Adaptive Business Intelligence by Zbigniew Michalewicz PDF

By Zbigniew Michalewicz

Within the glossy details period, managers needs to realize the aggressive possibilities represented through decision-support instruments. Adaptive company intelligence platforms mix prediction and optimization recommendations to aid determination makers in advanced, speedily altering environments. those structures deal with the basic questions: what's prone to ensue sooner or later? and what's the easiest determination instantly? Adaptive enterprise Intelligence comprises components of information mining, predictive modeling, forecasting, optimization, and adaptability.The authors have substantial educational examine backgrounds in synthetic intelligence and comparable fields, mixed with years of sensible consulting event in companies and industries around the globe. during this publication they clarify the technology and alertness of various prediction and optimization thoughts, in addition to how those ideas can be utilized to advance adaptive structures. The thoughts lined contain linear regression, time-series forecasting, determination bushes and tables, man made neural networks, genetic programming, fuzzy structures, genetic algorithms, simulated annealing, tabu seek, ant structures, and agent-based modeling.This publication is appropriate for enterprise and IT managers who make judgements in complicated business and repair environments, nonspecialists who are looking to comprehend the technological know-how in the back of higher predictions and judgements, and scholars and researchers who desire a quickly advent to this box.

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This is important for analyzing what-if scenarios. Let us conclude this chapter with an observation that we have already discussed (in general terms) in Chap. 1: namely, that all the knowledge in the world will not guarantee the right or best decision. Hence, even if we possessed “perfect knowledge” and were capable of accurately predicting the price of any car at any auction site for any day, we still would not know how to optimally distribute 3,000 cars on any given day because of all the issues mentioned above.

Variable composition – which is somewhat similar to data transformation – requires problem-specific knowledge to create new variables. Because these new variables (often called synthetic variables) present existing data in a “better” form, they may have a greater impact on the results than the specific prediction model used to produce these results. A trivial example is the creation of a new variable to record the average miles driven per year, which corresponds to the ratio: Mileage / (Current Year – Year + 1) The denominator would tell us the number of years the car was in service, and the entire ratio would tell us the average miles driven per year.

X The problem is so heavily constrained that finding even one feasible solution is often difficult, let alone searching for the optimum solution (or set of solutions). x The problem includes many (possibly conflicting) objectives. In the previous chapter, we gave an example of a real-world business problem in the car leasing industry. There is no question that the number of possible solutions to the car distribution problem is too large for any systematic search (we indicated that the distribution of 3,000 cars to 50 auction sites gives us 503000 possible solutions).

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