Business Intelligence–Hybrid Metaheuristics Techniques

Business Intelligence–Hybrid Metaheuristics Techniques

Mary Jeyanthi Prem (VELS University, Chennai, Tamil Nadu, India) and M. Karnan (Tamil Nadu College of Engineering, Coimbatore, Tamil Nadu, India)
Copyright: © 2014 |Pages: 7
DOI: 10.4018/ijbir.2014010105


Business Intelligence (BI) is about getting the right information, to the right decision makers, at the right time. A business intelligence environment offers decision makers information and knowledge derived from data processing, through the application of mathematical models and algorithms. BI systems tend to promote a scientific and rational approach to managing enterprises and complex organizations. Soft computing is a collection of new techniques in artificial intelligence, which exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The purpose of this article is to provide an overview of soft computing techniques for the optimal and dynamic decision making system in the current business world.
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2. Literature Review:

In a 1958 article, IBM researcher Hans Peter Luhn (Gartner, n.d.) used the term business intelligence. He defined intelligence as: “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.” Business intelligence as it is understood today is said to have evolved from the decision support systems which began in the 1960s and developed throughout the mid-80s. Decision Support System (DSS) originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.

In 1989 Howard Dresner (Bavis, 1987) (later a Gartner Group analyst) proposed “business intelligence” as an umbrella term to describe “concepts and methods to improve business decision making by using fact-based support systems.” A 2009 Gartner paper predicted (Gartner, n.d.) these developments in the business intelligence market that because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets By 2012, business units will control at least 40 percent of the total budget for business intelligence.

2.1 Demerits

There are several problems/challenges when trying to develop BI with semi-structured data, and according to Inmon and Nesavich (2008) (Kantardzic, 2002) some of those are:

  • 1.

    Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats.

  • 2.

    Terminology – Among researchers and analysts, there is a need to develop a standardized terminology.

  • 3.

    Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis..

  • 4.

    The management of semi-structured data is recognized as a major unsolved problem in the information technology industry. (Blumberg & Atre, 2003)

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