By Yasui Y.
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Extra resources for A data-analytic strategy for protein biomarker discovery profiling of high-dimensional proteomic dat
3 The issue of nonvolatility. exhibits a very different set of characteristics. Data warehouse data is loaded (usually en masse) and accessed, but it is not updated (in the general sense). Instead, when data in the data warehouse is loaded, it is loaded in a snapshot, static format. When subsequent changes occur, a new snapshot record is written. In doing so a history of data is kept in the data warehouse. The last salient characteristic of the data warehouse is that it is time variant. Time variancy implies that every unit of data in the data warehouse is accurate as of some one moment in time.
13. 13 shows that the operational environment is supported by the classical systems development life cycle (the SDLC). The SDLC is often called the “waterfall” development approach because the different activities are specified and one activity-upon its completion-spills down into the next activity and triggers its start. The development of the data warehouse operates under a very different life cycle, sometimes called the CLDS (the reverse of the SDLC). The classical SDLC is driven by requirements.
The SDLC assumes that requirements are known at the start of design (or at least can be discovered). In the world of the DSS analyst, though, new requirements usually are the last thing to be discovered in the DSS development life cycle. The DSS analyst starts with existing requirements, but factoring in new requirements is almost an impossibility. A very different development life cycle is associated with the data warehouse. The Development Life Cycle We have seen how operational data is usually application oriented and as a consequence is unintegrated, whereas data warehouse data must be integrated.