Maribel Yasmina Santos – författare

Big Data is a concept of major relevance in today’s world, sometimes highlighted as a key asset for productivity growth, innovation, and customer relationship, whose popularity has increased considerably during the last years. Areas like smart cities, manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes.

The concept of a Big Data Warehouse (BDW) is emerging as either an augmentation or a replacement of the traditional Data Warehouse (DW), a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems.

This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to BDWs as a way of efficiently storing and processing batch or streaming data for structured or semi-structured analytical problems.

Big Data is a concept of major relevance in today’s world, sometimes highlighted as a key asset for productivity growth, innovation, and customer relationship, whose popularity has increased considerably during the last years. Areas like smart cities, manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes.

The concept of a Big Data Warehouse (BDW) is emerging as either an augmentation or a replacement of the traditional Data Warehouse (DW), a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems.

This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to BDWs as a way of efficiently storing and processing batch or streaming data for structured or semi-structured analytical problems.

This book constitutes the proceedings of the 18th International Conference on Research Challenges in Information Sciences, RCIS 2024, which took place in Guimarães, Portugal, during May 2024.

The scope of RCIS is summarized by the thematic areas of information systems and their engineering; user-oriented approaches; data and information management; business process management; domain-specific information systems engineering; data science; information infrastructures, and reflective research and practice.

The 25 full papers, 12 Forum and 5 Doctoral Consortium papers included in these proceedings were carefully reviewed and selected from 100 submissions. They were organized in topical sections as follows:

Part I: Data and information management; conceptual modelling and ontologies; requirements and architecture; business process management; data and process science; security; sustainability; evaluation and experience studies

Part II: Forum papers; doctoral consortium papers.

This book constitutes the proceedings of the 18th International Conference on Research Challenges in Information Sciences, RCIS 2024, which took place in Guimarães, Portugal, during May 2024.

The scope of RCIS is summarized by the thematic areas of information systems and their engineering; user-oriented approaches; data and information management; business process management; domain-specific information systems engineering; data science; information infrastructures, and reflective research and practice.

The 25 full papers, 12 Forum and 5 Doctoral Consortium papers included in these proceedings were carefully reviewed and selected from 100 submissions. They were organized in topical sections as follows:

Part I: Data and information management; conceptual modelling and ontologies; requirements and architecture; business process management; data and process science; security; sustainability; evaluation and experience studies

Part II: Forum papers; doctoral consortium papers.