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.

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

2018 ◽  
pp. 847-861
Author(s):  
Satyadhyan Chickerur ◽  
Supreeth Sharma ◽  
Prashant M. Narayankar
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Business Intelligence ◽  
Quality Data ◽  
Agricultural Crops ◽  
Agricultural Community ◽  
Mining Tool ◽  
Set Up ◽  
Efficiency And Productivity ◽  
Agricultural Demand

Information technology now a days 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 paper 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 paper 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 tool 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 the open source tools like Pentaho's Kettle and Weka.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

Organizational Data Mining (ODM)

2008 ◽  
pp. 2289-2295 ◽  
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

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.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2011 ◽  
pp. 1013-1020
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

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.

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