Abstract
Recent developments in Big Data in financial industry has created a huge opportunity for design and development of effective aggregation (higher level) analytical measures (Fund, Portfolio, Sector, Industry etc.). Lack of these aggregated measures will jeopardize organization’s ability to provide the financial services promised to clients. Vendor solutions and existing academic research (Data Cube, OLAP) can provide these aggregated measures but are expensive, time consuming and not practical to implement for a small to mid-size investment organization. Our proposed solution using rule-based architecture is cost effective, efficient and building block for “Rapid Application and Decision Support Systems on Big Data”. Our new approach “Selective Dimensional Cuboids” provides a simple but robust solution with flexibility for future expansion into data mining, portfolio trend analysis and cycle forecasting. The solution is easily portable to any dimensional data set.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Fabozzi, F. (2007). Fixed income analysis, CFA institute investment series (2nd ed.). New York: Wiley.
Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques (3rd ed.). New York: Morgan Kaufmann.
Han, J., Pei, J., Dong, G., & Wang, K. (2001). Efficient computation of iceberg cubes with complex measures. In Proceedings of the 2001 ACM SIGMOD international conference on Management of data, May 21–24, 2001. Santa Barbara, California, USA, pp. 1–12. https://doi.org/10.1145/375663.375664
Fang, M., Sivakumar, N., Garcia-Molina, H., Motwani, R., & Ulman, J. (1998). Computing iceberg queries efficiently. In Proceedings of 24th VLDB conference, New York, USA
Gary, J., Bosworth, A., Layman, A., & Pirahesh, H. (1996). Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals, Microsoft, IBM, 1063-6382/96 IEEE
Elmasri, R., & Navathe, S. (2015). Fundamentals of data base systems (7th ed.). Pearson: Addison Wesley.
Harinarayan, V., Rajaram, A., & Ullman, J. (1996). Implementing data cubes efficiently. In SIGMOD ‘96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data (pp. 205–216). Hill, ISBN 978-0-07-179053-6
Microsoft SQL Server. (2012). SQL server 2012 tutorials: Analysis services—Multidimensional modeling.
Agarwal S., Agrawal, R., Deshpande, P. M., Gupta, A., Naughton, J., Ramakrishnan, R., & Sarawagi, S. (1996). On the computation of multidimentional aggregates. In Proceedings of the international conference on very large databases (pp. 506–521).
Ahmed, M., Mahmood, A. N., & Islam, M. R. (2016). future generation computer systems. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2015.01.001.
Esling, P., & Agon, C. (2012). Time series data mining. In ACM computing surveys (Vol 45 No., Article 12+).
Eaton, C., Deroos, D., Deutsch, T., Lapis, G., & Zikopoulos, P. (2011). Understanding big data analytics for enterprise class hadoop and streaming data. New York: McGraw-Hill.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Casturi, R., Sunderraman, R. Cost effective, rule based, big data analytical aggregation engine for investment portfolios. Wireless Netw 28, 1203–1209 (2022). https://doi.org/10.1007/s11276-018-01904-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-018-01904-5