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

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

Satyadhyan Chickerur (KLE Technological University, India), Supreeth Sharma (Akamai Technologies, India) and Prashant M. Narayankar (KLE Technological University, India)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/978-1-5225-2255-3.ch073
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Abstract

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.
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Introduction

Business Intelligence (BI) has been around for more than a decade and is being used in various application domains. BI is about creating value for organizations based on data or, more precisely, facts. While it seems like another buzzword to describe what successful entrepreneurs have been doing for years, if not centuries, that is, using business common sense? From a modern business-value perspective, corporations use BI (Sherman, 2014; Prasad & Acharya, 2011) to enhance decision-making capabilities for managerial processes (e.g., planning, budgeting, controlling, assessing, measuring, forecasting and monitoring) and to ensure critical information is exploited in a timely manner. And computer systems are the tools that help us do that better, faster, and with more reliability. BI is a broad category of application programs and technologies for gathering, storing, analyzing and providing access to data to help enterprise users make better business decisions. BI (Sherman, 2014; Prasad & Acharya, 2011) applications support the activities of decision support, query and reporting, online analytical processing, statistical analysis, forecasting, and data mining. BI includes a set of concepts and methods to improve business decision making by using fact-based support systems.BI has became synonymous with historical query and reporting tools for many years, the definition of BI in recent years has evolved to include technology that addresses data integration, predictive analytics and information deployment (Shmueli, Patel, & Bruce, 2010; Sherman, 2014; Prasad & Acharya, 2011). Predictive analytics often help companies address their most difficult business issues and achieve a competitive advantage in their respective markets. Forecasting is a discipline of analytics that provides the foundation for planning processes across organizations. Good forecasting provides a more accurate view of the future, helping an organization save time and reduce costs, while better serving its customers and managing its resources. When used with data integration and information deployment capabilities, forecasting becomes even more powerful by pulling from multiple data sources and disseminating forecast results consistently. Information technology for agricultural (Shmueli et al., 2010) use can be broadly classified for:

  • 1.

    Tools which might be used to improve productivity

  • 2.

    Tools which empower farmers to take informed and quality decisions

At present the information available for farmers is through conventional sources, which are slow and unreliable. As a result of the information not reaching in time to the farmers, there is lot of wastage in terms of crops getting spoiled, same crop getting excess production and some crops not getting produced at all. These factors ultimately result in controlling the prices of the commodities dependent upon a particular crop. Thus the Framers would be in a position to take the benefit of the technology used in the proposed approach. BI allows the decision maker to understand their business environment in order to make informed decision. Decision-making requires evaluating performance (what happened), testing hypotheses (why and how things happened) and predicting future events (what may happen). Stated simply management needs to know if their strategies are sound if they are being carried out. Most formally a business intelligence system allows users to answer above-mentioned decisions. Forecasting involves predicting and analysis of the given data, which can be achieved by BI.

  • Predictive Analytics: Predictive analytics represent any solution that supports the identification of meaningful patterns and correlations among variables in complex, structured, unstructured, historical, and potential future data sets for the purposes of predicting events and assessing the attractiveness of various courses of action.

  • Planning Decisions: This includes analytical reporting based on a data warehouse (DW) (Shmueli et al., 2010) or data mart and operational reporting based on an operational database management system. Reporting tools often include pixel-perfect positioning of data and graphics, a scripting language equal in power to a full program programming language, and the ability to handle complex headers, footers, nested subtotals, and multiple report bands on a single page.

Key Terms in this Chapter

Loading: The loading refers to the phase of loading the data into the end target, usually the data warehouse (DW).

ETL (Extract, Transform and Load): It is the primary step for applying the business intelligence. It mainly included all those preprocessing activities, which are required for transforming the data into a proper format, which are required for analysis.

Business Intelligence: Business intelligence is getting the right information to the right people at the right time so they can make decisions that ultimately improve performance.

Data Warehouse (DW): A data warehouse is a repository of information collected from multiple sources, stored under a unified schema, and usually residing at a single site.

Functional Dependencies: A functional dependency a constraint between two sets of attributes from the database. Functional dependencies are the formal tool for analysis of relational schema that enables us to detect the redundancies present in the clustered data.

Clustering: The term clustering refers to the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters.

Predictive Analytics: Predictive analytics represent any solution that supports the identification of meaningful patterns and correlations among variables in complex, structured, unstructured, historical, and potential future data sets for the purposes of predicting events and assessing the attractiveness of various courses of action.

Normalization: Normalization is the process of organizing the fields and tables of a relational database to minimize redundancy and dependency.

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