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|GDB||Dated: Feb 17, 15|
Please Note that GDB of CS614 will be launched on Monday, February 23, 2015 (23-February-2015) and will last for 48 hours only. The GDB will start on 23rd of February 2015 and end on 24th of February 2015. The Topic for GDB is given below,
Various universities are using information management systems that stores students, administrative and faculty information. The data of university management information system is available at each university level and there is a need to develop a data warehouse for the purpose of analysis and development of decision support environment. For the above purpose, HEC is planning to develop a data warehouse of universities information management system. In order to develop the data warehouse; the following implementation strategies can be used:
1. Top down approach
2. Bottom up approach
Keeping in view the requirements of the data warehouse and characteristics of the above mentioned approaches which approach you considers as the best approach that HEC should opt for data warehouse implementation. Justify your answer with reasons. Your answer must be to the point and precise.
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i think top down approach is better for given scenario
Tell me why its top down approach is better ?
sis solution b post kr den please
post solution here dear
oka will try to giv hints.
Top Down & Bottom Up approach: A Top Down approach is generally useful for
projects where the technology is mature and well understood, as well as where the
business problems that must be solved are clear and well understood. A Bottom Up
approach is useful, on the other hand, in making technology assessments and is a good technique for organizations that are not leading edge technology implementers. This
approach is used when the business objectives that are to be met by the data warehouse
are unclear, or when the current or proposed business process will be affected by the data
Thank you for this obvious diagram :P
The objective is clear, it is centralized. It is top down.
Bottom up approach is more suitable...!!
Top-Down vs. Bottom-Up
When you consider methodological approaches, their top-down structures or bottom-up structures play a basic role in creating a data warehouse. Both structures deeply affect the datawarehouse lifecycle.
If you use a top-down approach, you will have to analyze global business needs, plan how to develop a data warehouse, design it, and implement it as a whole. This procedure is promising: it will achieve excellent results because it is based on a global picture of the goal to achieve, and in principle it ensures consistent, well integrated data warehouses. However, a long story of failure with top-down approaches teaches that:
high-cost estimates with long-term implementations discourage company managers from embarking on these kind of projects;
analyzing and bringing together all relevant sources is a very difficult task, also because it is not very likely that they are all available and stable at the same time;
it is extremely difficult to forecast the specific needs of every department involved in a project, which can result in the analysis process coming to a standstill;
since no prototype is going to be delivered in the short term, users cannot check for this project to be useful, so they lose trust and interest in it.
In a bottom-up approach, data warehouses are incrementally built and several data marts are iteratively created. Each data mart is based on a set of facts that are linked to a specific company department and that can be interesting for a user subgroup (for example, data marts for inventories, marketing, and so on). If this approach is coupled with quick prototyping, the time and cost needed for implementation can be reduced so remarkably that company managers will notice how useful the project being carried out is. In this way, that project will still be of great interest.
The bottom-up approach turns out to be more cautious than the top-down one and it is almost universally accepted. Naturally the bottom-up approach is not risk-free, because it gets a partial picture of the whole field of application. We need to pay attention to the first data mart to be used as prototype to get the best results: this should play a very strategic role in a company. In fact, its role is so crucial that this data mart should be a reference point for the whole data warehouse. In this way, the following data marts can be easily added to the original one. Moreover, it is highly advisable that the selected data mart exploit consistent data already made available.
i think bottom-up approch is best......me i right??