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.

Uncovering and analyzing data associated with the current business environment is essential in maintaining a competitive edge. As such, making informed decisions based on this data is crucial to managers across industries. Integration of Data Mining in Business Intelligence Systems investigates the incorporation of data mining into business technologies used in the decision making process. Emphasizing cutting-edge research and relevant concepts in data discovery and analysis, this book is a comprehensive reference source for policymakers, academicians, researchers, students, technology developers, and professionals interested in the application of data mining techniques and practices in business information systems.

This book constitutes the refereed proceedings of the First International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2021, held in La Libertad, Ecuador, in November 2021. The 53 full papers and 2 short contributions were carefully reviewed and selected from 155 submissions. The volume covers a variety of topics, such as computer systems organization, software engineering, information storage and retrieval, computing methodologies, artificial intelligence, and others. The papers are logically organized in the following thematic blocks: Computing Solutions; Data Intelligence; Ethics, Security, and Privacy; Sustainability.

This book constitutes the refereed proceedings of the 13th Portuguese Conference on Artificial Intelligence, EPIA 2007, held in GuimarAes, Portugal, in December 2007 as eleven integrated workshops.

The 58 revised full papers presented were carefully reviewed and selected from a total of 210 submissions. In accordance with the eleven constituting workshops, the papers are organized in topical sections on general artificial intelligence (GAIW 2007), AI applications for sustainable transportation systems (AIASTS 2007), artificial life and evolutionary algorithms (ALEA 2007), ambient intelligence technologies and applications (AMITA 2007), building and applying ontologies for the semantic Web (BAOSW 2007), business intelligence (BI 2007), computational methods in bioinformatics and systems biology (CMBSB 2007), intelligent robotics (IROBOT 2007), multi-agent systems: theory and applications (MASTA 2007), search techniques for constraint satisfaction (STCS 2007), and text mining and applications (TEMA 2007).

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.