scholarly journals Requirements Elicitation in Data Mining for Business Intelligence Projects

2008 ◽  
pp. 139-150 ◽  
Author(s):  
Paola Britos ◽  
Oscar Dieste ◽  
Ramón García-Martínez
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Requirements Elicitation

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

scholarly journals Performance evaluation of different classification techniques using different datasets

2019 ◽  
Vol 9 (5) ◽  
pp. 3584
Author(s):  
Abdulkadir Özdemir ◽  
Uğur Yavuz ◽  
Fares Abdulhafidh Dael
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Multilayer Perceptron ◽  
Data Classification ◽  
Major Effect ◽  
Classification Techniques ◽  
The Difference ◽  
Two Parameters ◽  
Accuracy Performance

<span>Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.</span>

scholarly journals Atualização da doutrina de gerenciamento de crises: Incidentes policiais e centros de consciência situacional C5I na quarta revolução industrial

2020 ◽  
Vol 13 (1) ◽  
pp. 49-59
Author(s):  
Paulo Augusto Aguilar
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence

O presente artigo tem como objetivo abordar a atualização de gerenciamento de crises. Trata-se de um tema bastante importante para a atividade policial, pois visa a expandir a possibilidade das polícias brasileiras, seja militar, civil ou federal, de investigar delitos diversos, relacionados à segurança pública, ao fornecer conceitos de Big Data, Data Mining, Data Storytelling e Business Intelligence como forma de gerar melhor consciência situacional e imagem operacional comum de incidentes de todos os tipos e tamanhos, tudo isso com a flexibilidade de aplicativos disponíveis em smartphones, em tempo real, agilizando a capacidade de resposta e de adaptação do Estado diante de cenários VUCA, utilizado para descrever cenários caracterizados por volatilidade (volatility), incerteza (uncertainty), complexidade (complexity) e ambiguidade (ambiguity).

Business Intelligence in Sports Retail: Data Mining Application

2021 ◽  
Author(s):  
Francisca Castelo-Branco ◽  
Jose Luis Reis ◽  
Jose Carvalho Vieira ◽  
Jose Paulo Marques dos Santos
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Data Mining Application

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Overview of Business Intelligence through Data Mining

2014 ◽  
pp. 272-295
Author(s):  
Abdulrahman R. Alazemi ◽  
Abdulaziz R. Alazemi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Information Technologies ◽  
Customer Relationships ◽  
The Internet ◽  
Competitive Advantages ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Internet Business ◽  
Future Outcomes

The advent of information technologies brought with it the availability of huge amounts of data to be utilized by enterprises. Data mining technologies are used to search vast amounts of data for vital insight regarding business. Data mining is used to acquire business intelligence and to acquire hidden knowledge in large databases or the Internet. Business intelligence can find hidden relations, predict future outcomes, and speculate and allocate resources. This uncovered knowledge helps in gaining competitive advantages, better customer relationships, and even fraud detection. In this chapter, the authors describe how data mining is used to achieve business intelligence. Furthermore, they look into some of the challenges in achieving business intelligence.

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