Code generator for integrating warehouse data sources.
2001
- 337Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage337
- Abstract Views183
- Downloads154
Thesis / Dissertation Description
Data warehouse is a large integrated database organized around major subjects of an enterprise for the purpose of decision support querying. Many enterprises are creating their own data warehouse systems from scratch in different varying formats, making the issue of building a more efficient, more reliable, cost-effective and easy-to-use data warehouse system important. Building a code generator for creating a program that automatically integrates different data sources to target data warehouse is one solution. Thus, understanding approaches for integrating warehouse data sources is a key to the success of data warehouse code generation. Integrating warehouse data sources involves building a code generator program for creating both data warehouse and metadata using novel techniques for extracting and cleaning data. Many types of data sources like e-commerce databases, web databases and knowledge bases can also be accommodated in the code generator in the future. There is little or no literature showing the use of the newest integration techniques in code generator for data warehouse data integration. This thesis aims at employing new techniques for both data integration and code generation, in building a code generation tool for data warehouse data integration. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .L575. Source: Masters Abstracts International, Volume: 40-03, page: 0725. Adviser: C. Ezeife. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
Bibliographic Details
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