Integration Challenges for Analytics, Business Intelligence, and Data Mining

Integration Challenges for Analytics, Business Intelligence, and Data Mining

Ana Azevedo (CEOS:PP, ISCAP, Polytechnic of Porto, Portugal) and Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal)
Release Date: December, 2020|Copyright: © 2021 |Pages: 250|DOI: 10.4018/978-1-7998-5781-5
ISBN13: 9781799857815|ISBN10: 1799857816|EISBN13: 9781799857839|ISBN13 Softcover: 9781799857822
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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

Reviews and Testimonials

"This book volume provides state of the art analysis for a successful integration of three closely interrelated fields, namely, analytics, business intelligence, and data mining. The challenges of their integration are identified and potential solutions are analyzed and discussed. Readers, practitioners and developers will find in this volume valuable information and comprehensive coverage of the integration of analytics, business intelligence, and data mining."

– Prof. Fatos Xhafa, Technical University of Catalonia (UPC), Spain

"This book has content that has been requested for a long time. I would recommend it because it fine-tunes big data topics, and I think it will have a good impact on the computing society."

– Prof. Manuel Pérez-Cota, Universidade de Vigo, Spain

Table of Contents and List of Contributors

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