaches, we compare them

in Table 1, among the following set of six criteria:

C1: The approach is specific for XML

document-centric document.

C2: The approach uses constellation model.

C3: The approach uses galaxy model.

C4: The approach determines multidimensional

concepts manually.

C5: The approach determines multidimensional

concepts semi-automatically.

C6: The approach determines multidimensional

concepts automatically.

In this section, we have presented pertinent works

related to multidimensional modeling of documents.

Table 1: Comparison of multidimensional modeling

approaches for documents.

Criterion

Approach

C1 C2 C3 C4 C5 C6

(McCabe and al., 2000)

 

N - - -

(Tseng and Chou, 2006)

 

N - - -

(Krouf, 2004)

 



N N

(Ravat and al., 2007)

 



N N

(Tournier, 2007) & (Pujolle and

al., 2011)



 

N N

 : Criterion supported by the approach. N: Criterion not supported by the

approach. -: Not indicated by the authors.

We have focused on star and galaxy models. We

stress that a star model is characterized by a

predefined subject of analyses (i.e., fact). Whereas,

within a galaxy model the fact is not predefined; it

will be specified when querying the galaxy.

Consequently, a galaxy model is simpler than the

star model because it is based on a unique concept:

Dimension. Furthermore, analyses expressed on a

galaxy model are more flexible than analyses

expressed on a star model. Considering these

benefits, we have elected the galaxy model for

modeling the document warehouse.

The remaining of this paper overviews our

approach for building the schema of the document

warehouse (cf., Section 3) and then our method of

multidimensional modeling of documents (cf.,

Section 4). Experiments and evaluation are subjects

of Section 5.

3 PROPOSED APPROACH FOR

BUILDING XML DOCUMENT

WAREHOUSE

Generally, XML documents are described by

heterogeneous structures even though they belong to

a same domain. Thus, when a decision maker needs

to query these documents, he is constrained to write

several queries (i.e., as many queries as the number

of different structures in the document set). To tone

down this problem, we expect to provide a global

view of the document set. To achieve this global

view, we have proposed in (Feki and al., 2013) an

approach to elaborate the schema of the DocW. This

approach is composed of two methods: Unification

of XML documents structures, and Multidimensional

modeling of documents. Figure 2 depicts this

approach. Here is a short overview of these two

methods as they are required for the readability of

the remaining of this paper.

KEOD2014-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

Unification of XML documents structures. This

method receives as input a set of XML structures

belonging to the same domain and then produces a

limited number of unified trees validated by the

decision makers (Ben Messaoud and al., 2011a)

(Ben Messaoud and al., 2012). It consists of the four

main steps namely: a) Tree representation, b)

Generation of unified trees, c) Approval of unified

trees, and d) Correctness verification of trees.

Firstly, Tree representation translates XML

structures into trees by applying two rules. We

choose the formalism of tree, as adopted in (Lee and

al., 2002) and (Yoo and al., 2005), since it is

graphical and easy to be understood by unskilled

persons.

Secondly, Generation of unified trees step

produces a limited number of unified trees. It treats

both acronym and synonym ambiguities of tree

nodes, referring to a dictionary of acronyms and the

lexical database Wordnet. Then, it computes a

triangular Similarity Matrix (SM) which has n trees

in rows and in columns. It facilitates the

identification of trees to be merged. Each pair of

trees having their similarity factor higher than a

given threshold (experimentally determined) is

merged applying fusion-operators developed in (Ben

Messaoud and al., 2011a) (Fusion by inclusion,

Fusion by union of sub-trees, or Fusion by merging

nodes).

After that, the Approval of unified trees validates

trees according to the analytical requirements of

decision makers. In fact, they can delete and/or

rename nodes. Finally, the Correctness verification

of trees checks the syntactic validity of trees among

a set of four constraints called: Connected nodes,

Hierarchy, Uniqueness of the root node and

Acyclicity (Aouabed and al., 2012).

Note that the input structures and the output unified

trees are saved into the repository shown in Figure

3.a.

Multidimensional modeling. This method accepts

the structure of the input XML documents. It can be

either unified tree resulting from the previous

method (i.e., Unification of XML documents

structures) or XML structure and then produces

galaxy model. The output galaxy model is saved

according to the meta-model of Figure 3.b.

The following sections detail the semi-automatic

multidimensional modeling method, and then

presents our software prototype called Galaxy-Gen

(Galaxy Generation) that supports this method.

4 MULTIDIMENSIONAL

MODELING OF DOCUMENTS

Our method of multidimensional modeling of

documents aims to generate semi-automatically a

multidimensional model for the DocW; among the

existing multidimensional models, we have elected

the Galaxy model (Tournier, 2007). For readability

reasons of the paper, we first introduce the galaxy

and then our proposed method.

The galaxy model can be seen as a network of

entities (i.e., dimensions) connected by nodes (cf.

Figure 10). Each node denotes compatible entities

which could be used together in OLAP analytical

queries. In a galaxy, each entity can play a double

role: an analysis subject (i.e., fact) or an analysis

axis (i.e., dimension). As with the star model

(Golfarelli, 1998), an entity is composed of one or

more attributes hierarchically organized.

To generate such a galaxy model, we proposed in

(Ben Messaoud and al., 2011b) and (Feki and al.,

2013) a semi-automatic method composed of the

four following steps:

 Pretreatment of trees,

 Building galaxy models,

 Galaxy models approval, and

 Correctness verification of galaxy models.

Figure 4 exemplifies the sequencing of these

steps.

4.1 Pretreatment of Trees

This step receives as input a tree that represents

either the structure of a set of XML documents or

the unified tree resulting from the unification of

XML documents and then produces a pretreated

tree. In fact, a pretreated tree has cardinalities; they

are added by exploring XML documents compliant

to the input structure(s).

4.2 Building Galaxy Models

The building galaxy step translates each pretreated

tree into a galaxy model by applying a set of ten

rules detailed in (Ben Messaoud and al., 2011b). It

consists of the two sub-steps: Identification of

dimensions (i.e., entities) and galaxy-nodes, and

Identification of hierarchies.

Identification of dimensions and galaxy-nodes.

This identification applies three rules to determine

dimensions, and one rule for nodes. For clarity

Galaxy-Gen-AToolforBuildingGalaxyModelfromXMLDocuments

Structures of

XML documents

Struct 1

Struct 2

Struct n

Unificationof

XMLdocuments

structures

Document

Warehouse

Unified tree(s) Galaxy model(s)

Decisionalquery

Repository

xxxxxxxx

Multidimensional

modeling

Figure 2: Approach for building the schema of the XML Document Warehouse.

Tree

+Tree_Name

Non_Unified_Tree Unified_Tree

Structure

+Structure_Name

+Structure_Type

Doc_XML

+Doc_Name

1..*

No d e

+Node_Name

+Synonymous_Node

+Cardinality_Node

+Node_Name_Disam

1..*1..*

Tree_Node

+Node_Order

+Node_Level

+Parent

+Child

1..*

+Unified

+Non_Unified

2..2

Dime nsion

+Dimension_Name

Hierarchy

+Hierarchy_Name

Parameter

+Parameter_Name

Weak_Attribute

+Attribute_Name

Galaxy_Node

+Galaxy_Node_Name

Galaxy_Schema

+Galaxy_Name

Hier_Param

+Rank

1..*

0..*

10..1

0..1

(a) (b)

Figure 3: Meta-model of the Document Warehouse.

Pretreatment

of trees

Buildingof

galaxymodels

Galaxymodels

approval

Correctness

verificationofgalaxy

models?

Repository galaxy

models

Yes

Stop

Unified trees or

XMLstructures

Pretreated trees

Generated galaxy models

Validated galaxy models

Figure 4: Galaxy model modeling steps.

KEOD2014-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

reasons, we give these rules (cf. (Ben Messaoud and

al., 2011b) for further details).

Dimensions identification rules

Rd1: The root node r of a pretreated tree constitutes

a dimension called D-r.

Rd2: Every pair of non terminal nodes M and N

related through an arc M-N with cardinality (+ or *)-

(+ or *) transforms into two dimensions called: D-M

and D-N.

Rd3: Nodes having the same parent node and

describing a date (e.g., day, month, and year)

denotes the existence of a temporal dimension called

D-Date in the resulted model. These nodes are the

dimension’s parameters.

Galaxy-nodes identification rule

Rn1: Each pair of nodes M and N identified as

dimensions and related by an arc M-N in the

pretreated tree constitutes two dimensions connected

via a node (two compatibles dimensions).

Hierarchy identification. In a multidimensional

model, dimensions are composed of attributes; some

of them are organized into hierarchies that represent

analyses perspectives (Ravat and Teste, 2000).

Hierarchical attributes are said parameters. Within a

dimensional hierarchy, the lowest granularity is the

identifier of the dimension.

In our work, this identifier is a surrogate key

(artificial attribute which values are sequentially

generated). Parameters beyond the identifier are

extracted using four rules (Rp1, Rp2, Rp3 and Rp4).

Sometimes, parameters can be associated with

descriptive data called weak attribute (as the author

name for the author Id). We extract such attributes

using two rules (Rw1 and Rw2).

Parameters identification rules

Rp1: Every terminal node N linked to a parent node

M identified as a dimension where the arc M-N is

not annotated with cardinalities 1-1, transforms into

a parameter P-N at level 2.

Rp2: Each terminal node N linked to a parent node

M identified as a parameter at level i

where the arc

M-N is not annotated with the cardinalities 1-1,

represents a parameter P-N at level i-1.

Rp3: Every non terminal node N linked to a parent

node M identified as a parameter at a level i and

related by an arc M-N annotated with the

cardinalities 1-(+ or *), transforms a parameter P-N

at level i-1.

Rp4: Each non terminal node N linked to a parent

node M identified as a dimension and related by an

arc M-N not annotated with the cardinalities (+ or

*)-(+ or *), transforms into a terminal parameter P-

Weak attributes determination rules

Rw1: Each terminal node N having its parent node M

identified as a dimension D and the arc M-N is

annotated with the cardinalities (1 or 0)-1,

transforms into a weak attributes W-N for the

identifier of D.

Rw2: Each terminal node N having its parent node M

identified as a parameter P and the arc M-N is

annotated with the cardinalities (1 or 0)-1,

transforms into a weak attributes W-N for P.

4.3 Galaxy Models Approval

The galaxy model approval step displays models

issued from the previous step to the decision maker

for agreement. In fact, they adjust models according

to their analytical requirements. This adjustment

consists in deleting and/or renaming

multidimensional elements (i.e., dimension,

parameter). All these changes are saved in the

repository of Figure 3.b to be used later in querying

the galaxy.

4.4 Correctness Verification of

Galaxies

Galaxy models should be syntactically checked, for

this purpose we define a set of constraints. Eight

constraints are adapted from those defined for the

star model in the literature. In addition, we define

three specific constraints for the galaxy (Feki and

al., 2013). We classify all these model constraints

into three classes according to whether they apply on

dimensions, nodes or hierarchies.

Dimension constraints.

Cd1: Identification constraint: Every dimension

must have an identifier. It may be either a key

extracted from the data source or a surrogate key

(Hurtado and Mendelzon, 2002) (Carpani and

Ruggia, 2001).

Cd2: Non empty dimension: Each dimension should

have at least one hierarchy (Ben Abdallah and al.,

2008).

Cd3: Non isolated dimension: In a galaxy model,

every dimension has to be associated with n (n ≥ 1)

nodes.

Node constraints.

Cn1: Non isolated node: Each node must connect at

least to two different dimensions. This enables to

perform multidimensional analyses on n (n≥2) axes.

Galaxy-Gen-AToolforBuildingGalaxyModelfromXMLDocuments

Cn2: Disjunction of nodes: In a galaxy model, nodes

are not directly linked; the only links between nodes

are indirect via dimensions.

Hierarchy constraints.

Ch1: Hierarchical root: All hierarchies of a

dimension D begin from the identifier of D (Ben

Abdallah and al., 2008).

Ch2: Exclusive hierarchies: Any dimension having

the minimal hierarchy (A minimal hierarchy h is

restricted to two parameters: the identifier of the

dimension of h directly linked to All.) must not have

other hierarchies (Ben Abdallah and al., 2008).

Ch3: Non isolated attribute: Within a dimension D,

each attribute must belong to at least one hierarchy

of D (Ben Abdallah and al., 2008).

Ch4: Non empty Hierarchy: In a dimension D, a

hierarchy must contain at least two parameters: the

identifier of D and the All parameter (Ben Abdallah

and al., 2008).

Ch5: Rollup: All the parameters of a hierarchy,

excepting the All parameter, have at least a parent

(Hurtado and Mendelzon, 2002).

Ch6: Acyclicity: Each parameter, excepting the

parameter All, cannot be parent and child of the

same parameter by transitivity (Hurtado and

Mendelzon, 2002), (Ghozzi and al., 2003).

Note that among the extracted attributes for a

dimension, we may rarely find attributes describing

the structure and others relative to the metadata of

documents. To solve this problem, we split the set of

attributes into two dimensions. This split relies on

the usage of the Dublin Core Metadata Initiative

(Dublin Core Metadata Initiative:

www.dublincore.org.) which facilitates the

identification of the metadata attributes.

5 EXPERIMENTS AND

EVALUATION

In order to substantiate our method, we have

implemented a software tool named Galaxy-Gen for

the generation of multidimensional galaxy model.

This generation applies a set of rules (cf., Section 4).

It receives as input an XML structure or a unified

tree resulting from the unification of a set of DTD

and/or XSD belonging to the same domain and then

produces a multidimensional Galaxy model.

We have carried out two experiments: one on

academic documents and other using medical

documents.

5.1 Experiment on Academic

Collection

This first experiment is applied to a set of twenty

XML documents taken from the academic domain

and compliant to four complex DTDs (cf. Figure 5).

<?xml version="1.0" encoding="UTF-8"?>

<!ELEMENT Auth ((Name, Affiliation))>

<!ELEMENT Title (#PCDATA)>

<!ELEMENT Subsection ((Para))>

<!ELEMENT Section ((Title?, Subsection+))>

<!ELEMENT Para (#PCDATA)>

<!ELEMENT Name (#PCDATA)>

<!ELEMENT Month (#PCDATA)>

<!ELEMENT Day (#PCDATA)>

<!ELEMENT Article ((Title, Auth+,

Section+, Day, Month))>

<!ELEMENT Affiliation (#PCDATA)>

<?xml version="1.0" encoding="UTF-8"?>

<!ELEMENT Year (#PCDATA)>

<!ELEMENT Title (#PCDATA)>

<!ELEMENT Section ((Paragraph+))>

<!ELEMENT References (#PCDATA)>

<!ELEMENT Paragraph (#PCDATA)>

<!ELEMENT Name (#PCDATA)>

<!ELEMENT Month (#PCDATA)>

<!ELEMENT Institute (#PCDATA)>

<!ELEMENT Day (#PCDATA)>

<!ELEMENT Body ((Section+))>

<!ELEMENT Writer ((Name, Institute))>

<!ELEMENT Article ((Title, Writer+, Body,

References+, Day, Month, Year))>

<?xml version="1.0" encoding="UTF-8"?>

<!ELEMENT University (#PCDATA)>

<!ELEMENT Year (#PCDATA)>

<!ELEMENT Title (#PCDATA)>

<!ELEMENT Subsection_Number (#PCDATA)>

<!ELEMENT Subsection (Subsection_Number,

Title, Paragraph+, Fig.*, Table*)>

<!ELEMENT Section_Number (#PCDATA)>

<!ELEMENT Section (Section_Number, Title,

Paragraph+, Fig.*, Table*, Subsection*)>

<!ELEMENT Paragraph (#PCDATA)>

<!ELEMENT Outline (#PCDATA)>

<!ELEMENT Name (#PCDATA)>

<!ELEMENT Fig. (#PCDATA)>

<!ELEMENT Table (#PCDATA)>

<!ELEMENT Writer ((Name, University))>

<!ELEMENT Article ((Title, Writer+,

Outline, Section+, Year))>

<?xml version="1.0" encoding="UTF-8"?>

<!ELEMENT Tit (#PCDATA)>

<!ELEMENT Name (#PCDATA)>

<!ELEMENT Body (#PCDATA)>

<!ELEMENT Writer ((Name, Affiliation))>

<!ELEMENT Article ((Tit, Writer+,

Abstract, Body))>

<!ELEMENT Affiliation (#PCDATA)>

<!ELEMENT Abstract (#PCDATA)>

Figure 5: Four DTDs from the academic domain.

KEOD2014-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

In the remaining of this section, we present the

features of our software tool Galaxy-Gen while

exemplifying them through the galaxy-model

generated for this first experiment.

Since the input XML documents are described by

heterogeneous structures, we invoke our USD tool

(Unification of Structures of

XML Documents)

(Aoubed and al., 2012) in order to generate the

unified tree for the four DTDs. After that, the galaxy

generation process starts with picking the unified

tree(s) for which the user wants to get a Galaxy

model. Secondly the pretreatment step is launched to

produce the pretreated tree shown in Figure 6. In this

tree, cardinalities are automatically added for each

parent node (except for the root), one cardinality for

each outgoing edge. These cardinalities are

determined by exploring the twenty input XML

documents conform to the four DTDs of the running

example in this experiment. We note that the

structure of this pretreated tree and the structure of

the unified tree are identical.

Thirdly, dimensions of the galaxy are extracted

by applying three rules (cf., Section 4). Thus, four

dimensions are extracted: D-Article, D-References,

D-Writer and D-Date. The dimension D-Article is

identified by applying rules Rd1 and Rd2; whereas

D-References and D-Writer are extracted using only

Rd2. D-Date is identified by applying rule Rd3.

Among these dimensions, we assume that the

decision maker has selected three ones (D-Article,

D-Writer and D-Date) meaning that (s)he is

interested in these analyses axes. Figure 7 illustrates

the extracted dimensions.

This extraction of galaxy dimensions is followed

by the extraction of galaxy nodes. Indeed,

compatibles dimensions are linked via a node. In our

running example, there is only one node connecting

the three selected dimensions (cf., Figure 8).

After that, hierarchies of the selected dimensions

are determined. In fact, their parameters and weak

attributes are extracted by applying rules defined in

Section 4. Figure 9 shows the three hierarchies

called H_Writer_1, H_Writer_2 and H_Writer_3 of

the D-Writer dimension. The hierarchy H_Writer_1

has two parameters Id_D_Writer and P_Affiliation.

The identifier of the dimension (i.e., Id_D_Writer)

has one weak attribute WA_Name. We assume that

the decision maker is not interested with the name of

the author; thus, (s)he has not selected this attribute.

Finally, when the decision-maker checks

hierarchies with their parameters and weak

attributes, the galaxy model is automatically

produced. Figure 10 illustrates the obtained galaxy

for our running example.

Compared to the galaxy model built manually on

these documents, the generated galaxy model has

one dimension less; whereas hierarchies are almost

the same.

5.2 Experiment on Medical Documents

In order to better assess the Galaxy-Gen tool, we

have conducted a second experiment; it is performed

on a set of 1691 XML documents taken from the

medical collection Clef-2007. However, there were

some inadequacies in these documents: for example,

all keywords are gathered inside a unique textual tag.

In order to alleviate this difficulty we have improved

the DTDs of these documents (by adding the +

Legendofadded cardinalities:1:Anelementcanberepeatedonce.+:Anelementcanberepeated(cardinality>=1).

*:Anoptionalelementcanberepeated(cardinality>=0).

Figure 6: Pretreated tree with added cardinalities.

Galaxy-Gen-AToolforBuildingGalaxyModelfromXMLDocuments

SelectedDimensions

Dimensionsidentified

byapplyingthe three

proposedrules

Rulesusedfor

dimensiongeneration

Figure 7: Galaxy-Gen interface for selecting identified dimensions.

Identifiednode(s)

Compatiblesdimensions

fortheselectednode

Figure 8: Galaxy-Gen interface for selecting compatible nodes.

Selecteddimensions Generatedhierarchies

Identifiedparametersforthe

currentlydisplayedhierarchy

Wea kattributeIdentifiedforthe

currentdisplayedparameter

Figure 9: Galaxy-Gen interface for selecting hierarchies.

cardinality to some elements) to obtain more

accurate XML documents. Note these documents are

described by three DTDs we have generated using

XMLSpy; since these DTDs are long we do not

include them in this paper.

These DTDs have some elements in common, and

some different ones. They have the same root

element linked to a set of elements that differs

according to the DTD.

After processing these DTDs with the Galaxy-

Gen we obtained a galaxy model composed of the

following five dimensions: D_Casimage_Case,

D_Author, D_References, D_Keywords and

D_Reviewer.

In fact, for this second experiment, the galaxy

model issued from the prototype is identical to the

one built manually. This represents encouraging

results.

KEOD2014-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

Figure 10: Result galaxy model for the first experiment.

Figure 11: Result galaxy model for the second experiment.

5 CONCLUSION

Documents represent an important source for

decisional analyses. They merit to be integrated in

the decision support system. In this paper, our main

interest was to build the schema of the document

warehouse. More specifically, we gave a detailed

overview of the semi-automated method for the

construction of the multidimensional model for a

document warehouse. We have elected the Galaxy

model to represent this multidimensional model.

Likewise, we have presented a software tool, called

Galaxy-Gen that implements the method for the

generation of galaxy models.

Furthermore, we have conducted two experiments

using the Galaxy-Gen tool; they are to evaluate our

proposals. The first experiment is applied on a set of

XML documents taken from the academic domain.

Galaxy-Gen-AToolforBuildingGalaxyModelfromXMLDocuments

It produces a galaxy model composed of four

dimensions. Whereas, the second experiment is

performed on XML documents taken from the

collection Clef-2007. For this experiment, we

obtained a galaxy with five dimensions.

As a future work, we expect evaluate Galaxy-Gen

software tool on more XML structures. Also, we are

in the step of finishing the definition of a set of

analytical operations dedicated to the galaxy model,

and we aim implementing a query language for the

galaxy based on these operations.

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