255 research outputs found
BUILDING DSS USING KNOWLEDGE DISCOVERY IN DATABASE APPLIED TO ADMISSION & REGISTRATION FUNCTIONS
This research investigates the practical issues surrounding the development and
implementation of Decision Support Systems (DSS). The research describes the traditional
development approaches analyzing their drawbacks and introduces a new DSS development
methodology. The proposed DSS methodology is based upon four modules; needs' analysis,
data warehouse (DW), knowledge discovery in database (KDD), and a DSS module.
The proposed DSS methodology is applied to and evaluated using the admission and
registration functions in Egyptian Universities. The research investigates the organizational
requirements that are required to underpin these functions in Egyptian Universities. These
requirements have been identified following an in-depth survey of the recruitment process in
the Egyptian Universities. This survey employed a multi-part admission and registration DSS
questionnaire (ARDSSQ) to identify the required data sources together with the likely users
and their information needs. The questionnaire was sent to senior managers within the
Egyptian Universities (both private and government) with responsibility for student
recruitment, in particular admission and registration.
Further, access to a large database has allowed the evaluation of the practical suitability of
using a data warehouse structure and knowledge management tools within the decision
making framework. 1600 students' records have been analyzed to explore the KDD process,
and another 2000 records have been used to build and test the data mining techniques within
the KDD process.
Moreover, the research has analyzed the key characteristics of data warehouses and explored
the advantages and disadvantages of such data structures. This evaluation has been used to
build a data warehouse for the Egyptian Universities that handle their admission and
registration related archival data. The decision makers' potential benefits of the data
warehouse within the student recruitment process will be explored.
The design of the proposed admission and registration DSS (ARDSS) will be developed and
tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL
Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will
be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will
also be used as a clustering package.
Finally, the contribution of this research is found in the following areas:
A new DSS development methodology;
The development and validation of a new research questionnaire (i.e. ARDSSQ);
The development of the admission and registration data warehouse;
The evaluation and use of cluster analysis proximities and techniques in the KDD process
to find knowledge in the students' records;
And the development of the ARDSS software that encompasses the advantages of the
KDD and DW and submitting these advantages to the senior admission and registration
managers in the Egyptian Universities.
The ARDSS software could be adjusted for usage in different countries for the same purpose,
it is also scalable to handle new decision situations and can be integrated with other systems
Data mining languages for business intelligence
Doctoral Thesis in Information Systems and Technologies Area of Engineering and Manag
ement Information SystemsDesde 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
Data mining languages for business intelligence
Tese de doutoramento in Information Systems and Technologies (area of Engineering and Management Information Systems)Desde que Lunh usou, pela primeira vez, em 1958, o termo Business Intelligence (BI), grandes
transformações se operaram na área dos sistemas e tecnologias 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 estratégica é largamente reconhecida. Estes
sistemas apresentam-se como essenciais para um completo 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 empresariais.
As ferramentas de BI são ferramentas amigáveis, iterativas e interativas, permitindo aos
utilizadores finais um acesso fácil. Desta forma, é possível ao utilizador final manipular
diretamente os dados, tendo assim a possibilidade de extrair todo o valor para o negócio neles
contido. Um dos problemas apontados na utilização do DM na área do BI prende-se com o facto
de os modelos de DM serem, em geral, demasiado complexos para que os utilizadores de
negócio os possam manipular diretamente, contrariamente ao que ocorre com as outras
ferramentas de BI.
Neste contexto, foi identificado como problema de investigação a não existência de ferramentas
de BI que possibilitem ao utilizador de negócio a manipulação 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 empresarial 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 organizações poderem concorrer num
mercado extremamente exigente.
Considerando que os sistemas de BI assentam, maioritariamente, sobre sistemas operacionais
que utilizam sobretudo o modelo relacional de bases de dados, a investigação efetuada inspirouse
nos conceitos ligados ao modelo relacional de bases de dados e nas linguagens a ele
associadas em particular as linguagens Query-By-Example (QBE). Estas linguagens têm uma
forte componente de interactividade, são amigáveis e permitem iteratividade e são amplamente
utilizadas em ambiente de negócio pelos utilizadores finais.
Têm vindo a ser desenvolvidos esforços no sentido do desenvolvimento de padrões e normas na
área do DM, sendo dada grande relevância ao tema das bases de dados indutivas. No contexto das bases de dados indutivas é dada grande relevância às chamadas linguagens de DM. Estes
conceitos serviram, igualmente, de inspiração a esta 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 BI, 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 advir. Utilizando um protótipo de um sistema
de BI, a linguagem foi implementada, testada e avaliada conceptualmente. Verificou-se que a
linguagem possui as propriedades desejadas, a saber, é amigável, iterativa, interativa.
Finalmente, a linguagem foi avaliada por utilizadores finais que já tinham experiência anterior na
utilização de DM em contexto de BI. Verificou-se que na ótica destes utilizadores a utilização da
linguagem apresenta vantagens em relação à utilização tradicional de DM no âmbito do BI.Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations
happened in the field of information systems and technologies, especially in the area of decision
support systems. Nowadays, BI systems are widely used in organizations and their strategic
importance is clearly recognized. These systems present 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 increasing in the BI field, as well as the
acknowledgement of the relevance of its usage in enterprise BI systems.
BI tools are friendly, iterative and interactive, allowing 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 allowing 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 particular relevance in an entrepreneurial
universe where competition is stronger every day and 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 built on top of operational systems, which use
mainly the relational model for databases, the research was inspired on the concepts related to
this model and associated languages in particular Query-By-Example (QBE) languages. These
languages are widely used by business users in business environments, and have got a strong
interactivity component, are user-friendly, and allow for iterativeness.
Efforts are being developed in order to create standards and rules in the field of DM with great
relevance being given to the subject of inductive databases. Within the context of inductive
databases a great relevance is given to the so called DM languages. These concepts were also an
inspiration for this research. Despite their importance, these languages are not oriented to
business users in business environments. Linking concepts related with QBE languages and with DM languages, a new DM language for BI,
named as Query-Models-By-Example (QMBE) was conceived and implemented. This new
language is, by nature, user-friendly, iterative and interactive; it presents the same characteristics
as the usual BI tools allowing business users the direct manipulation of DM models and, through
this, the access to the potential value of these models with all the advantages that may arise.
Using a BI system prototype, the language was implemented, tested, and conceptually evaluated.
It has been verified that the language possesses the desired properties, namely, being userfriendly,
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
A Predictive Modeling System: Early identification of students at-risk enrolled in online learning programs
Predictive statistical modeling shows promise in accurately predicting academic performance for students enrolled in online programs. This approach has proven effective in accurately identifying students who are at-risk enabling instructors to provide instructional intervention. While the potential benefits of statistical modeling is significant, implementations have proven to be complex, costly, and difficult to maintain. To address these issues, the purpose of this study is to develop a fully integrated, automated predictive modeling system (PMS) that is flexible, easy to use, and portable to identify students who are potentially at-risk for not succeeding in a course they are currently enrolled in. Dynamic and static variables from a student system (edX) will be analyzed to predict academic performance of an individual student or entire class. The PMS model framework will include development of an open-source Web application, application programming interface (API), and SQL reporting services (SSRS). The model is based on knowledge discovery database (KDD) approach utilizing inductive logic programming language (ILP) to analyze student data. This alternative approach for predicting academic performance has several unique advantages over current predictive modeling techniques in use and is a promising new direction in educational research
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