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Privacy-preserving multidimensional big data analytics models, methods and techniques: A comprehensive survey

EXPERT SYSTEMS WITH APPLICATIONS 2025年 270卷

作者： Cuzzocrea, Alfredo Soufargi, Selim Univ Calabria IDEA Lab Arcavacata Di Rende Italy Univ Paris City Dept Comp Sci Paris France

The topic of privacy-preserving big data analytics is gaining momentum now, thanks to the plethora of modern application scenarios where it can be successfully applied. These range from smart city applications to e-government, from social networks to graph-like analysis tools, from logistic transport systems to business intelligence systems, and so forth. Healthcare systems, for instance, are turning out to be one of the major applicative settings where privacy-preserving big data analytics tools are extensively applied, due to the specific requirements dictated by the context (i.e., protecting individual privacy). multidimensional big data analytics methods area special case of big data analytics methods where the emphasis is put on the usage of multidimensional metaphors, which had a fortunate experience in OnLine Analytical Processing and Business Intelligence systems. One step forward, privacy-preserving multidimensional big data analytics, is thus of extreme interest to the research community because it combines the intrinsic privacy-preserving problem with the emerging multidimensional big data analytics methods. Inspired by these considerations, in this paper, we provide a comprehensive overview that focuses the attention on privacy-preserving multidimensional big data analytics models, methods, and techniques over big data by highlighting several characteristics of the various in-literature proposals and also providing a review of future research challenges in the investigated research domain.

关键词： big data big data analytics multidimensional big data analytics Privacy-preserving big data analytics Privacy-preserving multidimensional big data analytics

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multidimensional big data analytics over big Web Knowledge Bases: Models, Issues, Research Trends, and a Reference Architecture 8

Multidimensional Big Data Analytics over Big Web Knowledge B...

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8th IEEE International Conference on Multimedia big data (bigMM)

作者： Cuzzocrea, Alfredo Univ Calabria IDEA Lab Arcavacata Di Rende Italy

ISBN: (数字)9781665459631

ISBN: (纸本)9781665459631

This paper focuses the attention on the problem of supporting multidimensional big data analytics tools over big Web Knowledge Bases. These big data repositories are generated from web systems built on top of big datasets, as in several real-life examples such as web interfaces to social networks, e-government systems, smart city tools (e.g., for weather forecasting), and so forth. The problem of extracting actionable knowledge insights from big web knowledge bases is a relevant challenge at now. In line with this emerging research field, the paper proposes models, issues and research trends in the context of multidimensional big data analytics over big web knowledge bases, along with a reference architecture that impersonates this paradigm.

关键词： big data big Web Knowledge Bases big data Management big data analytics multidimensional big data analytics OLAP BI big OLAP data Cubes big data analytics Systems

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A Reference Architecture for Supporting multidimensional big data analytics over big Web Knowledge Bases: Definitions, Implementation, Case Studies

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2023年第4期17卷 545-568页

作者： Cuzzocrea, Alfredo Univ Calabria iDEA Lab I-87036 Arcavacata Di Rende Italy CNR ICAR I-87036 Arcavacata Di Rende Italy

This paper focuses attention on the problem of supporting multidimensional big data analytics tools over big Web Knowledge Bases. These big data repositories are generated from web systems built on top of big datasets, as in several real-life examples such as web interfaces to social networks, e-government systems, smart city tools (e.g., for weather forecasting), and so forth. The problem of extracting actionable knowledge insights from big web knowledge bases is a relevant challenge now. In line with this emerging research field, the paper proposes models, issues and research trends in the context of multidimensional big data analytics over big web knowledge bases, along with a reference architecture that impersonates this paradigm.

关键词： big data big Web knowledge bases big data management big data analytics multidimensional big data analytics OLAP BI big OLAP data cubes big data analytics systems

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MALAGA - multidimensional big data analytics over Massive Graph data

MALAGA - MultidimensionAL Big DAta Analytics over Massive Gr...

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2024 IEEE International Conference on big data, bigdata 2024

作者： Cuzzocrea, Alfredo Hajian, Mojtaba Hafsaoui, Abderraouf University of Calabria IDEA Lab Rende Italy University of Paris City Dept. of Computer Science Paris France

ISBN: (纸本)9798350362480

Focusing on the main research context represented by the issue of supporting big data analytics over big graph data, this paper introduces and experimentally assesses the framework MALAGA (multidimensional big data analytics over Massive Graph data). MALAGA incorporates several innovations, including OLAP analysis of big graph data, columnar-OLAP methodologies, and Apache Hive extensions. A comprehensive experimental assessment and analysis of the framework's performance is finally presented and discussed, by significantly integrating the conceptual contributions of our research. © 2024 IEEE.

关键词： big data big data analytics big Graph data Massive Graph data multidimensional big data analytics

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Machine-Learning-Based multidimensional big data analytics over Clouds via Multi-Columnar big OLAP data Cube Compression

Machine-Learning-Based Multidimensional Big Data Analytics o...

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2023 IEEE International Conference on big data, bigdata 2023

作者： Cuzzocrea, Alfredo Hafsaoui, Abderraouf Leung, Carson K. University of Calabria IDEA Lab Rende Italy University of Paris City Dept. of Computer Science Paris France University of Manitoba Dept. of Computer Science WinnipegMB Canada

ISBN: (纸本)9798350324457

This paper proposes a new theory on combining innovative multidimensional big data analytics with well-known Machine Learning (ML) in order to magnify the expressive power and the accuracy of knowledge insights discovery from massive big datasets. At the level of enabling technology, with the goal of fully supporting this novel paradigm, the issue of managing and mining big OLAP data cubes over Clouds arises. Due to computational complexity requirements, the latter challenge is addressed by proposing an innovative solution for (1) representing big OLAP data cubes over Clouds via a multi-column-based representation, and (2) compressing the deriving multi-column representations for achieving the desired effectiveness and efficiency. This paper introduces the fundamental model of Machine-Learning-Based multidimensional big data analytics, along with a reference architecture implementing it. © 2023 IEEE.

关键词： big data big data analytics big OLAP Cubes Machine Learning Machine-Learning-Based multidimensional big data analytics Multi-Columnar big OLAP Cube Compression Multi-Columnar Representation of big OLAP Cubes multidimensional big data analytics

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Evolving OLAP and BI towards Complex, High-Performance big-OLAP-data-Cube-Processing analytics Frameworks: How to Speed-Up Large-Scale, High-Dimensional Queries over Clouds

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Procedia Computer Science 2024年 246卷 4169-4175页

作者： Alfredo Cuzzocrea iDEA Lab University of Calabria Rende Italy Department of Computer Science University of Paris City Paris France

Starting from traditional OLAP and BI systems, this paper explores a new line of research represented by the so-called multidimensional big data analytics . This is an emerging area that is conquering the scene time by time, particularly due to its nice amenities to be found both in the theoretical as well as the industrial settings. In this respect, the paper proposes a methodology and a reference architecture for representing big OLAP data cubes over Clouds via columns , which mostly capture the intrinsic dimensions of the target cube. A preliminary evaluation of the methodology is also provided. Finally, an overview on future research directions in the investigated scientific field completes the contributions of this paper.

关键词： big data big data Management big data analytics multidimensional big data analytics OLAP BI big OLAP data Cube big data analytics Systems

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