CWM Extensions for Knowledge and Metadata Integration for Complex

Data Warehouse and Big Data

Ralaivao Jean Christian, Razaﬁndraibe Fabrice, Raherinirina Angelo and Rakotonirainy Hasina

University of Fianarantsoa, Madagascar

Keywords:

Data Warehouse, Description Logics, Integration, Knowledge, Mapping, Metadata, ODM, Ontology,

OWL DL, Transformation, UOP.

Abstract:

This document constitutes a continuation of the work carried out in the ﬁeld of complex data warehouses

(DW) relating to the management and formalization of knowledge and metadata. It proposes a methodolog-

ical approach to integrate two concepts, knowledge and metadata, within the framework of a complex DW

architecture. The objective of the work considers the use of the knowledge representation technique by de-

scription logic and the extension of the Common Warehouse Metamodel (CWM) speciﬁcations. Several es-

sential aspects of this work are expected, including the representation of knowledge in description logics and

the declination of this knowledge into coherent UML diagrams while respecting or extending CWM speciﬁ-

cations and using XML as a pivot, in particular OWL DL. Furthermore, the coupling between UML Ontology

Proﬁle (UOP) and the Ontology Deﬁnition Metamodel (ODM), for semantic modeling, integration of ontolo-

gies or enrichment of metadata, will be operationalized by transformation of models or by mapping or both

simultaneously. As a result, a new extension of CWM metamodel will be developed. This will have perfor-

mance consequences for a complex DW. The ﬁeld of application is vast but will be adapted to systems with

heterogeneous, complex and unstructured content and requiring a large (re)use of knowledge such as medical

data warehouses.

1 INTRODUCTION

We have proposed an architectural framework for

a complex data warehouse (Darmont et al., 2005).

As Figure 1 illustrates, an important element of this

framework is the implementation of domain knowl-

edge and metadata in the three phases of warehous-

ing: ETL (Extract - Transform - Load)/Integration,

Administration/Monitoring and Analysis/Usage.

Note that the proposed architecture conforms to

the CWM (Common Warehouse Metamodel) recom-

mended by the Object Management Group (OMG).

Based on this architecture, we conducted another

work on the integration of knowledge and metadata

for complex data warehouses. Thus, in (Ralaivao and

Darmont, 2007), we discussed the different possibili-

ties of integration and explored the integration of do-

main knowledge in the form of metadata.

The two other possibilities mentioned in (Ralaivao

and Darmont, 2007) remain to be explored, namely:

• integration of metadata in the form of knowledge,

in this case, our approach will take us to the ﬁeld

of knowledge-based warehouses ;

• separate knowledge and metadata management.

The architecture we have proposed is the basis of

X-WaCoDa, an XML-based approach for online stor-

age and analysis of complex data (Mahboubi et al.,

2009). Three trends in XML-based DW (XML Web

Warehouses (Vrdoljak et al., 2003; L., 2001; Gol-

farelli et al., 2003), XML Document Warehouses

(Nassis et al., 2005; Rajugan et al., 2005; Baril and

Bellahs

ene, 2003; Zhang et al., 2005), XML Data

Warehouses (Pokorny, 2006; Hummer et al., 2003;

Rusu et al., 2005; Park et al., 2005; Boussa

ıd et al.,

2006)) were united to acquire a reference model that

synthesizes all the work related to this ﬁeld.

And on the other hand, the community work-

ing in the ﬁeld of data and knowledge manage-

ment is faced with new approaches such as Big

Data and Data Lake (Sawadogo and Darmont, 2020;

Sawadogo et al., 2019) whose content is hetero-

geneous, complex, weakly structured or even un-

structured, non-standardized and inconsistent (Sakr

and GaberSakr, 2014), adding to these problems

the inexistence of methods and/or tools for integrat-

ing knowledge and metadata . This will necessar-

ily lead to a review of the architecture and tech-

niques around warehousing (ETL/Integration, Ad-

ministration/Monitoring, Usage/Reporting) and infer-

Christian, R., Fabrice, R., Angelo, R. and Hasina, R.

CWM Extensions for Knowledge and Metadata Integration for Complex Data Warehouse and Big Data.

DOI: 10.5220/0012689300003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 329-336

ISBN: 978-989-758-692-7; ISSN: 2184-4992

329

Figure 1: Architecture framework for a complex data warehouse.

ence (Wender, 2017) and especially for the discovery

and management of knowledge (Bornschlegl et al.,

2016).

We therefore propose to consider and deepen the

two axes mentioned above while considering these

new approaches.

2 MAIN QUESTION AND

HYPOTHESIS

A high-performance complex data warehouse (DW)

will integrate metadata of different forms, from its

conception to its exploitation through its implemen-

tation and its administration. But knowledge of the

ﬁeld is also very important for more performance and

ﬂexibility in the various phases of storage. For exam-

ple, in a complex DW for personalized anticipation

medicine (MAP) (Darmont, 2008) the data can come

from different operational data sources (ODS) (Bio-

logical, cardiovascular, biometric and psychological

stores) and can be varied and heterogeneous. There-

fore, data exchanges must be standardized by the use

of concepts (metadata) and innovative XML technolo-

gies as a pivot language.

Would it be interesting to manage metadata and

knowledge jointly vs separately? Would the inte-

gration of knowledge and metadata have an impact

on traditional data warehouse architectures and what

about the standard that prevails like the CWM of

OMG? How to consider the main knowledge mainly

formalized by ontology? How CWM will be adapted

with the ODM standard?

3 STATE OF THE ART AND

RELATED WORKS

(Inmon, 2005) in its deﬁnition of the data warehouse

uses the terms: (1) Subject orientation: constituting

an organization of data according to the domain (pro-

duction, sale, transaction or activity, . . . ), (2) Integra-

tion: structuring phase (Kimball and Ross, 2013), (3)

Historization: time axis, (4) Non volatility: conserva-

tion principle, (5) Aggregation : availability and ac-

cessibility by queries, OLAP, data mining, . . . These

terms constitute the main function of a data ware-

house.

The DW border breaks free of conventional frame-

works by expanding and integrating with the exploita-

tion of Big Data. (Ngo et al., 2019) proposed an agri-

cultural DW architecture for a business intelligence

operation. The use of common solutions such as

MongoDB, Cassandra or CONSUS DW demonstrates

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

330

the usefulness of DW in the context of Big Data.

However, there are more dissimilarities than sim-

ilarities between Big Data and complex DW. The ex-

ploitation of Big Data in a DW requires the use of

pivot language of which the most answered is XML.

XML documents allow not only to facilitate the

standardization of complex data (Darmont et al.,

2005) before the loading phase in the DW, but also

to constitute the storage core (XML-Based). In this

context, research on XML-based storage has been

steadily growing.

These different approaches respect the standard

imposed by the CWM (Poole et al., 2003) meta-

model of the OMG. Most DWs are based on meta-

data. The complexity of the data, however, dictated

by the heterogeneity of the sources and the absence

of data structures often makes it difﬁcult to integrate

them into a DW. The question of performance (Baril

and Bellahs

ene, 2003) takes on a primordial mean-

ing in all phases of storage. In addition, one of the

purposes of storage is the extraction of knowledge to

facilitate interpretation and decision-making. One of

the main questions is: ”Will integrating knowledge in

addition to metadata improve performance?”. It is in

this context and following our latest work (Darmont

et al., 2005; Ralaivao and Darmont, 2007; Mahboubi

et al., 2009) that (Liao et al., 2010) integrates meta-

data and/or ontologies in the ﬁeld of semantic Web

and (Wu and Hakansson, 2010) uses the Knowledge-

based system for integrating metadata into warehous-

ing.

The work of (Srinivasan, 2016) presents an archi-

tecture for a business intelligence (BI) which takes

into account at the same time the concepts and tech-

niques stemming from artiﬁcial intelligence, machine

and deep learning and excavations in the advent of

Big Data.

The proposed methods take into account three as-

pects (solutions) of this integration of knowledge and

metadata in a complex DW.

4 METHODS

In (Ralaivao and Darmont, 2007), we were able to in-

ventory and classify the metadata as well as to con-

sider the types of knowledge in order to commonly in-

tegrate them in complex ED. At the end of these ﬁrst

works, the transformation of knowledge in the form

of metadata (K → MD) is convincing for the domain

(Liao et al., 2010; Wu and Hakansson, 2010).

The consideration of ontologies in a DW is essen-

tial for the revision of knowledge (interpretation, in-

tegration, formulation, . . . ). A second method there-

fore proposes the transformation of metadata in the

form of knowledge MD → K. Thus, we will obtain

a Knowledge-based Warehouse DW (Nemati et al.,

2002).

The two methods will lead us to another perspec-

tive which is the hybridization of metadata and knowl-

edge (MD ⇄ K) to manage DW. It is based on the

theoretical fact that one can transform metadata into

knowledge and vice versa. The iterative and/or re-

cursive execution of this transformation constitutes

learning at the level of a complex DW.

5 RESEARCH EXPECTATIONS

In addition to the results obtained in the work carried

out so far, this work will reinforce the achievements

in terms of complex DW architecture and will provide

more details on the possibilities of integrating knowl-

edge and metadata. Adding to this we are working on

the following integration aspects:

1. the representation of knowledge (Pan, 2020) in

description logics (DLs) for a semantic and ax-

iomatic formalization of knowledge of the do-

main,

2. the mapping of DLs in UML (Dutra, 2002) which

will allow use in model-driven engineering and an

implementation based on UML diagrams,

3. integration of the UML diagram corresponding

to the DLs (De Giacomo, 2010) according to the

CWM speciﬁcations in order to respect the stan-

dards imposed by the OMG in the implementation

of DW in general.

5.1 Knowledge Representation in DLs

DLs constitute a formalized set of knowledge repre-

sentation languages (De Giacomo, 2010; Pan, 2020).

Based on two fundamental formal frameworks: the

TBox

and the ABox

, where the DLs formalize a

knowledge base K as 1) description language and 2)

inference services.

uller et al., 2011) for example offers an exten-

sion of DLs for an integrated framework for repre-

senting knowledge in different aspects.

We advance the hypothesis that DLs can be man-

aged or formalized by metadata. So we will have the

transformation K → DL → MD and vice versa.

Set of terminological axioms

Set of assertive axioms

CWM Extensions for Knowledge and Metadata Integration for Complex Data Warehouse and Big Data

331

Figure 2: CWM extensions.

5.2 DLs Mapping/Tranformation in

UML Diagram and Vice Versa

This technique would consist of DLs translation in

UML diagram (De Giacomo, 2010; Dutra, 2002).

However it is essential to consider inference services

as for the UML diagrams semantics (Le Duc, 2008) in

order to 1) check the consistency of a class or a class

diagram, 2) check the subsumption and the equiva-

lence between classes and 3) explain the logical con-

sequences of the properties.

This would allow an opening or an application of

the different existing approaches of knowledge engi-

neering (De Giacomo, 2010).

DL offers the possibility of writing the ontology in

OWL or the knowledge base where a concept of DL is

a class and a role of DL is a property in OWL. Since

RDF/XML is the normative syntax for exchanging in-

formation between systems, the OWL DL solution

based on RDF(S) offers maximum expressiveness re-

garding the SH OI N description logic (Baader et al.,

2017; Lutz et al., 2019). The latter supports data val-

ues, data types and their properties.

5.3 UML Diagram Integration in CWM

Speciﬁcation

From a standardization point of view, the CWM spec-

iﬁcations and their adaptations are crucial in the ﬁeld

of complex DW where the CWM is the champion of

standardization. This integration will take as a ref-

erence the CWM modeling and speciﬁcation levels,

namely the model, meta-model, meta-meta-model

and the Meta Object Facility (MOF) (Poole et al.,

2003).

This integration will facilitate the supply of the

warehouse, contribute to its administration mainly on

the performance aspect, guide users in its operation

and ﬁnally it will offer the opportunity to re-inject

the knowledge extracted from the analysis and exca-

vations in the warehouse itself.

This work will also propose extensions and/or

possibly grafts to the speciﬁcations of CWM meta-

models, while ensuring bottom-up compatibility and

eventually propose speciﬁc adaptations to Big Data.

Above the ﬁve functional layers of CWM will be

added a layer for knowledge management.

6 RESULTS

The contributions of this paper are as follows :

1. The CWM extensions with a new layer and somes

packages ;

2. OWL DL transformation and/or mapping with

UML ;

3. Plugins of package Core with a new ODM speci-

ﬁcation.

6.1 CWM Extensions

Many works to extend CWM (Soler et al., 2008b;

Demraoui et al., 2016) have been carried out but none

proposed to extend CWM to a new layer in order to

manage knowledge and metadata. This constitutes an

important advance in the ﬁeld of complex data ware-

houses, knowledge integration and its reinjection.

The extension examples proposed by (Demraoui

et al., 2016) are available in the works of (Zhao and

Huang, ; Soler et al., 2008a; Gomes et al., 2007; Mi-

douni et al., 2009; da Silva et al., 2010; Tavac and

Tavac, 2013) and (Thavornun, 2015).

The work of (Thavornun, 2015) is closest to this

ﬁeld of research and advances the following prob-

lems:

1. CWM mainly focuses on data warehouse meta-

data ;

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

332

2. CWM does not cover automated knowledge dis-

covery and management system.

The addition of a new layer Knowledge, on the

shaded box with some integrated packages (MD

& KB mapping, Ontology, Acquired and explicite

Knowledge, DL) and used packages (XML, RDF(S),

OWL DL, Core v2), Figure 2, will solve these prob-

lems although it will increase the number of meta-

models to consider. It will integrate the simultaneous

management of metadata and knowledge. These will

arise from OWL DL ontologies, based on the ODM

metamodel, a formal and expressive representation of

description logic.

The knowledge layer, however, uses XML meta-

models from the Resource layer for the OWL DL for-

malization and Core from the Object layer for basic

knowledge operations. As a result, a new version of

the Core package named Core v2 in CWM extension,

including a plugin of it, will be offered subsequently.

The transformation and/or mapping of OWL DL

into UML, based on the UOP metamodel, and vice

versa makes it possible to understand the knowledge

in the CWM repository and is proposed in the subsec-

tion 6.2.

6.2 OWL DL Transformation and/or

Mapping with UML

Figure 3 shows two parallel conceptual Modeling

Spaces (MS)

at the Metamodel level of the OMG

MOF architecture, namely the UOP and the ODM, the

respective metamodels of the UML models and the

OWL DL ontologies. The latter, located at layer M1,

model, in parallel, the real world, using modeling lan-

guages, in this present research, UML, OWL or other

language, deﬁned using different meta-metaconcepts.

RDF(S) as well as three different dialects of OWL,

namely OWL Full, OWL DL and OWL Lite, are ex-

amples of languages in the M2 layer. OWL DL is

most suitable in our complex data warehouse context

because it promotes maximum expressiveness while

maintaining the completeness and decidability of cal-

culations.

At the conceptual level, we establish a transforma-

tion from UML to ODM and vice versa at the meta-

modeling level. An example of a transformation mod-

eling language for such purposes in MOF is Query-

View-Transformation (QVT) (Gardner and Grifﬁn,

2003; Kruse, 2015; OMG, 2016) or the Atlas Trans-

formation Language (ATL) (Kruse, 2015). And the

A modeling space (MS) is a modeling architecture

based on a particular (meta)metamodel

mapping proposed by (Brockmans et al., 2006) com-

plies with the transformation rules. Figure 4 repre-

sents a model for transforming UML into OWL DL

and vice versa. Two packages formalize the ”QVT

transformation” from UML to OWL and vice versa

and the ”OWL (de)serialization”.

Figure 3: Ontology transformation and/or mapping with

UML.

The metamodel proposed in Figure 6 constitutes

an isomorphic correspondence, for mapping, with

OWL DL using UML concepts.

We propose a QVT transformation model as pro-

posed by (Haasjes, ).

Figure 4: QVT transformation of UML and OWL and

(de)serialization of OWL.

6.3 Core Extension

The AnnotedElement class, from the Knowledge

layer, is extended to the ModelElement class of the

Core class (Figure 5). This extension will not only en-

sure ontology integration (plugin of the model in Fig-

ure 5), but also compatibility with CWM while leav-

ing all of CWM’s functionalities intact.

The extension in question concerns ODM and is

CWM Extensions for Knowledge and Metadata Integration for Complex Data Warehouse and Big Data

333

Figure 5: Extension of the ModelElement class of the Core metamodel by the AnnotedElement class.

Figure 6: Elements structuring the ODM metamodel.

represented by the Figure 6. It offers all the integra-

tions of the normative syntax between OWL DL and

DL (Obitko et al., 2004; Baader et al., 2017; Lutz

et al., 2019).

OWL DL axioms and facts (Obitko

et al., 2004; Baader et al., 2017; Lutz

et al., 2019) about classes such as

EnumerateClass(A o

. . . o

), SubClassOf(C

)

(Subsumption), EquivalentClasses(C

. . . C

)

(Equivalent Classes), DisjointClassses(C

. . . C

)

(Disjoint Classes) and DataType(D); on properties

DataTypeProperty(range, Symmetric, . . . , Transitive)

(Data type properties) SubPropertyOf(U 1U2)

(Subsumption on properties),

EquivalentProperties(U

. . . U

) (Equivalent prop-

erties); on annotations, AnnotationProperty(S)

or on individuals, SameIndividual(o

. . . o

) and

DifferentIndividual(o

. . . o

) ; are easily repre-

sented by the model in Figure 6.

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

334

7 CONCLUSION

We recall that the issues to which the work is based

were translated such as:

• the difﬁculty of integrating metadata and even

more knowledge into a complex ED;

• the increasing heterogeneity and management of

sources and content;

• the performance of existing complex and hetero-

geneous data management solutions;

• the lack of methods and/or tools for integrating

knowledge and metadata.

The integration approach by different representa-

tion approaches – DLs, Mapping, Transformation and

CWM speciﬁcations – should ensure the relevance of

the proposed architectural framework as well as its

implementation.

The tools that will be developed for experimenta-

tion in this research work will be generalized and will

be made available to the scientiﬁc community work-

ing in the ﬁeld of complex DW and Big Data. Such

tools should be used to verify the proposed ideas.

REFERENCES

(2016). MOF Query/View/Transformation, version 1.3,

https://www.omg.org/spec/QVT/1.3/About-QVT/.

Baader, F., Horrocks, I., Lutz, C., and Sattler, U. (2017). An

introduction to description logic.

Baril, X. and Bellahs

ene, Z. (2003). Designing and Man-

aging an XML Warehouse. XML Data Management:

Native XML and XML-Enabled Database System.

Addison-Wesley.

Bornschlegl, M. X., Engel, F. C., Bond, R., and Hemmje,

M. L. (2016). Advanced Visual Interfaces: Supporting

Big Data Applications. Springer, Information Systems

and Applications, gerhard goos, juris hartmanis, and

jan van leeuwen edition. Lecture Notes in Computer

Science.

Boussa

ıd, O., BenMessouad, R., Choquet, R., and An-

thoard, S. (2006). X-warehousing: An xml-based

approach for warehousing complex data. 10th East-

European Conference on Advances in Databases

and Information System (ADBIS’06), Thessaloniki,

Greece, Vol. 4152 of Lecture Notes in Computer Sci-

ence. Springer:39–54.

Brockmans, S., Haase, P., Hitzler, P., and Studer, R. (2006).

A metamodel and uml proﬁle for rule-extended owl

dl ontologies. In Sure, Y. and Domingue, J., editors,

The Semantic Web: Research and Applications, pages

303–316. Springer Berlin Heidelberg.

da Silva, J., de Oliveira, A. G., do Nascimento Fidalgo, R.,

Salgado, A. C., and Times, V. C. (2010). Modelling

and querying geographical data warehouses. 35:592–

614.

Darmont, J. (2008). Entreposage de donn

ees complexes

pour la m

edecine d’anticipation personnalis

ee. IC-

SSHC.

Darmont, J., Boussa

ıd, O., Ralaivao, J. C., and Aouiche, K.

(2005). An architecture framework for complex data

warehouses. 7th International Conference on Enter-

prise Information Systems (ICEIS’05), Miami, USA.

INSTICC:370–373.

De Giacomo, G. (2010). Description logics for conceptual

data modeling in uml. Technical report, Dipartimento

di Informatica e Sistemistica, SAPIENZA Universit

di Roma.

Demraoui, L., Behja, H., Zemmouri, E. M., and Ben Ab-

bou, R. (2016). A viewpoint based extension of the

common warehouse metamodel to support the user’s

viewpoint approach. 11:137.

Dutra, M. (2002). Using uml for knowledge representation.

Technical report, Sandpiper Software, Inc.

Gardner, T. and Grifﬁn, C. (2003). A review of omg mof

2 . 0 query / views / transformations submissions and

recommendations towards the ﬁnal standard.

Golfarelli, M., Rizzi, S., and Vrdoljak, B. (2003). Data

warehouse design from xml sources. 4th Interna-

tional Workshop on Data Warehousing and OLAP

(DOLAP’01), Atlanta, USA, ACM Press:40–47.

Gomes, P., Farinha, J. T., and Trigueiros, M. J. (2007). A

data quality metamodel extension to cwm. In Asia-

Paciﬁc Conference on Conceptual Modelling.

Haasjes, R. Metamodel transformations between uml and

owl.

Hummer, W., Bauer, A., and Harde, G. (2003). Xcube: Xml

for data warehouse. 6th International Workshop on

Data Warehousing and OLAP (DOLAP’03), New Or-

leans, USA, ACM:33–40.

Inmon, W. H. (2005). Building the Data Warehouse, vol-

ume 4. Wiley Publishing Inc., robert elliot edition.

Kimball, R. and Ross, M. (2013). The Data Warehouse

Toolkit: The Deﬁnitive Guide to Dimensional Mod-

eling, Third Edition. J. Wiley and Sons, Inc., wiley

edition.

Kruse, S. (2015). Co-Evolution of Metamodels and Model

Transformations - An operator-based, stepwise ap-

proach for the impact resolution of metamodel evo-

lution on model transformations.

L., X. (2001). A dynamique warehouse for xml data of

the web. International Database Engineering and Ap-

plication Symposium (IDEAS’05), Grenoble, France,

IEEE Computer Society:3–7.

Le Duc, C. (2008). Transformation d’ontologies bas

ees sur

la logique de description : Application dans le com-

merce

electronique. Phd thesis, Universit

e de Nice -

Sophia Antipolis.

Liao, S.-H., Huang, H.-C., and Chen, Y.-N. (2010). A

semantic approch to heterogeneous metadata integra-

tion. Springer- Verlag Berlin heidelberg, pages 205–

214.

Lutz, C., Sattler, U., Tinelli, C., Turhan, A.-Y., and Wolter,

F. (2019). Description logic, theory combination, and

all that: Essays dedicated to franz baader on the occa-

CWM Extensions for Knowledge and Metadata Integration for Complex Data Warehouse and Big Data

335

sion of his 60th birthday. Lecture Notes in Computer

Science N°11560.

Mahboubi, H., Ralaivao, J. C., Loudcher, S., Boussa

ıd,

O., and Bentayeb, F. (2009). X-wacoda: An xml-

based approach for warehousing and analyzing com-

plex data. Advances in Data Warehousing and Mining,

IGI Publishing(3):38–54. Data Warehousing Design

and Advanced Engineering Applications: Methods for

Complex Construction.

Midouni, S. A. D., Darmont, J., and Bentayeb, F.

(2009). Approche de mod

elisation multidimen-

sionnelle des donn

ees complexes : application aux

donn

ees m

edicales,. In Journ

ees Francophones sur

les Entrep

ots de Donn

ees et l’Analyse en ligne.

uller, J.-P., Rakotonirainy, H. L., and Herv

e, D. (2011).

Towards a description logic for scientiﬁc modeling.

International Conference on Knowledge Engineering

and Ontology Development (KEOD).

Nassis, V., Rajugan, R., Dillon, T. S., and Rahayu, J. W.

(2005). Conceptual and systematic design approach

for xml document warehouses. International Journal

of Data Warehousing & Mining 1 (3), pages 63–86.

Nemati, H. R., Steiger, D. M., Iyer, L. S., and Herschel,

R. T. (2002). Knowledge warehouse: an architec-

tural integration of knowledge management, decision

support, artiﬁcial intelligence and data warehousing.

pages 143–161.

Ngo, V. M., Le-Khac, N.-A., and Kechadi, M.-T. (2019).

Designing and implementing datawarehouse for agri-

cultural big data. In Chen, K., Seshadri, S., and Zhang,

L.-J., editors, Big Data - BigData 2019, pages 1–17.

8th International Congress Held as Part of the Services

Conference Federation, SCF 2019.

Obitko, M., Sn

asel, V., and Smid, J. (2004). Ontology de-

sign with formal concept analysis. In International

Conference on Concept Lattices and their Applica-

tions.

Pan, Y. (2020). Multiple knowledge representation of arti-

ﬁcial intelligence. ELSEVIER. Institute of Artiﬁcial

Intelligence, Zhejiang University.

Park, B. K., Han, H., and Song, I. Y. (2005). Xml-

olap: A multidimensional analysis framework for xlm

warehouses. 7th International Conference on Data

Warehousing and Knowledge Discovery (DaWaK’05),

Copenhagen, Denmark, Springer:267–280. Vol. 3589

of lecture Notes in Computer Science.

Pokorny, J. (2006). Xml data warehouse: Modeling

and querying. 5th International Baltic Conference

(BalticDb&IS’06), Tallin, Estonia, Institute of Cy-

bernectics at Tallin Technical University:267–280.

Poole, J., Chang, D., Tolbert, D., and Mellor, D. (2003).

Common Warehouse Metamodels. Wiley Publishing

Inc., omg press edition.

Rajugan, R., Chang, E., and Dillon, T. S. (2005). Con-

ceptual design of an xml fact repository for dispersed

xml document warehouses and xml marts. 20th In-

ternational Conference on Computer and Information

Technology (CIT’05), Shanghai, China.

Ralaivao, J. C. and Darmont, J. (2007). Knowledge and

metadata integration for warehousing complex data.

6th International Conference on Information Systems

Technology and its Applications (ISTA 07), Kharkiv,

Ukraine. GI-Edition(107):164–175. Lecture Notes in

Informatics.

Rusu, L. I., Rahaya, J. W., and Taniar, D. (2005). A method-

ology for buidlding xml data warehouse. International

Journal of DataWarehousing and Mining 1 (2), pages

67–92.

Sakr, S. and GaberSakr, M. M. (2014). Large Scale and Big

Data: Processing and Management. CRC Press.

Sawadogo, P. and Darmont, J. (2020). On data lake archi-

tectures and metadata management. JIIS.

Sawadogo, P. N., Kibata, T., and Darmont, J. (2019). Meta-

data management for textual documents in data lakes.

ICEIS.

Soler, E., Trujillo, J., Fern

andez-Medina, E., and Piattini,

M. G. (2008a). Building a secure star schema in data

warehouses by an extension of the relational package

from cwm. 30:341–350.

Soler, E., Trujillo, J., Fern

andez-Medina, E., and Piattini,

M. G. (2008b). An extension of the relational meta-

model of cwm to represent secure data warehouses at

the logical level. 6:355–362.

Srinivasan, V. (2016). The Intelligent Enterprise in the Era

of Big Data. WILEY.

Tavac, M. and Tavac, V. (2013). The general algorithm for

the design of the mda transformation models. pages

171–176.

Thavornun, V. (2015). Metadata management for knowl-

edge discovery.

Vrdoljak, B., Banek, M., and Rizzi, S. (2003). Design-

ing web warehouse from xml schemas. 5th Intena-

tional Conference on DataWarehousing and knowl-

edge Diskovery (DaWak’03), Prague, Czech Republic,

pages 89–98. Vol. 2737 of Lecture Notes in Computer

Science. Springer.

Wender, B. A. (2017). Reﬁning the Concept of Scientiﬁc In-

ference When Working with Big Data: Proceedings of

a Workshop. The National Academic Press. National

Academics of Sciences, Engineering, and Medicine.

Wu, D. and Hakansson, A. (2010). Applying a knowledge

based system for metadata integration for data wara-

houses. In Setchi, R., Jordanov, I., Howlett, R. J., and

Jain, L. C., editors, Knowledge-Based and Intelligent

Information and Engineering Systems, pages 60–69.

Zhang, J., Wang, W., Liu, H., and Zhang, S. (2005). X-

warehouse: building query pattern-driven data. In-

ternational conference World Wide Web (WWW’05),

CHiba, Japan, ACM:896–897.

Zhao, X. and Huang, Z. A formal framework for reasoning

on metadata based on cwm. In International Confer-

ence on Conceptual Modeling.

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

336