iDEA Lab, University of Calabria, Rende, Italy
Multidimensional Big Data Analytics over Big Web Knowledge Bases: Models, Issues, Research Trends, and a Reference Architecture
Abstract: Big Web Knowledge Bases are actual extensions of well-known knowledge bases where the data layer is composed by big data repositories. These repositories include: social networks, e-government bases, biomedical banks, industry 4.0 web repositories, and so forth. In this context, one of the most relevant research challenges is represented by the issue of supporting big data analytics over such systems, by achieving all the benefits deriving from knowledge discovery and decision making. Multidimensional big data analytics is an emerging paradigm within the more general big data analytics research context, where the main emphasis is
in engrafting fortunate multidimensional metaphors inherited by previous OLAP and BI experiences into the main big data analytics process. Inspired by this scientific blueprint, in this talk we provide models, issues and research trends related to the goal of supporting multidimensional big data analytics paradigms over big Web knowledge bases. Furthermore, we provide a reference architecture that implements this novel paradigm, by strictly following its foundations and its axioms.
Bio: Alfredo Cuzzocrea is Professor of Computer Engineering at the University of Calabria, Rende, Italy. He is Head of the Big Data Engineering and Analytics Lab of the University of Calabria, Rende, Italy. He is also Research Fellow of the National Research Council (CNR), Rome, Italy. Previously, he has covered the role of Full
Professor in Computer Engineering at the University of Lorraine, Nancy, France, where he held the Excellence Chair in Computer Engineering. He has also been International Senior Research Fellow of the ISITE-BFC Research Excellence Program of the Ministry of Higher Education and Research (MESR), France. He holds several visiting professor positions worldwide. His current research interests span the following scientific fields: big data, database systems, data mining, data warehousing, and knowledge discovery. He is author or co-author of more than 680 papers in international conferences (including CIKM, MDM, EDBT, SSDBM, PAKDD, DOLAP), international journals (including TKDE, JCSS, IS, FGCS, INS, JMLR) and international books. He is recognized in prestigious international research rankings, such as: (i) 1st World-Wide Scientist for Research Topic: “OnLine Analytical Processing (OLAP)” by Microsoft Academic, Redmond, WA, USA; (ii) Top 2% World-Wide Scientist by METRICS, Stanford, CA, USA; (iii) Top Scientist in Computer Science and Electronics by Guide2Research, Clifton, NJ, USA; (iv) Top Researcher in Computer Science by SciVal –
Elsevier, Amsterdam, Netherlands; (v) Top Italian Scientist in Computer Sciences by Virtual Italian Academy, Manchester, UK. Similarly, his international bibliometric research indicators are of high values, as recognized by Google Scholar H-Index, Scopus H-Index, ACM Download Metrics, and DBLP Most Prolific Authors. His research results have been awarded largely. He is involved in several national and international research projects (including international research projects founded by the Horizon 2020 EU framework programme for research and innovation), where he covers responsibility roles.