Emerging Perspectives in Big Data Warehousing

  • 6h 48m
  • David Taniar, Johanna Wenny Rahayu (eds)
  • IGI Global
  • 2019

The concept of a big data warehouse appeared in order to store moving data objects and temporal data information. Moving objects are geometries that change their position and shape continuously over time. In order to support spatio-temporal data, a data model and associated query language is needed for supporting moving objects.

Emerging Perspectives in Big Data Warehousing is an essential research publication that explores current innovative activities focusing on the integration between data warehousing and data mining with an emphasis on the applicability to real-world problems. Featuring a wide range of topics such as index structures, ontology, and user behavior, this book is ideally designed for IT consultants, researchers, professionals, computer scientists, academicians, and managers.

About the Author

David Taniar received his PhD in Databases from Victoria University (Australia, 1997) and is now a Senior Lecturer at Monash University (Australia). He has published more than 100 research articles and edited a number of books in the Web technology series. He is on the editorial board of a number of international journals, including Data Warehousing and Mining, Business Intelligence and Data Mining, Mobile Information Systems, Mobile Multimedia, Web Information Systems, and Web and Grid Services. He has been elected as a Fellow of the Institute for Management of Information Systems (UK).

In this Book

  • A Two-Tiered Segmentation Approach for Transaction Data Warehousing
  • Real-Time Big Data Warehousing
  • Deductive Data Warehouses: Analyzing Data Warehouses with Datalog (By Example)
  • A Trajectory Ontology Design Pattern for Semantic Trajectory Data Warehouses: Behavior Analysis and Animal Tracking Case Studies
  • Personalized Spatio-Temporal OLAP Queries Suggestion Based on User Behavior and a New Similarity Measure
  • Building OLAP Cubes from Columnar NoSQL Data Warehouses
  • Commercial and Open Source Business Intelligence Platforms for Big Data Warehousing
  • Index Structures for Data Warehousing and Big Data Analytics
  • Multidimensional Analysis of Big Data
  • Development of ETL Processes Using the Domain-Specific Modeling Approach
  • Introduction of Item Constraints to Discover Characteristic Sequential Patterns
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