Abstract

The Indian Materials Database (IMDB) is a national project aiming to develop a database through compilation of materials property data available in different laboratories in India. The database contains data on mechanical, corrosion, nondestructive evaluation, thermal and optical properties of a wide variety of materials. Selecting the appropriate data modeling technique is crucial for the successful deployment of such a database. Dimensional modeling is a logical design technique to present data in a standard, intuitive framework that allows for high-performance access. Dimensional modeling of data results in a ‘Star Schema’, where the data constitutes a central fact table surrounded by dimension tables. This paper discusses the model and architecture of the material database using a ‘Snowflake Schema’, which is a variation of ‘Star Schema’, where some of the dimensions are normalized into multiple related tables. The database contains a central fact table linked to multiple dimensions namely, 1) Materials 2) Properties of materials 3) Details of experiments conducted on materials and 4) Source from which data is obtained.

References

• S. Rajeswari, S.A.V. Satya Murty, Baldev Raj et al. (2010), Challenges in designing a database for corrosion and mechanical properties of materials, In Proceedings of the Second Asian Symposium for Materials Database, Sanya, China. pp. 339-344.
• W.H. Inmon (1993), Building the Data Warehouse, Wiley.
• R. Kimball (1997), A Dimensional Modeling Manifesto http://ralph12.securesites.net/html/articles_search/articles1997/9708d15.html Accessed 23 Dec 2010.
• R. Kimball, M. Ross, W. Thornthwaite, and L. Reeves. (1998), The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses, Wiley.
• R. Kimball, M. Ross, W. Thornthwaite, J. Mundy and B. Becker. (1998), The Data Warehouse Lifecycle Toolkit, Wiley.
• M.A. Kortini and L.D. Moody (2000), From Enterprise Models to Dimensional Models: A Methodology for Data Warehouses and Data Mart Design, Proceedings of the International Workshop on Design and Management of Data Warehouses, Stockholm, Sweden. 5.1-5.12.
• M. Golfarelli, D. Maio, and S. Rizzi. (1998), The dimensional fact model: a conceptual model for data warehouses, International Journal of Cooperative Information Systems, Volume 7. pp. 215-247.
• M. Golfarelli, D. Maio, and S. Rizzi (1998), Conceptual Design of Data Warehouses from E/R Schema, In Proceedings of the Thirty-First Annual Hawaii international Conference on System Sciences, Volume 7. pp.334.
• I.Y. Song, W. Rowen, C. Medsker and E. Ewen. (2001), An Analysis of Many-to-Many Relationships Between Fact and Dimension Tables in Dimension Modeling, Proceedings of the International Workshop on Design and Management of Data Warehouses, Interlaken, Switzerland. pp. 6.1-6.13.
• I.Y. Song and M.E. Jones (2008), Dimensional modeling: Identification, classification and evaluation of patterns, Decision Support Systems, Volume 45, Issue 1. pp. 59-76.
• T. Martyn (2004), Reconsidering Multi-Dimensional Schemas, ACMs Special Interest Group on Management of Data, Volume 33, Issue 1. pp. 83-88.
• M. Levene and G. Loizou (2003), Why is the Snowflake Schema a good data warehouse design, Information Systems, Volume 28, Issue 3. pp. 225–240.
• Xiu-Juan Zhang, Ke-Zhang Chen, Xin-An Feng (2008), Material database for the material design of components made of a multiphase perfect material, Materials & Design, Volume 29, Issue 6. pp. 1131-1144
• P.S. Ramalhete, A.M.R. Senos, C. Aguiar, Digital tools for material selection in product design , Materials & Design, Volume 31, Issue 5, May 2010. pp. 2275-2287
• P.J. Karditsas, G. Lloyd, M. Walters, A. Peacock (2006) , The European Fusion Material properties database , Fusion Engineering and Design, Volume 81, Issues 8-14. pp. 1225-1229
• Vassiliadis P (1998), Modeling Multidimensional Databases, Cubes and Cube Operations, In Proceedings of the 10th International Conference on Scientific and Statistical Database Management (SSDBM '98), IEEE Computer Society. pp. 53-62.