Integration Challenges for Analytics, Business Intelligence, and Data Mining

Integration Challenges for Analytics, Business Intelligence, and Data Mining

Ana Azevedo (CEOS.PP-ISCAP/IPP, Porto, Portugal) and Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal)
Projected Release Date: December, 2020|Copyright: © 2021 |Pages: 340
ISBN13: 9781799857815|ISBN10: 1799857816|EISBN13: 9781799857839|DOI: 10.4018/978-1-7998-5781-5

Description

As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration.

Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Algorithms
  • Artificial Intelligence
  • Big Data
  • Business Analytics
  • Data Analytics
  • Data Intelligence
  • Data Science
  • Decision Making
  • Decision Support Systems
  • Machine Learning

Table of Contents and List of Contributors

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