Data Modeling for Big Data Analytics

Dear Colleagues,

Business intelligence (BI) applications are data-intensive; thus, data modeling is a key aspect to ensure their effectiveness and efficiency. At the conceptual level, data modeling provides a high level of abstraction in describing the structure and features of the information to be delivered in an implementation-independent way. At the logical and physical levels, it enables data structures to be specifically fine-tuned to achieve good performances on the target data model. Since the 1990s, data modeling for BI has mainly meant multidimensional modeling, which has been a key to access the benefits of OLAP [DM1] querying in data warehouses. Nowadays, the increase in analytics and big data technologies asks for a review and extension of the classical multidimensional paradigm and paves the way to new solutions tailored for the emerging user needs and for the specific technological features of big data platforms. In this context, we seek for original submissions that contribute novel approaches, solutions, methods, languages, and applications on the following topics:

• Data modeling in the context of conceptual design of data warehouses, analytics, and business intelligence applications, in presence of big data sources
• Data modeling in the context of logical design of data warehouses, analytics, and business intelligence applications, when the target platform is a big data one
• Design methodologies related to the above mentioned data models
• Techniques for efficiently querying and accessing the above mentioned data models

Prof. Matteo Golfarelli
Prof. Stefano Rizzi
Guest Editors

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