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Salvatore F. Pileggi
The University of Queensland
Brisbane, Australia
 

Salvatore Flavio Pileggi is a research fellow in the School of Information Technology and Electrical Engineering (ITEE) at the University of Queensland (Australia). His research currently focuses on different aspects of computational science and eResearch, including, among others, knowledge representation, data semantics, and analytics.

Previously, after a short experience as a software engineer in 2007, he held research positions at top-level institutions in Spain (Polytechnic University of Valencia, 2007-2012), in New Zealand (University of Auckland, 2013-2014), and in France (INRIA & UPMC-LIP6, 2014-2016). In those years, he worked across multiple research areas including (but not limited to) cyber-physical systems, semantic technologies, and cloud computing.

He received a MSc in Computer Engineering from the University of Calabria (Italy) in 2005 and a PhD (cum laude) in Communications from the Polytechnic University of Valencia (Spain) in 2011.

He has authored/co-authored well over 50 research publications, including papers in journals and conferences, as well as book chapters. His first paper (2006) was awarded (Best Paper Award) at ICWMC 2006. In 2012, he edited the book Semantic Interoperability: Issues, Solutions, Challenges.

He is constantly involved in technical committees of selected scientific events, and periodically reviews for top-level international journals. Since 2014, he has served as a reviewer (Featured Reviewer from 2017) for ACM Computing Reviews where he regularly publishes his reviews. He chaired several international workshops (SSW 2010, SSW 2011, IWSI 2011, IWSI 2012, IWCCTA 2011 and UXeLATE 2012), in conjunction with relevant international conferences. He acted as a guest editor for Future Internet (MDPI) in 2012 and 2014.

Recently, he has been actively working on several multi-disciplinary collaborative research projects involving specialists from industry and academia.

For more information on Flavio’s research activities, please visit his homepage.


     

What technological features are used in smartphone apps that promote physical activity? A review and content analysis
Mollee J., Middelweerd A., Kurvers R., Klein M.  Personal and Ubiquitous Computing 21(4): 633-643, 2017. Type: Article

A systematic review of the use of mobile technology to promote physical activity is proposed in this paper. The number of apps aimed at healthcare available in commercial stores is strongly and constantly increasing: the underpinning idea, common ...

 

Value and misinformation in collaborative investing platforms
Wang T., Wang G., Wang B., Sambasivan D., Zhang Z., Li X., Zheng H., Zhao B.  ACM Transactions on the Web 11(2): 1-32, 2017. Type: Article

Collaborative investing platforms often rely on the common “wisdom of the crowd” concept in a domain in which even highly paid, well-educated, and experienced professionals make mistakes, providing wrong or inaccurate evaluations....

 

Dimensional enrichment of statistical linked open data
Varga J., Vaisman A., Romero O., Etcheverry L., Pedersen T., Thomsen C.  Journal of Web Semantics 40(C): 22-51, 2016. Type: Article

As with most consolidated analysis techniques, online analytical processing (OLAP) usually assumes a local data environment (for example, analysis within an organization) in which multidimensional datasets are modeled according to proprietary stan...

 

A trust evaluation scheme for complex links in a social network: a link strength perspective
Li M., Xiang Y., Zhang B., Huang Z., Zhang J.  Applied Intelligence 44(4): 969-987, 2016. Type: Article

As links among users are the channel for interchanging information inside social networks, trust is a widely accepted indicator or parameter to discover and analyze potential security threats. This work proposes a trust evaluation schema for socia...

 

 Catching synchronized behaviors in large networks: a graph mining approach
Jiang M., Cui P., Beutel A., Faloutsos C., Yang S.  ACM Transactions on Knowledge Discovery from Data 10(4): 1-27, 2016. Type: Article

The automatic detection and accurate interpretation of suspicious graph patterns is one of the key issues in spotting malicious activities inside real-world systems, such as fake followers in Twitter, social network manipulation, and distributed d...

 
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