Data models

An APS planning model contains master data and dynamic data. 

All data objects contain an identification key and structured information. Materials and resources can be defined by relatively simple property tables. Master recipes require more complex structures to describe which resources have to be used at which time interval by which operation and with which operation parameters, and which materials are needed or produced at which point in time and in which quantity. 

Material flow and resource allocation can be defined by time, duration, type and quantity in the planning model. They describe a definite (by best current knowledge) change of the planning model in the future. In addition to this there are a number of fuzzy information data that have to be included in the planning model but are only weak assumptions about the future planning situation. These include, e.g., planned orders and planned independent requirements. 

Planned orders are place holders for process orders that have yet to be checked for planning feasibility by detailed scheduling. In a hierarchical planning model they are interpreted as a hint to the details planner that they should create and schedule a process order. They are often the result of an automated MRP (material requirements planning) run that is based on planned independent requirements and does not consider resource capacities. 

A planned independent requirement is a planned requirement quantity for a product for a certain period of time. It is not necessarily created on the basis of any customer requirement. 

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Coordinate atomic coordinate data MODEL, AEOM, SIC ATOM... 

The primal advantage of hierarchical databases is that the relationship between the data at the different levels is easy. The simplicity and efficiency of the data model is a great advantage of the hierarchical DBS. Large data sets (scries of measurements where the data values are dependent on different parameters such as boiling point, temperature, or pressure) could be implemented with an acceptable response time. 

After intakes have been estimated, they arc organized by population, as appropriate. Then, tlie sources of uncertainty (e.g., variability in analytical data, modeling results, parameter assumptions) and their effect on tlie exposure estimates are evaluated and sunuiumzed. Tliis information on uncertainty is important to site decision-makers who must evaluate tlie results of the e.xposure... 

Data — Model — Prediction — Further Data — Adjustment of the... 

Lombardo F, Gifford E and Shalaeva MY. In silico ADME prediction data, models, facts and myths. Mini Rev Med Chem 2003 3 861-75. 

Such conformational dependence presents challenges and an opportunity. The challenges he in properly accounting for its consequences. In many cases, exact conformational energetics and populations in a sample may be unknown, and the nature of the sample inlet may sometimes also mean that a Boltzmann distribution cannot be assumed. Introducing this uncertainty into the data modeling process produces some corresponding uncertainty in the theoretical interpretation of data... 

Models have been formulated to enable the simulation of the concentration vs. radial distance profile as it develops with time, from which the time-dependent concentration vs. distance, d, profile, observed at the probe, can be extracted for comparison with experimental data. Models based on Eqs. (29) and (30) give similar results for conditions encountered practically. 

Jouzel J, G. Hoffmann G, Koster RD, Masson V (2000) Water isotopes in precipitation data/model comparison for present-day and past chmates. Quat Sci Rev 19 363-379 Kagan E, Agnon A, Bar-Matthews M, Ayalon A (2002) Cave deposits as recorders of paleoseismicity A record from two caves located 60 km west of the Dead Sea Transform (Jerusalem, Israel). In Environmental Catastrophes and Recoveries in the Holocene (onhne abstract http //atlas-conferences.com/cgi-bin/abstract/caiq-38)... 

The Clinical Data Interchange Standards Consortium (CDISC) is a non-profit group that defines clinical data standards for the pharmaceutical industry. CDISC has developed numerous data models that you should familiarize yourself with. Four of these models are of particular importance to you ... 

Operational Data Model (ODM). The ODM is a powerful XML-based data model that allows for XMF-based transmission of any data involved in the conduct of clinical trials. SAS has provided support for importing and exporting ODM files via the CDISC procedure and the XML LIBNAME engine. 

You will be exporting, importing, and creating data for these models, so it is important that you learn about them. The FDA has begun to formally endorse the use of these data models in their guidance. Eventually the FDA will probably require data to be formatted to the CDISC model standards for regulatory submissions. 

Operational Data Model (ODM) for clinical data interchange... 

Denormalization of data is needed when a statistical procedure requires that the information to be analyzed must be on the same observation. Procedures in SAS that perform data modeling are often the ones that require denormalized data, as they require that the dependent variable be present on the same observation as the independent variables. For example, imagine that you are trying to determine a mathematical model that predicts under what conditions a therapy is successful. That model might look like this ... 

Filters are designed to remove unwanted information, but do not address the fact that processes involve few events monitored by many measurements. Many chemical processes are well instrumented and are capable of producing many process measurements. However, there are far fewer independent physical phenomena occurring than there are measured variables. This means that many of the process variables must be highly correlated because they are reflections of a limited number of physical events. Eliminating this redundancy in the measured variables decreases the contribution of noise and reduces the dimensionality of the data. Model robustness and predictive performance also require that the dimensionality of the data be reduced. 

Swanson, L. W. (1998). Brain Maps Structure of the Rat Brain a Laboratory Guide with Printed and Electronic Templates for Data, Models, and Schematics. New York, NY Elsevier. 

In the independence approach the planning functions are stripped from the old-fashioned ERP system and a modem complete APS system is added as a separate server system with an independent persistent data model and integrated by an interface (Fig. 12.2). Some users of the ERP system also use this separate APS system. This approach is supported, e.g., by the software products SAP APO resp. 

In the embedding approach the ERP system is enhanced with subordinate planning systems that are integrated into the user interface of the ERP system and create a local temporary data storage (LiveCache). All data is still held persistently only in the ERP system (Fig. 12.3). This allows for the LiveCache to use a projection of the ERP data model that is more suitable for APS purposes. This approach is used, e.g., by the software product OR Soft SCHEDULE++. 

The independence approach allows for a good encapsulation of the APS issue but requires major changes in business processes. It requires the creation and maintenance of an independent data model with its own data structure and the definition of new business processes. Introduction of the APS system must be done in a big bang. Integration is not fully guaranteed, its quality depends on the throughput and the error tolerance of the integration interface. 

The embedding approach may require an improvement of modeling in the ERP system (i.e., to maintain additional detailed information for APS purposes) but it can utilize all established business processes, data models and infrastructures. Introduction of the subordinate planning system can be done step-by-step with minimum impact on established business processes. Integration is fully guaranteed... 

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