2016
Journal article
Open Access
Big data research in Italy: a perspective
Bergamaschi S., Carlini E., Ceci M., Furletti B., Giannotti F., Malerba D., Mezzanzanica M., Monreale A., Pasi G., Pedreschi D., Perego R., Ruggieri S.
Job offers Smart cities Energy Engineering and Power Technology General Computer Science General Chemical Engineering Job offers Privacy Smart city General Engineering Materials Science (miscellaneous) Privacy Energy Environmental Engineering Big data
The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.
Source: Engineering (Beijing) 2 (2016): 163–170. doi:10.1016/J.ENG.2016.02.011
Publisher: Engineering sciences press, Beijing, Cina
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[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, et al., editors Proceedings of the 2013 IEEE International Conference on Big Data; 2013 Oct 6-9; 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; 2013 May 1-3; 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, et al., editors Proceedings of the 2015 IEEE International Conference on Big Data; 2015 Oct 29-Nov 1; 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; 2014 Jun 11−13; Cagliari, Italy; 2014.
[6] Nanni M, Trasarti R, Furletti B, Gabrielli L,Van Der Mede P, De Bruijn J, et al. 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, et al., editors Proceedings of the 2015 IEEE International Conference on Big Data; 2015 Oct 29−Nov 1; 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; 2011 Aug 21-24; San Diego, CA, USA; 2011. p. 1100−8.
[10] Trasarti R, Olteanu-Raimond AM., Nanni M, Couronné T, Furletti B, Giannotti F, et al. 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; 2014 Feb 3-6; Honolulu, HI, USA; 2014. p. 982-8.
[12] Gini R, Francesconi P, Mazzaglia G, Cricelli I, Pasqua A, Gallina P, et al. 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; 2009 Jun 5. 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, et al. Innovative power operating center management exploiting big data techniques. In: Proceedings of the 18th International Database Engineering & Applications Symposium; 2014 Jul 7−9; Porto, Portugal. New York: ACM; 2014. p. 326−9.
[17] Ceci M, Corizzo R, Fumarola F, Ianni M, Malerba D, Maria G, et al. Big data techniques for supporting accurate predictions of energy production from renewable sources. In: Proceedings of the 19th International Database Engineering and Applications Symposium; 2015 Jul 13−15; 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; 2006 Sep 4−8; 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; 2011 Oct 17-20; 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; 2010 Jun 22-25; 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 Jun 21-26; 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; 2012 Jul 25−27; 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, et al. 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; 2015 Feb 7−9; 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, et al. 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, et al. 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; 2008 Aug 23-28; 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; 2013 Aug 26−30; Trento, Italy; 2013. p. 1188−9.
[41] Madhavan J, Jeffery SR, Cohen S, Dong XL, Ko D, Yu C, et al. Web-scale data integration: you can afford to Pay As You Go. In: Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research; 2007 Jan 7-10; 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; 2014 Sep1-5; 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; 2014 Oct 19; 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; 2015 Oct 8-10; Cagliari, Italy; 2015.
[47] Fagan JC. Usability studies of faceted browsing: a literature review. Inform Technol Libr 2010;29(2):58−66.
[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, et al., editors Proceedings of the 2013 IEEE International Conference on Big Data; 2013 Oct 6-9; 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; 2013 May 1-3; 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, et al., editors Proceedings of the 2015 IEEE International Conference on Big Data; 2015 Oct 29-Nov 1; 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; 2014 Jun 11−13; Cagliari, Italy; 2014.
[6] Nanni M, Trasarti R, Furletti B, Gabrielli L,Van Der Mede P, De Bruijn J, et al. 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, et al., editors Proceedings of the 2015 IEEE International Conference on Big Data; 2015 Oct 29−Nov 1; 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; 2011 Aug 21-24; San Diego, CA, USA; 2011. p. 1100−8.
[10] Trasarti R, Olteanu-Raimond AM., Nanni M, Couronné T, Furletti B, Giannotti F, et al. 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; 2014 Feb 3-6; Honolulu, HI, USA; 2014. p. 982-8.
[12] Gini R, Francesconi P, Mazzaglia G, Cricelli I, Pasqua A, Gallina P, et al. 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; 2009 Jun 5. 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, et al. Innovative power operating center management exploiting big data techniques. In: Proceedings of the 18th International Database Engineering & Applications Symposium; 2014 Jul 7−9; Porto, Portugal. New York: ACM; 2014. p. 326−9.
[17] Ceci M, Corizzo R, Fumarola F, Ianni M, Malerba D, Maria G, et al. Big data techniques for supporting accurate predictions of energy production from renewable sources. In: Proceedings of the 19th International Database Engineering and Applications Symposium; 2015 Jul 13−15; 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; 2006 Sep 4−8; 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; 2011 Oct 17-20; 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; 2010 Jun 22-25; 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 Jun 21-26; 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; 2012 Jul 25−27; 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, et al. 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; 2015 Feb 7−9; 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, et al. 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, et al. 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; 2008 Aug 23-28; 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; 2013 Aug 26−30; Trento, Italy; 2013. p. 1188−9.
[41] Madhavan J, Jeffery SR, Cohen S, Dong XL, Ko D, Yu C, et al. Web-scale data integration: you can afford to Pay As You Go. In: Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research; 2007 Jan 7-10; 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; 2014 Sep1-5; 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; 2014 Oct 19; 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; 2015 Oct 8-10; Cagliari, Italy; 2015.
[47] Fagan JC. Usability studies of faceted browsing: a literature review. Inform Technol Libr 2010;29(2):58−66.
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BibTeX entry
@article{oai:it.cnr:prodotti:424127,
title = {Big data research in Italy: a perspective},
author = {Bergamaschi S. and Carlini E. and Ceci M. and Furletti B. and Giannotti F. and Malerba D. and Mezzanzanica M. and Monreale A. and Pasi G. and Pedreschi D. and Perego R. and Ruggieri S.},
publisher = {Engineering sciences press, Beijing, Cina},
doi = {10.1016/j.eng.2016.02.011},
journal = {Engineering (Beijing)},
volume = {2},
pages = {163–170},
year = {2016}
}CNR authors
Carlini, Emanuele
0000-0003-3643-5404
Furletti, Barbara0000-0003-3621-3211
Giannotti, Fosca0000-0003-3099-3835
Monreale, Anna0000-0001-8541-0284
Pedreschi, Dino0000-0003-4801-3225
Perego, Raffaele0000-0001-7189-4724
Ruggieri, Salvatore0000-0002-1917-6087
High Performance Computing (2002-ongoing)
Knowledge Discovery and Data Mining (2002-ongoing)
CNR ExploRA
ISTI Repository
DOIAlso available from
ISTI Repository
DOIAlso available from
