The data is available in abundance in any business
organization. It includes the records for finance, maintenance,
inventory, progress reports etc. As the time progresses, the data keep
on accumulating and the challenge is to extract the information from
this data bank. Knowledge discovery from these large and complex
databases is the key problem of this era. Data mining and machine
learning techniques are needed which can scale to the size of the
problems and can be customized to the application of business. For
the development of accurate and required information for particular
problem, business analyst needs to develop multidimensional models
which give the reliable information so that they can take right
decision for particular problem. If the multidimensional model does
not possess the advance features, the accuracy cannot be expected.
The present work involves the development of a Multidimensional
data model incorporating advance features. The criterion of
computation is based on the data precision and to include slowly
change time dimension. The final results are displayed in graphical
form.
[1] P. Adriaans and D. Zantinge "Data Mining", Pearson Education, USA,
2002, pp. 1-200.
[2] Alexandros Karakasidis "ETL queues for active data warehousing"
Sixth International Conference on Extending Database Technology,
USA, 2005, pp. 153-165.
[3] A. L. P. Chen, J-S. Chiu and F. S. C. Tseng, "Evaluating Aggregate
Operations over imprecise Data", IEEE Transactions on Knowledge
and Data Engineering, Vol8, 1996, pp.273-284.
[4] C. Bettini, C. E. Dyreson, W. S. Evans, R. T. Snodgrass, X. S. Wang,
"A Glossary of Time granularity Concepts", In Temporal Databases:
Research and Practice, 1998, pp. 406-413.
[5] C. Li and X. S. Wang, "A Data Model for Supporting On-Line
Analytical Processing" Fifth International Conference on Information
and Knowledge Management, 1996, pp. 81-88.
[6] Chang-Sub Park, young Ho Kim, Yoon-Joon Lee, "Rewriting OLAP
Queries using Materialized Views and Dimension Hierarchies in Data
warehouses" IEEE, 2001, pp. 515-523.
[7] Daniel A. Keim, Hans-Peter Kriegel, "Visualization technique for
Mining Large databases: A Comparison", IEEE Transaction on
Knowledge and Data Engineering,Vol 8., 1996, pp.923-938.
[1] P. Adriaans and D. Zantinge "Data Mining", Pearson Education, USA,
2002, pp. 1-200.
[2] Alexandros Karakasidis "ETL queues for active data warehousing"
Sixth International Conference on Extending Database Technology,
USA, 2005, pp. 153-165.
[3] A. L. P. Chen, J-S. Chiu and F. S. C. Tseng, "Evaluating Aggregate
Operations over imprecise Data", IEEE Transactions on Knowledge
and Data Engineering, Vol8, 1996, pp.273-284.
[4] C. Bettini, C. E. Dyreson, W. S. Evans, R. T. Snodgrass, X. S. Wang,
"A Glossary of Time granularity Concepts", In Temporal Databases:
Research and Practice, 1998, pp. 406-413.
[5] C. Li and X. S. Wang, "A Data Model for Supporting On-Line
Analytical Processing" Fifth International Conference on Information
and Knowledge Management, 1996, pp. 81-88.
[6] Chang-Sub Park, young Ho Kim, Yoon-Joon Lee, "Rewriting OLAP
Queries using Materialized Views and Dimension Hierarchies in Data
warehouses" IEEE, 2001, pp. 515-523.
[7] Daniel A. Keim, Hans-Peter Kriegel, "Visualization technique for
Mining Large databases: A Comparison", IEEE Transaction on
Knowledge and Data Engineering,Vol 8., 1996, pp.923-938.
@article{"International Journal of Information, Control and Computer Sciences:52955", author = "Manpreet Singh and Parvinder Singh and Suman", title = "Conceptual Multidimensional Model", abstract = "The data is available in abundance in any business
organization. It includes the records for finance, maintenance,
inventory, progress reports etc. As the time progresses, the data keep
on accumulating and the challenge is to extract the information from
this data bank. Knowledge discovery from these large and complex
databases is the key problem of this era. Data mining and machine
learning techniques are needed which can scale to the size of the
problems and can be customized to the application of business. For
the development of accurate and required information for particular
problem, business analyst needs to develop multidimensional models
which give the reliable information so that they can take right
decision for particular problem. If the multidimensional model does
not possess the advance features, the accuracy cannot be expected.
The present work involves the development of a Multidimensional
data model incorporating advance features. The criterion of
computation is based on the data precision and to include slowly
change time dimension. The final results are displayed in graphical
form.", keywords = "Multidimensional, data precision.", volume = "1", number = "12", pages = "3808-6", }