Decision Support System
Data warehouse revolutionized automated knowledge extraction systems. It enabled extracting knowledge from a reasonable size of datasets that are created by integrating data from different sources and organize them in a single store by undergoing a process that consists of extraction, loading, and transformation (better known as ELT). Even though Big Data is heard today more times than data warehousing, the market share still is in favor of warehousing. Most of the organizations still uses data warehousing for their data processing. This course covers concepts, algorithms, and technologies of data warehousing. It also covers a wide range of models, modeling techniques such as indexing that are used in building an efficient decision support systems (DSS). Additionally, it covers various tools that are available for developing DSS. During the course a real-world data warehousing scenario will be simulated.
- Provide a solid background on concepts, techniques, algortihms of data warehousing, data integration and analysis online (OLAP).
- Provide a strong background of data warehousing design approaches (ETL), data cleansing, data models, metadata and data maintenance.
- Different data integration approaches including GAV, LAV, BAV will be discussed.
- Provide a comprehensive detail of state of the art which includes a wide number of tools for developing DSS.
- OLAP approach, with the construction of data cubes, indexing and optimization of access will be detailed.
What is in it for the Participants?
- Learning the fundamentals of decision support systems.
- Learning concepts, techniques, and algorithms of data warehousing.
- Learning concepts, techniques, and algorithms of data imtegration.
- Learning concepts, techniques, and algorithms of online analysis.
- Learning the technologies of the decision support system.
- Being able to contribute in designing and developing decision support system.