Abstract
As the data warehouse is the core framework of a Business Intelligence system, changes to the business model at stake also imply changes to the applied data model, which require specialized maintenance and architecture operations, such as: halting the system, data warehouse redesign and reimplementation, changes to loading processes and information retrieval logic, tests, reloading of
data and system rebooting.
Considering time, risk and cost implied in these operations, strongly related to data model rigidity and complexity, it seems advisable to seek streamlining of change processes, by framing a new simple, safe and generalizable data model.
Aiming at this purpose, after reviewing existing data model concepts, and by focusing research on a specific need of the pharmaceutical industry, a new model (ZeEN - Zero Effort Entity-Network) is presented here, which was succesfully benchmarked against traditional relational and dimensional models and Anchor Modeling recent approach, for performance, and implementation and maintenance complexity.
From the experiment, conclusions are drawn over Business Intelligence generic needs, and future work is suggested.
data and system rebooting.
Considering time, risk and cost implied in these operations, strongly related to data model rigidity and complexity, it seems advisable to seek streamlining of change processes, by framing a new simple, safe and generalizable data model.
Aiming at this purpose, after reviewing existing data model concepts, and by focusing research on a specific need of the pharmaceutical industry, a new model (ZeEN - Zero Effort Entity-Network) is presented here, which was succesfully benchmarked against traditional relational and dimensional models and Anchor Modeling recent approach, for performance, and implementation and maintenance complexity.
From the experiment, conclusions are drawn over Business Intelligence generic needs, and future work is suggested.
Original language | Portuguese |
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Qualification | Master of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 3 Jul 2013 |
Publication status | Published - 3 Jul 2013 |
Keywords
- Database
- Data warehouse
- Data modeling
- Business Intelligence
- Normalization
- Customer Relationship Management