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Advisor(s)
Abstract(s)
Desde que Lunh usou, pela primeira vez, em 1958, o
termo Business Intelligence (BI), grandes
transformações se operaram na área dos sistemas e t
ecnologias de informação e, em especial,
na área dos sistemas de apoio à decisão. Atualmente
, os sistemas de BI são amplamente
utilizados nas organizações e a sua importância est
ratégica é largamente reconhecida. Estes
sistemas apresentam-se como essenciais para um comp
leto conhecimento do negócio e como
uma ferramenta insubstituível no apoio à tomada de
decisão. A divulgação das ferramentas de
Data Mining (DM) tem vindo a aumentar na área do BI, assim como o reconhecimento da
relevância da sua utilização nos sistemas de BI emp
resariais.
As ferramentas de BI são ferramentas amigáveis, ite
rativas e interativas, permitindo aos
utilizadores finais um acesso fácil. Desta forma, é
possível ao utilizador final manipular
diretamente os dados, tendo assim a possibilidade d
e extrair todo o valor para o negócio neles
contido. Um dos problemas apontados na utilização d
o DM na área do BI prende-se com o facto
de os modelos de DM serem, em geral, demasiado comp
lexos para que os utilizadores de
negócio os possam manipular diretamente, contrariam
ente ao que ocorre com as outras
ferramentas de BI.
Neste contexto, foi identificado como problema de i
nvestigação a não existência de ferramentas
de BI que possibilitem ao utilizador de negócio a m
anipulação direta dos modelos de DM e,
consequentemente, não possibilitando extrair todo o
valor potencial neles contidos. Este aspeto
reveste-se de particular importância num universo e
mpresarial no qual a concorrência é cada vez
mais forte e no qual o conhecimento do negócio, das
variáveis envolvidas e dos potenciais
cenários representam um papel fundamental para as o
rganizações poderem concorrer num
mercado extremamente exigente.
Considerando que os sistemas de BI assentam, maiori
tariamente, sobre sistemas operacionais
que utilizam sobretudo o modelo relacional de bases
de dados, a investigação efetuada inspirou-
se nos conceitos ligados ao modelo relacional de ba
ses de dados e nas linguagens a ele
associadas em particular as linguagens Query-By-Exa
mple (QBE). Estas linguagens têm uma
forte componente de interactividade, são amigáveis
e permitem iteratividade e são amplamente
utilizadas em ambiente de negócio pelos utilizadore
s finais.
Têm vindo a ser desenvolvidos esforços no sentido d
o desenvolvimento de padrões e normas na
área do DM, sendo dada grande relevância ao tema da
s bases de dados indutivas. No contexto
Data mining languages for business intelligence
iv
das bases de dados indutivas é dada grande relevânc
ia às chamadas linguagens de DM. Estes
conceitos serviram, igualmente, de inspiração a est
a investigação. Apesar da importância destas
linguagens de DM, elas não estão orientadas para os
utilizadores finais em ambientes de
negócio.
Ligando os conceitos relacionados com as linguagens
QBE e com as linguagens de DM, foi
concebida e implementada uma linguagem de DM para B
I, à qual foi dado o nome QMBE. Esta
nova linguagem é por natureza amigável, iterativa e
interativa, isto é, apresenta as mesmas
características que as ferramentas de BI habituais
permitindo aos utilizadores finais a
manipulação direta dos modelos de DM e, deste modo,
aceder a todo o valor potencial desses
modelos com todos as vantagens que daí poderão advi
r. Utilizando um protótipo de um sistema
de BI, a linguagem foi implementada, testada e aval
iada conceptualmente. Verificou-se que a
linguagem possui as propriedades desejadas, a saber
, é amigável, iterativa, interativa.
Finalmente, a linguagem foi avaliada por utilizador
es finais que já tinham experiência anterior na
utilização de DM em contexto de BI. Verificou-se qu
e na ótica destes utilizadores a utilização da
linguagem apresenta vantagens em relação à utilizaç
ão tradicional de DM no âmbito do BI.
Since Lunh first used the term Business Intelligenc e (BI) in 1958, major transformations happened in the field of information systems and te chnologies, especially in the area of decision support systems. Nowadays, BI systems are widely us ed in organizations and their strategic importance is clearly recognized. These systems pre sent themselves as an essential part of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasi ng in the BI field, as well as the acknowledgement of the relevance of its usage in en terprise BI systems. BI tools are friendly, iterative and interactive, a llowing business users an easy access. This way, the user can directly manipulate data, thus having the possibility to extract all the value contained into that business data. One of the problems noted in the use of DM in the field of BI is related to the fact that DM models are, generally, too complex in order to be directly manipulated by business users, as opposite to other BI tools. Within this context, the nonexistence of BI tools a llowing business users the direct manipulation of DM models was identified as the research problem , since that, as a consequence of business users not directly manipulating DM models, they can be not able of extracting all the potential value contained in DM models. This aspect has a par ticular relevance in an entrepreneurial universe where competition is stronger every day an d the knowledge of the business, the variables involved and the possible scenarios play a fundamental role in allowing organizations to compete in an extremely demanding market. Considering that the majority of BI systems are bui lt on top of operational systems, which use mainly the relational model for databases, the rese arch was inspired on the concepts related to this model and associated languages in particular Q uery-By-Example (QBE) languages. These languages are widely used by business users in busi ness environments, and have got a strong interactivity component, are user-friendly, and all ow for iterativeness. Efforts are being developed in order to create stan dards and rules in the field of DM with great relevance being given to the subject of inductive d atabases. Within the context of inductive databases a great relevance is given to the so call ed DM languages. These concepts were also an inspiration for this research. Despite their import ance, these languages are not oriented to business users in business environments. Data mining languages for business intelligence vi Linking concepts related with QBE languages and wit h DM languages, a new DM language for BI, named as Query-Models-By-Example (QMBE) was conceiv ed and implemented. This new language is, by nature, user-friendly, iterative an d interactive; it presents the same characteristics as the usual BI tools allowing business users the d irect manipulation of DM models and, through this, the access to the potential value of these mo dels with all the advantages that may arise. Using a BI system prototype, the language was imple mented, tested, and conceptually evaluated. It has been verified that the language possesses th e desired properties, namely, being user- friendly, iterative, and interactive. The language was evaluated later by business users who were already experienced in using DM within the context of BI. It has been verified that, according to these users, using the language presents advantages when comparing to the traditional use of DM within BI.
Since Lunh first used the term Business Intelligenc e (BI) in 1958, major transformations happened in the field of information systems and te chnologies, especially in the area of decision support systems. Nowadays, BI systems are widely us ed in organizations and their strategic importance is clearly recognized. These systems pre sent themselves as an essential part of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasi ng in the BI field, as well as the acknowledgement of the relevance of its usage in en terprise BI systems. BI tools are friendly, iterative and interactive, a llowing business users an easy access. This way, the user can directly manipulate data, thus having the possibility to extract all the value contained into that business data. One of the problems noted in the use of DM in the field of BI is related to the fact that DM models are, generally, too complex in order to be directly manipulated by business users, as opposite to other BI tools. Within this context, the nonexistence of BI tools a llowing business users the direct manipulation of DM models was identified as the research problem , since that, as a consequence of business users not directly manipulating DM models, they can be not able of extracting all the potential value contained in DM models. This aspect has a par ticular relevance in an entrepreneurial universe where competition is stronger every day an d the knowledge of the business, the variables involved and the possible scenarios play a fundamental role in allowing organizations to compete in an extremely demanding market. Considering that the majority of BI systems are bui lt on top of operational systems, which use mainly the relational model for databases, the rese arch was inspired on the concepts related to this model and associated languages in particular Q uery-By-Example (QBE) languages. These languages are widely used by business users in busi ness environments, and have got a strong interactivity component, are user-friendly, and all ow for iterativeness. Efforts are being developed in order to create stan dards and rules in the field of DM with great relevance being given to the subject of inductive d atabases. Within the context of inductive databases a great relevance is given to the so call ed DM languages. These concepts were also an inspiration for this research. Despite their import ance, these languages are not oriented to business users in business environments. Data mining languages for business intelligence vi Linking concepts related with QBE languages and wit h DM languages, a new DM language for BI, named as Query-Models-By-Example (QMBE) was conceiv ed and implemented. This new language is, by nature, user-friendly, iterative an d interactive; it presents the same characteristics as the usual BI tools allowing business users the d irect manipulation of DM models and, through this, the access to the potential value of these mo dels with all the advantages that may arise. Using a BI system prototype, the language was imple mented, tested, and conceptually evaluated. It has been verified that the language possesses th e desired properties, namely, being user- friendly, iterative, and interactive. The language was evaluated later by business users who were already experienced in using DM within the context of BI. It has been verified that, according to these users, using the language presents advantages when comparing to the traditional use of DM within BI.
Description
Doctoral Thesis in Information Systems and Technologies Area of Engineering and Manag
ement Information Systems
Keywords
Business intelligence Descoberta de conhecimento em bases de dados Data mining Linguagens de data mining Bases de dados indutivas Data warehouses inductivas Design Science research Modelo relacional Business intelligence Knowledge discovery from databases Data mining standards Data mining Data mining languages Query-by-example Inductive databases Inductive data warehouse Relational model Design Science Research. Business users Design science research
Citation
Publisher
Universidade do Minho