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Curriculum Vitae

Summary

I'm a PhD student in Industrial and Environmental Engineering (curricula Industry 4.0) from November 2017. My main research activity concerns Machine/Deep Learning for Spoken Language Processing (Audio-Visual Speech Recognition/Enhancement).

Teaching activities

• Teaching tutor of "Foundations of Computer Science" (BSc Degree in Electronic Engineering - Unimore)

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• Address: Dipartimento di Ingegneria dell'Informazione, via Pietro Vivarelli 10 - 41125 Modena Italy
• Telephone: +39-059-2056304
• Email: luca.gagliardelli@unimore.it
• Office location: Doctorate Laboratory, 1° floor, DII

Summary

I am Luca Gagliardelli, a PhD student of the Doctoral School in ICT of Modena and Reggio Emilia. My research topic is the the big data integration.

Before starting the PhD school I worked as research fellow in the DBGroup (from May 2015 until October 2016).

During my first PhD year I mainly worked on the development of SparkER, an Entity Resolution framework for Apache Spark (https://github.com/Gaglia88/sparker).

Teaching activities

• Teaching assistant for the course of principle of database management systems "Sistemi Informativi e Basi di Dati" - Università di Modena e Reggio Emilia
  
• Teaching assistant for the course of Big Data Management and Governance - Università di Modena e Reggio Emilia
  
• Teaching assistant for the course of principle of database management systems "Basi di Dati" - Università di Modena e Reggio Emilia

Publications

• Simonini, G., Gagliardelli, L., Zhu, S., & Bergamaschi, S. (2018, July). Enhancing Loosely Schema-aware Entity Resolution with User Interaction. In 2018 International Conference on High Performance Computing & Simulation (HPCS) (pp. 860-864). IEEE.
• Zhu, S., Gagliardelli, L., Simonini, G., & Beneventano, D. (2018, July). How improve Set Similarity Join based on prefix approach in distributed environment. In 2018 International Conference on High Performance Computing & Simulation (HPCS) (pp. 844-851). IEEE.
• Gagliardelli, L., Zhu, S., Simonini, G., & Bergamaschi, S. (2018). BigDedup: a Big Data Integration toolkit for Duplicate Detection in Industrial Scenarios. In 25th International Conference on Transdisciplinary Engineering (TE2018) (Vol. 7, pp. 1015-1023).
• Magnotta, L., Gagliardelli, L., Simonini, G., Orsini, M., & Bergamaschi, S. (2018). MOMIS Dashboard: a powerful data analytics tool for Industry 4.0. In 25th International Conference on Transdisciplinary Engineering (TE2018) (Vol. 7, pp. 1074-1081).
• Bergamaschi, S., Beneventano, D., Mandreoli, F., Martoglia, R., Guerra, F., Orsini, M., ... & Gagliardelli, L. (2018). From Data Integration to Big Data Integration. In A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years (pp. 43-59). Springer International Publishing.
• Orsini M., Calanchi E., Magnotta L., Gagliardelli L., Govi M., Mele F., Tupler R. "The Italian FSHD Registry: an enhanced data integration and analytics framework for Smart Health Care", RTSI 2017
• Sonia Bergamaschi, Luca Gagliardelli, Giovanni Simonini, Song Zhu, BigBench Workload Executed by using Apache Flink, In Procedia Manufacturing, Volume 11, 2017, Pages 695-702, ISSN 2351-9789, https://doi.org/10.1016/j.promfg.2017.07.169
• Beneventano, D., Bergamaschi, S., Gagliardelli, L., & Po, L. (2015, November). Driving Innovation in Youth Policies with Open Data. In International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management (pp. 324-344). Springer, Cham.
• Beneventano D., Bergamaschi S., Gagliardelli L. and Po L. (2015). Open Data for Improving Youth Policies. 7th International Conference on Knowledge Engineering and Ontology Development: 118-129. Paper

Giovanni Simonini Loc.: Dipartimento di Ingegneria dell'Informazione, via Pietro Vivarelli 10 - 41125 Modena Italy MO-27-01-a19, 1° floor Building 27 Tel.: (interno) 6138 Mail: simonini{at}unimore{dot}it :

CS Ph.D. and postdoc here in Modena from March 2016.

My research activity concerns Big Data, Data Integration (in particular Entity Resolution) and Semantic Web.

Ongoing Projects:

• Entity Reoslution (a.k.a. Entity Matching, Record Linkage, Deduplication):
  • BLAST: a Loosely Schema-aware Meta-blocking Approach for Entity Resolution, PVLDB 2016
    CODE here: http://stravanni.github.io/blast/

Teaching activities

• Instructor for the course “Emerging tools and techniques for massive data analytics” organized by CINECA and CINI 
  • September  2016 - Modena
  • December  2015 - Rome
  • April  2015 - Bologna
  • December 2014 – Bologna
• Teaching assistant for the courses of advanced database techniques "Tecnologia delle basi di dati" and principle of database management systems "Basi di Dati" - Università di Modena e Reggio Emilia

Publications

• Book Chapters
  • S.Bergamaschi, F.Guerra, G.Simonini “Keyword Search over Relational Databases: Issues, Approaches and Open Challenges”, Bridging Between Information Retrieval and Databases - pages 54--73, 2014, Springer Berlin Heidelberg
• Journal

  • Giovanni Simonini, Sonia Bergamaschi, H. V. Jagadish: BLAST: a Loosely Schema-aware Meta-blocking Approach for Entity Resolution. PVLDB 9(12): 1173-1184 (2016)

  • Bergamaschi, S., Ferrari, D., Guerra, F., Simonini, G., & Velegrakis, Y. (2016). Providing Insight into Data Source Topics. Journal on Data Semantics, 1-18.
  • Francesco Guerra, Giovanni Simonini, Maurizio Vincini: Supporting Image Search with Tag Clouds: A Preliminary Approach. Adv. in MM 2015: 439020:1-439020:10 (2015)
• Conferences
  • Simonini, G., & Zhu, S. (2015, July). Big data exploration with faceted browsing. In High Performance Computing & Simulation (HPCS), 2015 International Conference on (pp. 541-544). IEEE.
  • M.Interlandi, G.Simonini, S.Bergamaschi “Towards Declarative Imperative Data-parallel Systems”, SEBD 2014. (June)
  • Simonini, Giovanni, and Francesco Guerra. "Using big data to support automatic Word Sense Disambiguation." High Performance Computing & Simulation (HPCS), 2014 International Conference on. IEEE, 2014.
  • S.Bergamaschi, D.Ferrari, F.Guerra, and G.Simonini "Discovering the topics of a data source: a statistical approach". SDSW 2014 – Workshop of the ISWC 2014

• Technical Reports
  • “World Summit on Big Data and Organization Design”, Group “Data Challenge”

Università degli Studi di Modena e Reggio Emilia Address: Dipartimento di Ingegneria dell'Informazione, via Vignolese, 905 - 41125 Modena Italy Telephone: +39-059-2056304 Email: fabio.benedetti@unimore.it Office location: Doctorate Laboratory, 1° floor, DII

Summary

I am Fabio Benedetti, a PhD student of the Doctoral School in ICT of Modena and Reggio Emilia. My research topic regards the study of ontologies in the eHealth domain and now my research effort is focused on the development of LODeX: a tool that produces a highly representative summary of a LOD source. Starting from the URL of a SPARQL Endpoint, the tool launches a set of predefined SPARQL queries and automatically generates a summary of the source. During my Ph.D i discovered a real passion for Data Science. I fed this passion taking some specialization online courses that made me able to work with large amount of data.

Before starting the PhD school I did an internship at Datariver with the goal of reengineering a framework for clinical data management. In July 2012 I obtained the Master Degree in Computer Science and Engineering from the University of Modena and Reggio Emilia. My Degree dissertation was: “Re-engineering of a framework for the management and analysis of clinical data in the form of web application database”. I collaborated with Doxee in a Business Intelligence and Data Integration project. During this period I explored the use of the Pentaho Open Source platform to integrate data coming from different Databases and produce BI reports. Since October 2012 I collaborate in DataRiver research projects.

Projects

LODeX

The Linked Open Data (LOD) Cloud has more than tripled its sources in just three years (from 295 sources in 2011 to 1014 in 2014). While the LOD data are being produced at a increasing rate, LOD tools lack in producing an high level representation of datasets and in supporting users in the exploration and querying of a source. To overcome the above problems and significantly increase the number of consumers of LOD data, we devised a new method and a tool, called LODeX, that promotes the understanding, navigation and querying of LOD sources both for experts and for beginners. It also provides a standardized and homogeneous summary of LOD sources and supports user in the creation of visual queries on previously unknown datasets.

Activities

Tutorial on Sentiment Analysis

A simple project with some script that implement techniques of Sentiment Analysis for the course of Big Data Analysis of the department of engeneering of Modena e Reggio Emilia.

Slides & GitHub

Web Science Summer School - Southampton University (20-26 July 2014)

 The School has been characterized by mixture of invited talks and tutorials on several aspects of data science, including data analytics, data publishing and interlinking, social media management, open data, and crowdsourcing.

Web page

RDA Research Data Alliance - Member of the Big Data Analytic Interest Group

 I won a grant as Early Career Scientist and I have been assigned the Big Data Analytic Interest group. I participated to the RDA Fourth Plenary Meeting 22 - 24 September 2014 in Amsterdam, where I also presented a poster containing my research on Linked Open Data.

Web page

Certifications

• M101J: MongoDB for Java Developers
• Health Informatics in the Cloud - Coursera Georgia Institute of Technology
• Introduction to Data Science - Coursera Computer Science & Engineering University Of Washington
• M101P: MongoDB for Developers
• Machine Learning on Coursera
• edX Verified Certificate for Scalable Machine Learning with Spark
• Learning Python for Data Analysis and Visualization (data projects on GitHub)
• Statistical Learning - Stanford Online Course

Publications

• Beneventano, D., Bergamaschi, S., Sorrentino, S., Vincini, M., Benedetti, F. Semantic annotation of the CEREALAB database by the AGROVOC linked dataset (2014) Ecological Informatics. Paper (electronic edition)

• F. Benedetti, S. Bergamaschi, and L. Po, “A visual summary for linked open data sources,” 2014, International Semantic Web Conference (Posters & Demos). online

• F. Benedetti, S. Bergamaschi, and L. Po, “Online index extraction from linked open data sources,” 2014, Linked Data for Information Extraction (LD4IE) Workshop held at International Semantic Web Conference. online

• Fabio Benedetti, Sonia Bergamaschi, and Laura Po. 2015. Visual Querying LOD sources with LODeX. In Proceedings of the 8th International Conference on Knowledge Capture (K-CAP 2015). paper
• Fabio Benedetti, Sonia Bergamaschi and Laura Po. LODeX: A tool for Visual Querying Linked Open Data.  2015, International Semantic Web Conference (Posters & Demos). paper
• Fabio Benedetti, Sonia Bergamaschi and Laura Po. Exposing the underlying schema of LOD sources. to appear at Web Intelligence (WI) and Intelligent Agent Technologies (IAT) 2015. paper
• Benedetti F, Bergamaschi S. A model for visual building SPARQL queries. SEBD 2016. Paper
• Benedetti F, Beneventano D, Bergamaschi S. Context Semantic Analysis: a knowledge-based technique for computing inter-document similarity. SISAP 2016 (International Conference on Similarity Search and Applications) 164-178 Paper

Università degli Studi di Modena e Reggio Emilia Address: Dipartimento di Ingegneria dell'Informazione, via Vignolese, 905 - 41125 Modena Italy Telephone: Email: song.zhu@unimore.it Office location: Doctorate Laboratory, 1° floor, DII

I obtained my Ph.D. in ICT at 13/02/2018. I want to express my deepest gratitude to my advisor Prof. Sonia Bergamaschi and my co-advisor Prof. Domenico Beneventano.  I wish to express my sincere thanks to every member of DBGroup.

My main research activity concerns Big Data Integration. My study is focused on join algoritms in distributed enviroment.

Teaching activities

• Instructor for the course “Emerging tools and techniques for massive data analytics” organized by CINECA and CINI.
  • April 8 - 10 2015 - Bologna
  • October 14-16 2015 - Milano
  • December 13-15, 2015 - Roma
  • April 12 - 14 2016 - Bologna
• Instructor for the School On Scientific Data Analytics and Visualization (jointly organized by Cineca and the DBGROUP), 20-25 June, 2016 - Bologna
  
• Teaching assistant for the courses of "Tecnologia delle basi di dati" and "Basi di Dati" - Università di Modena e Reggio Emilia
• Instructor for the course on Big Data Analytics (jointly organized by Cineca and the DBGROUP), 19-22 September, 2016 - Modena

Publications 

• Conferences
  • Bergamaschi Sonia, Gagliardelli Luca, Simonini, Giovanni and Zhu Song. BigBench workload executed by using Apache Flink. Procedia Manufacturing 11 (2017): 695-702.
  • Song Zhu, Giuseppe Fiameni, Giovanni Simonini and Sonia Bergamaschi. SOPJ: A Scalable Online Provenance Join for Data Integration. High Performance Computing & Simulation (HPCS), 2017 International Conference on. IEEE, 2017.
  • Sonia Bergamaschi, Giovanni Simonini and Song Zhu. Big Data Exploration with Faceted Browsing. Proceedings of the 10th Scientific Meeting of Classification and Data Analysis Group. 2015.
  • Simonini, G., & Zhu, S. (2015, July). Big data exploration with faceted browsing. In High Performance Computing & Simulation (HPCS), 2015 International Conference on (pp. 541-544). IEEE.
• Workshops
  
  • Luca Gagliardelli, Giovanni Simonini, Song Zhu and Giuseppe Fiameni. SparkER: an Entity Resolution framework for Apache Spark. IEEE Young Professional Event @ RTSI 2017.
• Book chapters
  • Sonia Bergamaschi, Domenico Beneventano, Federica Mandreoli, Riccardo Martoglia, Francesco Guerra, Mirko Orsini, Laura Po, Maurizio Vincini, Giovanni Simonini, Song Zhu, Luca Gagliardelli and Luca Magnotta. From Data Integration to Big Data Integration” in A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, Springer International Publishing, 2018.
  • Sonia Bergamaschi, Giovanni Simonini and Song Zhu. Enhancing big data exploration with faceted browsing, full paper, Post-Conference Proceedings volume Classification, (Big) Data Analysis and Statistical Learning, Springer International Publishing, 2017.

Skills

• SQL
• C++, Java, C#, Python, Scala
• Qlikview
• Apache Hadoop, Apache Spark, Apache Ignite

PhD.Ing. R. Carlos Nana Mbinkeu (Visiting Researcher)

Office: Tel.: Fax: E-mail:

Dipartimento di Ingegneria dell'Informazione via Vignolese, 905 - 41100 Modena Italy 059 2056143 int 6143 059 2056129 rodriguecarlos.nanambinkeu@unimo.it