Abstract
In the new age, digital data is the most important source of acquiring knowledge. For this purpose, collect data from various sources like websites, blogs, webpages, and most important databases. Database and relational databases both provide help to decision making in the future work. Nowadays these approaches become time and resource consuming there for new concept use name data warehouse. Which can analyze many databases at a time on a common plate from with very efficient way. In this paper, we will discuss the database and migration from the database to the data warehouse. Data Warehouse (DW) is the special type of a database that stores a large amount of data. DW schemas organize data in two ways in which star schema and snowflakes schema. Fact and dimension tables organize in them. Distinguished by normalization of tables. Nature of data leads the designer to follow the DW schemas on the base of data, time and resources factor. Both design-modeling techniques compare with the experiment on the same data and results of applying the same query on them. After the performance evaluation, using bitmap indexing to improve the schemas performance. We also present the design modeling techniques with respect to data mining and improve query optimization technique to save time and resource in the analysis of data.
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References
Sidi, E., El, M., Amin, E.: Star schema advantages on data warehouse: using bitmap index and partitioned fact tables. Int. J. Comput. Appl. 134(13), 11–13 (2016)
Jan, B., Alharbi, M., Mujeeb-ur-rehman, Khan, F.A., Imran, M., Ahmad, A.: Efficient data access and performance improvement model for virtual data warehouse. Sustain. Cities Soc. 35, 232–240 (2017)
Yusuf, A.: A design comparison: data warehouse schema versus conventional relational database schema. In: CEUR Workshop Proceedings (2016)
North, M., Thomas, L., Richardson, R., Akpess, P.: Data warehousing: a practical managerial approach. Comput. Sci. Inf. Technol. 5, 18–26 (2017)
Angelini, M., Catarci, T., Mecella, M., Santucci, G.: The visual side of the data. In: Flesca, S., Greco, S., Masciari, E., Saccà , D. (eds.) A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years. SBD, vol. 31, pp. 3–25. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61893-7_1
Flesca, S., Greco, S., Masciari, E., Saccà , D. (eds.): A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years. SBD, vol. 31. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61893-7
Abdalaziz Ahmedl, R., Mohamed Ahmed, T.: Generating data warehouse schema. Int. J. Found. Comput. Sci. Technol. 4(1), 1–16 (2014)
Sandhu, M.K., Kaur, A., Kaur, R.: Data warehouse schemas. Int. J. Innov. Res. Adv. Eng. (IJIIRAE) 2, 47–51 (2015)
Cherniack, M., Lawande, S., Tran, N.: Optimizing snowflake schema queries (2014)
Priyadharsini, C., Thanamani, D.A.S.: An overview of knowledge discovery database and data mining techniques. Int. J. Innov. Res. Comput. Commun. Eng. 2(1), 1571–1578 (2014)
Ristoski, P., Paulheim, H.: Feature selection in hierarchical feature spaces. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS (LNAI), vol. 8777, pp. 288–300. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11812-3_25
Maimon, O., Rokach, L.: Introduction to Knowledge Discovery and Data Mining, pp. 1–15 (2016)
Pavya, K., Srinivasan, D.B.: Feature selection techniques in data mining: a study. Int. J. Sci. Dev. Res. 2(6), 594–598 (2017)
Maimon, O., Rokach, L.: Data Mining and Knowledge Discovery Handbook, pp. 1–15. Springer, Boston (2005). https://doi.org/10.1007/b107408
Golfarelli, M., Rizzi, S.: From star schemas to big data: 20+ years of data warehouse research. In: Flesca, S., Greco, S., Masciari, E., Saccà , D. (eds.) A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years. SBD, vol. 31, pp. 93–107. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61893-7_6
Bhide, M.A., Mittapalli, S.K., Padmanabhan, S.: Star and snowflake schemas in extract, transform, load processes (2016)
Sidi, E., El, M., Amin, E.: The impact of partitioned fact tables and bitmap index on data warehouse performance. Int. J. Comput. Appl. 135, 39–41 (2016)
Difference Between Star and Snowflake Schema. https://techdifferences.com/difference-between-star-and-snowflake-schema.html
Benjelloun, M., El, M., Amin, E.: Impact of using snowflake schema and bitmap index on data warehouse querying. Int. J. Comput. Appl. 180(15), 33–35 (2018)
Dageville, B., et al.: The snowflake elastic data warehouse. In: SIGMOD/PODS 2016, San Francisco, CA, USA, 26 June–01 July 2016 (2016)
Difference between snowflakes schema and star schema (2016). https://techdifferences.com/difference-between-star-and-snowflake-schema.html#
Cheng, X., Schneider, P.: Star and snowflake join query performance (2017)
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Iqbal, M.Z., Mustafa, G., Sarwar, N., Wajid, S.H., Nasir, J., Siddque, S. (2020). A Review of Star Schema and Snowflakes Schema. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_12
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