Data Cube Representation for patient Diagnosis System Using Fuzzy Object-Oriented Database
AbstractIn the current scenario, everyone wants to store and fetch the information in an easy and faster way. Therefore, the data cube is one of the leading tools in these days that facilitate the user to store and retrieve the decision support information in a faster manner with ease. In this paper the patient diagnostic system (PDS) is proposed for the patient who is suffered from the several types of fever and modeling of fuzzy object-oriented database. An attempt is made to design a three dimensional data cube for the fuzzy object-oriented database for storing the vague or imprecise information in it. A class, sequence and activity diagrams are also designed for the graphical representation of the proposed work through the well known modeling language i.e. Unified Modeling Language (UML).
(1) Saxena V., Ansari A. G. and Kumar K., Data Cube Representation of Patient Registration System through UML, International Journal of Computer Science and Network Security, Vol. 8, No. 10, October 2008.
(2) Dev H. and Mishra K. S., Design of Data Cubes and Mining for Online Banking System, International Journal of Computer Applications, Vol. 30, No. 3, September 2011.
(3) Hsieh J. M., Chen S. M. and Yu. S. P. 2005. Integrating DCT and DWT for approximating Cube Streams. In Proceedings of the 14th ACM International conference on Information and Knowledge Management (CIKM), pp 179-186.
(4) Li C., Cong G., Tung H. K. A. and Wang S. 2004. Incremental Maintenance of Quotient Cube for Median. In Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp 226-235.
(5) Malvestuto M. F., A Join-Link Operator to Combine Data Cubes and Answer Queries from Multiple Data Cubes, ACM Transactions on Database Systems (TODS), Vol. 39, Issue 3, September 2014.
(6) Doka K., Tsoumakos D., and Koziris N. 2010. Distributing the Power of OLAP. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (HPDC 10), pp 324-327.
(7) Morfonios K., Konakas S., Ioannidis Y. and Kotsis N., ROLAP implementations of the data cube, ACM Computing Surveys (CSUR), Vol. 39, Issue 4, 2007.
(8) Zhao P., Li X., Xin D., and Han J. 2011. Graph Cube: On Warehousing and OLAP multidimensional Network. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp 853-864.
(9) Chen Z., Ordonez C. and Alvarado G. C. 2009. Fast and Dynamic OLAP exploration using UDFs. In Proceedings of the ACM SIGMOD International Conference on Management of Data pp 1087-1090.
(10) Roy S. and Suciu D. 2014. A Formal Approach to Finding Explanations for Database Queries. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pp 1579-1590.
(11) Nandi A., Yu C., Bohannon P., and Ramakrishna R., Data Cube Materialization and Mining over MapReduce, Transactions on Knowledge and Data Engineering, Vol. 6, No. 1, January 2012.
(12) Hung E., Cheung W.D. and KAO B., Optimization in Data Cube System Design, Journal of Intelligent Information Systems, pp 17-45, 2004.