Abstract: A data processing device (100) characterizes behavior properties of equipment under observation (105). The device (100) has a plurality of processing units that are adapted to process input values (a) to output values (e) according to numerical transfer functions. The functions implement an input-to-output mapping specified by a configuration (C) that is obtained by pre-processing historic data (114) from a plurality of master equipment (104). The configuration is related to the behavior properties of the equipment (105) so that some of the output values (e) represent the behavior properties of the equipment (105) under observation.

Abstract: TPO is an industry-neutral price optimization solution that recommends optimal prices by performing a simultaneous evaluation of all network-wide demand and supply considerations. TPO produces an optimal time-phased price profile designed to achieve the greatest return on inventory. An optimizer technique takes the baseline demand forecasts, competitive intelligence, inventory data, and other related parameters and data representing real-world objects, and determines the optimal prices at which the user will achieve the greatest return on inventory. The optimizer technique can be directed to maximize revenues, profits, or market share. TPO produces a recommended price profile, that can be manipulated in an interactive graphical user interface, and illustrates what price must be charged now, and what prices must be charged later in the booking cycle. Using price sensitive forecasting to estimate how price impacts demand, TPO helps users to maximize revenues, profits, or market share.

Abstract: Not all facts in a data warehouse are described by the same set of dimensions. However, there can be associations between the data dimensions and other dimensions. By maintaining a set of relationships that are capable of linking the dimensional keys used in existing data to the keys of an associated dimension, a data transformation can be constructed that summarizes by the original and by the associated dimensions in feeds in an analytical data mart (cube) that includes all the dimensions. This cube can then be consolidated and analyzed in a slice-and-dice fashion as though all the dimensions were independent. Data transformed in this manner can be analyzed alongside data from a source that is keyed by all of the dimensions.