The chapter presents machine learning approaches for business continuity and recovery optimization in agribusiness. Firstly, a mathematical method, entitled business continuity points (BCPTs) is tested with domain data for its potential to predict process recovery results, namely recovery time and criticality ranking of key operations. A 72.22% accuracy has been estimated. Then, decision tree prediction with 10-fold cross validation and random forest has been 92.31% accurate in classifying business functions as critical or not. Additionally, a new multi-approach and multi-class decision tree classifier with some of the BCPTs input variables is presented, with 55.36% accuracy, and 70.37% and 88.89% accuracy rates when boosted with the 10 folds and the random forest. Finally, regression analysis techniques are used to improve the initial recovery time BCPTs formula. Exponential regression has been more precise compared to the quadratic model (R2exp=0.954, R2quad=0.85). Despite current data limitations, the inferred prediction patterns are robust and highly accurate in the given field.
The paper investigates the importance of business intelligence solutions in modern enterprises using association rule mining techniques. The research is based on a questionnaire addressed to different employee target groups regarding their age interval, their employment status, their domain of employment, their experience or inexperience with business intelligence tools and their positive or negative aspect regarding the importance of business intelligence in modern companies. 90 responses have been received and used for dataset formulation. Using the association rule induction standard procedure, the most popular rules with respect to different antecedent item combinations and business intelligence value as consequent item have been inferred setting as minimum confidence 50% and minimum support 0,1. The collected data have been prepared in common separated values format and the association rules have been inferred using the R- Package. In general, among other rules, a strong relation between BI experience and positive BI aspect can be reported which is also confirmed via simple Pearson X2 statistical test in R. An investigation paradox which has been spotted is the negative opinion regarding the BI usefulness stemming from a minority of respondents familiar with BI tools.
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