Generalized Association Rules for ER Models by Using Mining Operations on Fuzzy Datasets | Chapter 9 | Recent Progress in Science and Technology Vol. 6

 Today implausible story world is functioning in an extreme-competitive atmosphere. However, the business wholes have realized that the key to endurance and sustainability of the utilization of the basic document file which is got from various trade processes. In other words, it means that the competitiveness is worked out by means of deal with the dataset and ensuring that the styles and patterns provide awareness into the decision making process. However, conclusion making is complex and complicated as various factors need expected taken into concern. These factors produce the concept of the fluffy datasets. The fuzzy datasets further decrease the scope of the currents and patterns so that familiar accurate decisions maybe obtained.The paper inquires to address the issues of the fuzzy datasets in agreements of bringing in adulthood in the decision making process by guaranteeing that the association rule excavating processes are able to pay more accuracy and accuracy in the decision making processes. Further, the paper seeks to address the issues of the individual relationship forming that exists in the table tables and the means and device deployed to overcome the issues and challenges formal by ER modelling. The projected study aims to extend the existent algorithms comprising of Extended Apriori and Apriori star to decide a new algorithm. The gift of the study results in an attempt to standardize algorithms for judgment the most appropriate come into being tables comprising of fluffy data.

Author(s) Details:

Praveen Arora,
Jagan Institute of Management Studies, New Delhi, India.

Sanjive Saxena,
Jagan Institute of Management Studies, New Delhi, India.

Deepti Chopra,
Jagan Institute of Management Studies, New Delhi, India.

Please see the link here: https://stm.bookpi.org/RPST-V6/article/view/9875

Study on Data Warehousing Applications: An Analytical Tool for Decision Support System | Chapter 04 | Recent Recent Advances in Mathematical Research and Computer Science Vol. 8

 This paper gives an overview of data warehousing technologies, with a focus on the unique demands that data warehouses make on database management systems (DBMSs). Data warehouses and other data-driven decision support systems can handle data extraction from a variety of sources. Data warehouses standardise data throughout the enterprise to provide a unified picture of data. Decision makers can get the information they need from data warehouses (DW). On-line analytical processing (OLAP) applications are supported by the data warehouse, and their functional and performance requirements are considerably different from those of the on-line transaction processing (OLTP) applications typically supported by operational databases. Data warehouses enable effective data mining by providing on-line analytical processing (OLAP) tools for interactive analysis of multidimensional data at various granularities. Data warehousing and OLAP have evolved as major technologies for storing, organising, and retrieving large amounts of data. Both are critical components of decision assistance, which has become a hot topic in the database business.

This study examines the characteristics, uses, and architecture of data warehousing (DW) using Data Mining, Online Analytical Processing (OLAP), and On-line Transaction Processing (OLTP) technologies.


Author(S) Details

Mohammed Shafeeq Ahmed
Department of Computer Science, Gulbarga University, Gulbarga, Karnataka, India.

View Book:- https://stm.bookpi.org/RAMRCS-V8/article/view/5744