Rapid unconstrained fab model using a business intelligence tool: DM: Data management and data mining tools

2016 ◽  
Author(s):  
Subramaniam Pazhani ◽  
Madan Chakravarthi ◽  
Diwas Adhikari
Keyword(s):  
Data Mining ◽  
Data Management ◽  
Business Intelligence ◽  
Fab Model ◽  
Mining Tools

Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework

2019 ◽  
pp. 460-477
Author(s):  
Satyadhyan Chickerur ◽  
Supreeth Sharma ◽  
Prashant M. Narayankar
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Business Intelligence ◽  
Quality Data ◽  
Agricultural Crops ◽  
Agricultural Community ◽  
Set Up ◽  
Efficiency And Productivity ◽  
Agricultural Demand ◽  
Mining Tools

Information technology is playing a very important role in all the spheres of life, starting from healthcare to entertainment. The agricultural community is not far behind in utilizing information technology for increasing the efficiency and productivity of agriculture and allied activities. This chapter proposes how the concepts of BI (business intelligence), BI tools, data mining tools might be used for forecasting the agricultural demand of various crops reliably and more efficiently. The chapter clearly elaborates how BI tools could be used during various stages of ETL (extract, transform, and load) and how cleansed, quality data could be used by data mining tools for forecasting. Experiments are carried out for forecasting the demands for various agricultural crops by using the previous year's demand, and the results are encouraging. The experimental set up involved open source tools like Pentaho's Kettle and Weka.

scholarly journals Analysis on Data Mining Tools Used in Business Intelligence and Inventions

2021 ◽  
pp. 57-68
Author(s):  
Madeleine Wang Yue Dong
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Clustered Data ◽  
Competitive Intelligence ◽  
Business Decisions ◽  
Annual Trend ◽  
Mathematical Analyses ◽  
Mining Tools

This paper will evaluate data mining tools for competitive intelligence and technology. Data analyzers i.e. Thomson and OmniViz are the tools for completing diversified and sophisticated mathematical analyses of data. AnaVist and Aureka are considerable for modest visualization of statistics and itoplistsi is used for creating maps that are stylish. Novel features of OmniViz during the comparison of other tested tools are used for visualizing clustered data from difference viewpoints, which makes it possible to assess the attributes using patent map animation. The Thomson data analyzer provides effective tools that compare various subsets for data, such as the identification of unique attribute values. In citation assessments, Aureka is used as well as in illustrative patent maps. AnaVist is the best in retrieving basis statistics smoothly and quickly. The findings from four tools were similar, despite the fact that various databases for data retrieving were utilized. Superior investors and assignees list were the same, since they were an annual trend for geographical and technological business segments. Nonetheless, the conclusions from the findings were that business decisions are made using their tools to enhance competitive intelligence.

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.

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

2021 ◽  
pp. 1-19
Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh
Keyword(s):  
Data Mining ◽  
Hot Spot ◽  
Multinomial Logit ◽  
Hot Spot Analysis ◽  
Mining Tools ◽  
Spot Analysis

DATA MINING FOR THE MANAGEMENT OF SOFTWARE DEVELOPMENT PROCESS

2004 ◽  
Vol 14 (06) ◽  
pp. 665-695 ◽  
Author(s):  
J. L. ÁLVAREZ-MACÍAS ◽  
J. MATA-VÁZQUEZ ◽  
J. C. RIQUELME-SANTOS
Keyword(s):  
Data Mining ◽  
Software Development ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Development Process ◽  
A Priori ◽  
Post Mortem ◽  
Software Development Process ◽  
A Priori Analysis ◽  
Mining Tools

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.

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