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MRE-KDD+

Alfredo Cuzzocrea & Pablo Garcia Bringas

International Journal of Data Warehousing and Mining2026https://doi.org/10.4018/ijdwm.395849article
ABDC C
Weight
0.50

What the paper says

Big data settings are currently evolving from classical systems that focus on supporting advanced decision-support processes—as applied to many real-life scenarios, which are typically populated by distributed and heterogeneous data sources, such as conventional distributed data warehousing environments—to cooperative information systems. Different data formats contribute to define challenging big data systems, in which the main issue consists in supporting modern big data analytics involving massive amounts of data. As a consequence, a relevant research challenge is how to efficiently integrate, process, and mine such distributed knowledge, which composes the foundations of final big data analytics processes. Starting from these considerations, in this paper the authors propose an online analytical mining-based framework for supporting big data analytics, along with a formal model underlying this framework, called Multi-Resolution Ensemble-Based Model for Advanced Knowledge Discovery in Big Data Warehouses.

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https://doi.org/https://doi.org/10.4018/ijdwm.395849

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@article{alfredo2026,
  title        = {{MRE-KDD+}},
  author       = {Alfredo Cuzzocrea & Pablo Garcia Bringas},
  journal      = {International Journal of Data Warehousing and Mining},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijdwm.395849},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.