Bayesian network modelling technologies have been compared with other AI (artificial intelligence) techniques such as neural networks and ruled-based techniques in recent scientific research. The probabilistic networks showed better performance over these techniques in water analysis [78] [79]. This experiment therefore evaluates our ESA with the classical statistical methods as a baseline of comparisons in handling uncertainties.
Once again, the classical statistical methods use the frequency concept of probability [8] [67] to handle uncertainties, while the temporal probabilistic modelling of the ESA uses Bayes’ theorem [34]
over time. The uncertainties imply that if a set of unobserved sales situations arises, how will the decision-makers handle it? This type of situation enables them to be prepared for unforeseen sales contingencies. If an IT technology cannot provide a meaningful solution, then the information gap problem which leads to the misalignment is created for the business analysts. We therefore reasoned with a sampled selected set of probabilistic situations shown in Table 6.3 using both approaches, but found that the classical statistical methods fall short or are limited to capturing the uncertainties embedded in the business environment (the meat packer’s sales data).
University
of Cape
Town
120
Observe zero percentages (or probabilities) for all situations chosen at random on the statistical methods column in Table 6.3. We understand that this is because the classical statistical methods assume that all possible data of sales situations must be observed [8], but this is not true in the real life of uncertainty, especially in business. For the five sales situations shown in the other column, the temporal probabilistic model of the ESA outperforms the classical methods in handling uncertainty. Out of the unobserved sales data, the temporal probabilistic model finds the most likely percentages of the meat sales, given the respective situations in Table 6.3. These results are compared as shown in Figure 6.15.
This shows that the ESA could reason with the underlying uncertainties and provides meaningful information to the sales managers. This carries the decision-makers along with IT technologies, rather than being inaccessible or unfriendly, and the non-zero results contribute likely ideas to SBITA.
Table 6.3: Comparing reasoning with uncertainties embedded in business situations using both classical statistical methods and the ESA.
Probabilistic Business Situations Frequency Concept of Probability
Temporal Probabilistic Reasoning –ESA pr(Product-Type t = Chicken | Sales-Outlet t=
Brackenfell, PublicHoliday t = Holiday)
0.0%
t = January
18.04%
t = January pr(Product-Type t = Coldmeats | Sales-Outlet t =
Tableview, PublicHoliday t = Holiday)
0.0%
t = February
18.97%
t = February pr(Sales-Outlet t =BRUMA| Product-Type t = Lamb,
PublicHoliday t = None)
0.0%
t = March
13.01%
t = March pr(Sales-Outlet t =BRUMA| Product-Type t = Beef,
PublicHoliday t = None)
0.0%
t = April
14.67%
t = April pr(Sales-Outlet t =TOKAI| Product-Type t = Beef,
PublicHoliday t = None)
0.0%
t = January
17.04%
t = January
University
of Cape
Town
121
Figure 6.15: Comparing Capability of Handling Unforeseen (Uncertainty) Situations between Classical Statistical Methods and the ESA.
6.5
Conclusions
In this chapter, we have described the development of a new temporal probabilistic modelling technology, ESA, from the domain of well-defined DBN research areas. By interactively revealing local dynamics from global behaviour over time, ESA has shown itself to be suitable for better understanding of patterns in any domain of interest. The ESA is rigorously subjected to a number of experimental evaluations and applications to show that it can reveal variability in order to supply a detailed pattern understanding of real-life problems. This consequently bridges the information gap and therefore facilitates the making of accurate and economical decisions. We have shown that DBN needs to be evolved with variability in the temporal frames of the time steps. We carried out empirical evaluations on the ESA to measure its reliability as 87.5% in water quality. Recall the risk variability reflected in the situation awareness results in Figures 6.6 – 6.9, which approximately follow the theoretical understanding of water quality. The ESA contributes illustrative solutions to SBITA problems by making correct and astute decisions. It was shown that hidden knowledge of products that are likely to be sold together can be revealed to decision-makers from ever-changing customer buying patterns, through using the temporal probabilistic reasoning. We have also illustrated with our technology that revealing hidden knowledge about the percentage of product sales in festive periods can make production and distribution planning easier.
In the water quality experiment, the ESA is further compared with related techniques, such as the widely used smallest DBN, and found that the ESA guarantees wider applicability to science, engineering and industry. We also carried out performance evaluations of our ESA and the classical statistical
University
of Cape
Town
122
methods often implemented in spreadsheets regarding capability of handling uncertainties embedded in sales environments (multivariate time series). The results show from the five probabilistic situations selected in Table III and Figure 6.15 that the temporal probabilistic reasoning of the ESA outperforms the classical methods in handling uncertainties embedded in business environments. From the water quality results, this chapter shows that the ESA can potentially become a powerful pattern recognition technology to operate safely by avoiding the appearance of critical or risk situations for the real-life problems. This chapter also shows that the temporal probabilistic reasoning of the ESA can potentially become a more powerful technology for completely resolving the misalignment problems of business and IT. With reference to the complexity of the business environment, recall the hidden knowledge revealed over time about the 82% customer buying patterns and the decision guidelines including production planning. If the current implementations of classical statistical methods such as spreadsheets can integrate temporal probabilistic reasoning for decision-makers, there will be more contributive solutions to SBITA problems.
Thus, interestingly, the ESA accommodates more users (e.g. managers, professionals, etc.), unlike most existing DBNs that require highly skilled users.
University
of Cape
Town
123