Dimensional analysis based causal ordering
Conference Proceeding
Shen, Q., Peng, T., & Milne, R. (1998)
Dimensional analysis based causal ordering. In Proceedings of the 13th International Workshop on Qualitative Reasoning, 193-202
This paper presents a novel approach for generating causal dependencies between system variables, from an acausal description of the system behaviour, and for identifying the ...
Best practice for implementing a data warehouse: a review for strategic alignment.
Conference Proceeding
Weir, R., Peng, T., & Kerridge, J. (2003)
Best practice for implementing a data warehouse: a review for strategic alignment. In Design and Management of Data Warehouses 2003: Proceedings of the 5th Intl. Workshop DMDW'2003, Berlin, Germany, September 8, 2003
A review of literature pertaining to data warehouse implementations over the last eight years has been undertaken. It was found that the views of data warehouse practitioners ...
Improving data quality in data warehousing applications
Conference Proceeding
Li, L., Peng, T., & Kennedy, J. (2010)
Improving data quality in data warehousing applications. In J. Filipe, & J. Cordeiro (Eds.), Proceedings of the 12th International Conference on Enterprise Information Systems, 379-382. https://doi.org/10.5220/0002903903790382
There is a growing awareness that high quality of data is a key to today’s business success and dirty data that exits within data sources is one of the reasons that cause poor...
A discrete formalism for reasoning about action and change
Conference Proceeding
Knight, B., Ma, J., & Peng, T. (1996)
A discrete formalism for reasoning about action and change. In R. Adey, G. Rzevski, & R. Teti (Eds.), Applications of artificial intelligence in engineering XIIdoi:10.2495/AI970101
No abstract available.
A tool for generating synthetic data
Conference Proceeding
Peng, T., & Telle, A. (2018)
A tool for generating synthetic data. In DATA '18 Proceedings of the First International Conference on Data Science, E-learning and Information Systemshttps://doi.org/10.1145/3279996.3280018
It is popular to use real-world data to evaluate data mining techniques. However, there are some disadvantages to use real-world data for such purposes. Firstly, real-world da...