Novel Database Systems and Data Mining Algorithms in the Big Data Era

Dear Colleagues,

The increasing availability of huge amount of data, the so-called big data, possibly produced by devices that define heterogeneous, decentralized and distributed environments, poses new challenges for the database and data mining communities.

First, big data must be efficiently and properly collected, integrated, stored, managed and queried by means of novel database systems. Then, novel scalable data mining and machine learning algorithms can be applied on big data to extract useful, compact, interpretable and actionable knowledge from the collected data useful to improve decision-making processes. Some efforts have been already devoted to address the scalability issues related big data management and analytics. However, more efficient and novel systems and algorithms are still needed. Moreover, other important issues, such as data integration, data tailoring, data contextualization and interpretability are still open research issues in the big data context.

This Special Issue focuses on the design, implementation and validation of novel database systems and data mining algorithms for big data management and analytics. The Special Issue covers the entire big data analytics pipeline: from data acquisition to knowledge extraction and exploitation.

The topics of interest include, but are not limited to:

• NoSQL databases
• Scalable and distributed frameworks for big data management and analytics
• Scalable data mining and machine learning algorithms
• Big data integration
• Data tailoring for big data
• Big data analytics and contextual information
• Big data and interpretability
• Big heterogeneous data (e.g., textual data, images, videos, social data)
• Vector representations of text

Prof. Dr. Paolo Garza
Prof. Dr. Elisa Quintarelli
Prof. Dr. Luca Cagliero
Guest Editors

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