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tracts with a System Developer (role). Within the contractor s (System Developer role) program organization, a PRODUCT level team (Acquirer role) allocates requirements to Development Team (System Developer role) to develop and deliver a SUBSYSTEM. The SUBSYSTEM development team, acting as an Acquirer (role), may procure some of the SUBSYSTEM s components from various vendors (System Developer roles). 4. For simplicity, the description addresses a one-time procurement for a system, product, or service. Some complex system development efforts may consist of a series spiral development deliverables or contracts. In these cases, the requirements may be initially immature thereby necessitating stages of development to mature the requirements to a level necessary and suf cient for nal system development. Thus the SE Process Model is reapplied to all levels of abstraction for each iteration of the spiral. These approaches serve to reduce system development risk.

The rst step of the SE Process Model is to simply understand the entity s opportunity/problem and solution spaces. System analysts and SEs need to understand and validate: HOW the User intends to use the system as well as WHAT expectations are levied on the entity to achieve higher-level mission objectives. This requires understanding: 1. The entity s contextual role in the next higher level solution space namely the User s level 0 system or the deliverable SYSTEM, PRODUCT, SUBSYSTEM, SUBASSEMBLY, and other levels. 2. HOW the User plans to deploy, operate, support, and dispose of the system namely use cases and scenarios. 3. The system s interfaces with external systems in its OPERATING ENVIRONMENT such as HUMAN-MADE, NATURAL, and INDUCED ENVIRONMENTS. 4. The system s mission event timeline (MET) or allocations. 5. The expected outcomes from system interactions with its OPERATING ENVIRONMENT. 6. Products, by-products, and services comprising the system outputs required to accomplish those outcomes.

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The relationship between physical and logical systems becomes especially important in fault and configuration management, when dealing with a failing blade or other component. Management tools are available that provide the ability to power off the failing blade, reprovision the tasks that were running on that blade to another blade, and when the failing blade has been swapped, to bring the new blade online. (If the blades are running within a virtualized environment, virtualization software functions allow the new blade to be added into a pool of blades, simplifying the reprovisioning process.)

Information in bioinformatics has a number of characteristics that fit well with the facilities provided by object orientation: Order is important. Much of the most important biological information involves ordering. For example, basic DNA or protein sequence information is intrinsically ordered. However, orderings also exist among higher level concepts, such as secondary structure elements in proteins, or genes in a chromosome. As a result, it is important that data models for biological data are able to represent ordered data

You will often hear people refer to a system s attributes, properties, and characteristics. To the casual observer who researches the de nitions of these terms, most dictionaries de ne these terms by referencing the other. For purposes of our discussions, we will employ the following as a means of delineating the differences between the terms.

The term attributes classi es functional or physical features of a system. Examples include gender; unit cost; nationality, state, and city of residence; type of sport; organizational position manager; and xed wing aircraft versus rotor.

The term, properties, refers to the mass properties of a system. Examples include composition; weight; density; and size such as length, width, or height.

directly The fact that the relational model contains no explicit mechanism for describing ordered collections has been a significant impediment to its use in bioinformatics Schemas are complex Although many biological analyses essentially involve fairly exhaustive searches over raw sequences, the description of associated structural, organizational, or functional information tends to give rise to complex schemas For example, a schema for describing protein structure data or for describing the organization of a genome involves many object types and relationships among them Furthermore, there are often hierarchical relationships (part of and is-a) within biological databases that can also benefit from explicit representation in data models Analyses are complex Although there is a role for ad-hoc queries expressed using declarative query languages in biological databases, in practice, query language interfaces are generally less important than programming interfaces.

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