[1]	Europe Big Data market 2015−2020 [Internet]. New York: PR Newswire Association LLC.; c2016 [updated 2016 May 30, cited 2016 Jun 12]. Available from: http://www.prnewswire.com/news-releases/europe-big-data-market-2015---2020-300276656.html.

[2]

Furletti B, Gabrielli L, Renso C, Rinzivillo S. Analysis of GSM calls data for understanding user mobility behavior. In: Hu X, Lin TY, Raghavan V, Wah B, Baeza-Yates R, Fox G, ., editors Proceedings of the 2013 IEEE International Conference on Big Data; <Date>2013 Oct 6–9</Date>; Santa Clara, CA, USA; 2013. p. 550–5.

[3]	Furletti B, Gabrielli L, Renso C, Rinzivillo S. Pisa tourism fluxes observatory: deriving mobility indicators from GSM call habits. In: Proceedings of the 3rd International Conference on the Analysis of Mobile Phone Datasets; <Date>2013 May 1–3</Date>; Cambridge, MA, USA; 2013.

[4]

Gabrielli L, Furletti B, Trasarti R, Giannotti F, Pedreschi D. City users’ classification with mobile phone data. In: Ho H, Ooi BC, Zaki MJ, Hu X, Haas L, Kumar V, ., editors Proceedings of the 2015 IEEE International Conference on Big Data; <Date>2015 Oct 29–Nov 1</Date>; Santa Clara, CA, USA; 2015. p. 1007–12.

[5]

Furletti B, Gabrielli L, Giannotti F, Milli L, Nanni M, Pedreschi D. Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach. In: Proceedings of the 47th SIS Scientific Meeting of the Italian Statistical Society; <Date>2014 Jun 11−13</Date>; Cagliari, Italy; 2014.

[6]	Nanni M, Trasarti R, Furletti B, Gabrielli L, Van Der Mede P, De Bruijn J, . Transportation planning based on GSM traces: a case study on ivory coast. In: Nin J, Villatoro D, editors Citizen in sensor networks. Cham: Springer International Publishing; 2014. p. 15–25.

[7]	Pappalardo L, Simini F, Rinzivillo S, Pedreschi D, Giannotti F, Barabási AL. Returners and explorers dichotomy in human mobility. Nat Commun 2015;6:8166.

[8]

Pappalardo L, Pedreschi D, Smoreda Z, Giannotti F. Using big data to study the link between human mobility and socio-economic development. In: Ho H, Ooi BC, Zaki MJ, Hu X, Haas L, Kumar V, ., editors Proceedings of the 2015 IEEE International Conference on Big Data; <Date>2015 Oct 29−Nov 1</Date>; Santa Clara, CA, USA; 2015. p. 871–8.

[9]	Wang D, Pedreschi D, Song C, Giannotti F, Barabási AL. Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; <Date>2011 Aug 21–24</Date>; San Diego, CA, USA; 2011. p. 1100–8.

[10]	Trasarti R, Olteanu-Raimond AM., Nanni M, Couronné T, Furletti B, Giannotti F, . Discovering urban and country dynamics from mobile phone data with spatial correlation patterns. Telecommu Policy 2015;39(3−4):347–62.

[11]	Liu W, Park EK. Big data as an e-health service. In: Proceedings of the 2014 IEEE International Conference on Computing, Networking and Communications; <Date>2014 Feb 3–6</Date>; Honolulu, HI, USA; 2014. p. 982–8.

[12]

Gini R, Francesconi P, Mazzaglia G, Cricelli I, Pasqua A, Gallina P, . Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and national survey. BMC Public Health 2013;13(1):15.

[13]	Directive 2009/28/EC of the European Parliament and of the Council on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European Union L 140; <Date>2009 Jun 5</Date>. p. 16–47.

[14]	Ioakimidis CS, Oliveira LJ, Genikomsakis KN. Wind power forecasting in a residential location as part of the energy box management decision tool. IEEE Trans Ind Inform 2014;10(4):2103–11.

[15]	Bessa RJ, Miranda V, Gama J. Entropy and correntropy against minimum square error in offline and online three-day ahead wind power forecasting. IEEE Trans Power Syst 2009;24(4):1657–66.

[16]

Ceci M, Cassavia N, Corizzo R, Dicosta P, Malerba D, Maria G, . Innovative power operating center management exploiting big data techniques. In: Proceedings of the 18th International Database Engineering & Applications Symposium; <Date>2014 Jul 7−9</Date>; Porto, Portugal. New York: ACM; 2014. p. 326–9.

[17]

Ceci M, Corizzo R, Fumarola F, Ianni M, Malerba D, Maria G, . Big data techniques for supporting accurate predictions of energy production from renewable sources. In: Proceedings of the 19th International Database Engineering and Applications Symposium; <Date>2015 Jul 13−15</Date>; Yokohama, Japan New York: ACM; 2015. p. 62–71.

[18]	Bofinger S, Heilscher G. Solar electricity forecast—approaches and first results. In: Proceedings of the 21st European Photovoltaic Solar Energy Conference; <Date>2006 Sep 4−8</Date>; Dresden, Germany; 2006. p. 4–8.

[19]	Pelland S, Galanis G, Kallos G. Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model. Prog Photovoltaics 2013;21(3):284–96.

[20]	Sharma N, Sharma P, Irwin DE, Shenoy PJ. Predicting solar generation from weather forecasts using machine learning. In: Proceedings of the 2011 IEEE International Conference on Smart Grid Communications; <Date>2011 Oct 17–20</Date>; Brussels, Belgium; 2011. p. 528–33.

[21]	Stojanova D, Ceci M, Appice A, Džeroski S. Network regression with predictive clustering trees. Data Min Knowl Disc 2012;25(2):378–413.

[22]	Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I. Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing; <Date>2010 Jun 22–25</Date>; Boston, MA, USA. Berkeley: USENIX Association; 2010. p. 1765–73.

[23]	Fayyad U, Piatetsky-Shapiro G, Smyth P. The KDD process for extracting useful knowledge from volumes of data. Commun ACM 1996;39(11):27–34.

[24]	Boselli R, Cesarini M, Mercorio F, Mezzanzanica M. Planning meets data cleansing. In: Proceedings of the 24th International Conference on Automated Planning and Scheduling; 2014<Date>Jun 21–26</Date>; Portsmouth, NH, USA; 2014. p. 439−43.

[25]	Mezzanzanica M, Boselli R, Cesarini M, Mercorio F. Data quality sensitivity analysis on aggregate indicators. In: Helfert M, Francalanci C, Felipe J, editors Proceedings of the International Conference on Data Technologies and Applications;<Date> 2012 Jul 25−27</Date>; Rome, Italy; 2012. p. 97–108.

[26]	Mezzanzanica M, Boselli R, Cesarini M, Mercorio F. A model-based evaluation of data quality activities in KDD. Inform Process Manag 2015;51(2):144–66.

[27]

Amato F, Boselli R, Cesarini M, Mercorio F, Mezzanzanica M, Moscato V, . Challenge: processing web texts for classifying job offers. In: Kankanhalli MS, Li T, Wang W, editors Proceedings of the 2015 IEEE International Conference on Semantic Computing; <Date>2015 Feb 7−9</Date>; Anaheim, CA, USA; 2015. p. 460–3.

[28]	Monreale A, Rinzivillo S, Pratesi F, Giannotti F, Pedreschi D. Privacy-by-design in big data analytics and social mining. EPJ Data Sci 2014;3(1):10.

[29]	Monreale A, Andrienko G, Andrienko NV, Giannotti F, Pedreschi D, Rinzivillo S, . Movement data anonymity through generalization. Trans Data Privacy 2010;3(2):91–121.

[30]	Giannotti F, Lakshmanan LVS, Monreale A, Pedreschi D, Wang H. Privacy-preserving mining of association rules from outsourced transaction databases. IEEE Syst J 2013;7(3):385–95.

[31]	Monreale A, Wang WH, Pratesi F, Rinzivillo S, Pedreschi D, Andrienko G, . Privacy-preserving distributed movement data aggregation. In: Vandenbroucke D, Bucher B, Crompvoets J, editors Geographic information science at the heart of Europe. Cham: Springer International Publishing; 2013. p. 225–45.

[32]	Romei A, Ruggieri S. A multidisciplinary survey on discrimination analysis. Knowl Eng Rev 2014;29(5):582–638.

[33]	Romei A, Ruggieri S, Turini F. Discrimination discovery in scientific project evaluation: a case study. Expert Syst Appl 2013;40(15):6064–79.

[34]	Ruggieri S. Using t-closeness anonymity to control for non-discrimination. Trans Data Privacy 2014;7(2):99–129.

[35]	Hajian S, Domingo-Ferrer J, Monreale A, Pedreschi D, Giannotti F. Discrimination- and privacy-aware patterns. Data Min Knowl Disc 2015;29(6):1733–82.

[36]	Cafarella MJ, Halevy A, Wang ZD, Wu E, Zhang Y. WebTables: exploring the power of tables on the web. In: Proceedings of the Very Large Database Endowment; <Date>2008 Aug 23–28</Date>; Auckland, New Zealand; 2008. p. 538–49.

[37]	Bizer C, Heath T, Berners-Lee T. Linked data: the story so far. In: Sheth A, editor Semantic services, interoperability and web applications: emerging concepts. Hershey: IGI Global; 2011. p. 205–27.

[38]	Batini C, Rula A, Scannapieco M, Viscusi G. From data quality to big data quality. J Database Manage 2015;26(1):60–82.

[39]	Firmani D, Mecella M, Scannapieco M, Batini C. On the meaningfulness of “Big Data Quality”. Data Sci Eng 2016;1(1):6–20.

[40]	Dong XL, Srivastava D. Big data integration. In: Proceedings of the Very Large Databases Endowment; <Date>2013 Aug 26−30</Date>; Trento, Italy; 2013. p. 1188–9.

[41]	Madhavan J, Jeffery SR, Cohen S, Dong XL, Ko D, Yu C, . Web-scale data integration: you can afford to Pay As You Go. In: Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research; <Date>2007 Jan 7–10</Date>; Asilomar, CA, USA; 2007. p. 342–50.

[42]	Papadakis G, Ioannou E, Palpanas T, Niederée C, Nejdl W. A blocking framework for entity resolution in highly heterogeneous information spaces. IEEE Trans Knowl Data En 2013;25(12):2665–82.

[43]	Papadakis G, Koutrika G, Palpanas T, Nejdl W. Meta-blocking: taking entity resolution to the next level. IEEE Trans Knowl Data En 2014;26(8):1946–60.

[44]	Papadakis G, Papastefanatos G, Koutrika G. Supervised meta-blocking. In: Proceedings of the Very Large Databases Endowment; <Date>2014 Sep1–5</Date>; Hangzhou, China.; 2014. p. 1929–40.

[45]	Bergamaschi S, Ferrari D, Guerra F, Simonini G. Discovering the topics of a data source: a statistical approach. In: Proceedings of the Workshop on Surfacing the Deep and the Social Web Co-located with the 13th International Semantic Web Conference; <Date>2014 Oct 19</Date>; Trentino, Italy; 2014.

[46]	Bergamaschi S, Simonini G, Zhu S. Enhancing big data exploration with faceted browsing. In: Proceedings of the 10th Scientific Meeting of Classification and Data Analysis Group; <Date>2015 Oct 8-10</Date>; Cagliari, Italy; 2015.

[47]	Fagan JC. Usability studies of faceted browsing: a literature review. Inform Technol Libr 2010;29(2):58–66.