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.

Expanding Business Intelligence Power with System Dynamics

2011 ◽  
pp. 126-140 ◽  
Author(s):  
Edilberto Casado
Keyword(s):  
Data Mining ◽  
System Dynamics ◽  
Real World ◽  
Business Intelligence ◽  
Simulation And Modeling ◽  
Business World ◽  
Data Mining Techniques ◽  
Business Dynamics ◽  
Using Data ◽  
Systems Thinking Approach

This chapter explores the opportunities to expand the forecasting and business understanding capabilities of Business Intelligence (BI) tools with the support of the system dynamics approach. System dynamics tools can enhance the insights provided by BI applications — specifically by using data-mining techniques, through simulation and modeling of real world under a “systems thinking” approach, improving forecasts, and contributing to a better understanding of the business dynamics of any organization. Since there is not enough diffusion and understanding in the business world about system dynamics concepts and advantages, this chapter is intended to motivate further research and the development of better and more powerful applications for BI.

Symbiotic Data Miner

2010 ◽  
pp. 1903-1908 ◽  
Author(s):  
Kuriakose Athappilly
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Business Environment ◽  
Evolutionary Approach ◽  
Complex Data ◽  
Data Mining Techniques ◽  
New Knowledge ◽  
Technical Professionals

Symbiotic data mining is an evolutionary approach to how organizations analyze, interpret, and create new knowledge from large pools of data. Symbiotic data miners are trained business and technical professionals skilled in applying complex data-mining techniques and business intelligence tools to challenges in a dynamic business environment.

Symbiotic Data Mining

2011 ◽  
pp. 1083-1086
Author(s):  
Kuriakose Athappilly ◽  
Alan Rea
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Business Environment ◽  
Evolutionary Approach ◽  
Complex Data ◽  
Data Mining Techniques ◽  
New Knowledge ◽  
Technical Professionals

Symbiotic data mining is an evolutionary approach to how organizations analyze, interpret, and create new knowledge from large pools of data. Symbiotic data miners are trained business and technical professionals skilled in applying complex data-mining techniques and business intelligence tools to challenges in a dynamic business environment.

scholarly journals Classification of BOF Slag by Data Mining Techniques According to Chemical Composition

2020 ◽  
Vol 12 (8) ◽  
pp. 3301
Author(s):  
Sara M. Andrés-Vizán ◽  
Joaquín M. Villanueva-Balsera ◽  
J. Valeriano Álvarez-Cabal ◽  
Gemma M. Martínez-Huerta
Keyword(s):  
Data Mining ◽  
Environmental Impact ◽  
Basic Oxygen Furnace ◽  
Added Value ◽  
Data Mining Techniques ◽  
Different Types ◽  
Potential Applications ◽  
Bof Slag ◽  
Slag Valorization

In the process of converting pig iron into steel, some co-products are generated—among which, basic oxygen furnace (BOF) slag is highlighted due to the great amount generated (about 126 kg of BOF slag per ton of steel grade). Great efforts have been made throughout the years toward finding an application to minimize the environmental impact and to increase sustainability while generating added value. Finding BOF slag valorization is difficult due to its heterogeneity, strength, and overall swallowing, which prevents its use in civil engineering projects. This work is focused on trying to resolve the heterogeneity issue. If many different types of steel are manufactured, then different types of slag could also be generated, and for each type of BOF slag, there is an adequate valorization option. Not all of the slag can be valorized, but it can be a tool for reducing the amount that must go to landfill and to minimize the environmental impact. An analysis by means of data mining techniques allows a classification of BOF slag to be obtained, and each one of these types has a better adjustment to certain valorization alternatives. In the plant used as an example of the application of these studies, eight different slag clusters were obtained, which were then linked to their different potential applications with the aim of increasing the amount valorized.

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