Abstract: An analyzing device requires regular maintenance for maintaining a predetermined analyzing performance thereof. However, the more complex the analyzing device becomes, the more number of maintenance items are provided. For example, if a plurality of types of maintenances to be performed by removing same component, efficiency may be improved by performing the same as a whole. However, when conforming with a manual, if recommended timings to perform the maintenance are shifted delicately, there is a probability of occurrence of a case where the same components are removed several times for maintenance during a short time. At the time when an instruction to perform maintenance is issued, maintenance items to be performed are extracted, the extracted maintenance items are sorted into a recommended performance order in conformity to a preset rule such that the similar maintenance or the maintenance of the same portion or the like are performed continuously.

Abstract: Embodiments leverage capabilities of an in-memory database to manage measurement and modeling of Operational Leading Indicators (OLIs). An OLI template may comprise four model components: ratio calculation; factor calculation; predictive modeling; and cost estimation. Each model component is motivated and explained in terms of information sources, and analytical or statistical modelling tasks used in its definition. Embodiments combine analytical and statistical modelling utilizing in-memory computing, to process large amounts of unmodified source data, calculate cost measures rapidly without preaggregation, and/or run linear regression models on the same data set and in the same memory space without a need for separate hardware. An engine in communication with the in-memory database that comprises a large volume of available data, is configured to receive values for OLI factors as inputs.