2021
Conference article
Open Access
Collaborative visual environments for evidence taking in digital justice: a design concept
Erra Ugo, Capece Nicola, Lettieri Nicola, Fabiani Ernesto, Banterle Francesco, Cignoni Paolo, Dazzi Patrizio, Aleotti Jacopo, Monica Riccardo
collaborative environment Digital justice digital justice Collaborative environment spatial computing remote sensing Spatial computing Remote sensing
In recent years, Spatial Computing (SC) has emerged as a novel paradigm thanks to the advancements in Extended Reality (XR), remote sensing, and artificial intelligence. Computers are nowadays more and more aware of physical environments (i.e. objects shape, size, location and movement) and can use this knowledge to blend technology into reality seamlessly, merge digital and real worlds, and connect users by providing innovative interaction methods. Criminal and civil trials offer an ideal scenario to exploit Spatial Computing. The taking of evidence, indeed, is a complex activity that not only involves several actors (judges, lawyers, clerks, advisors) but it often requires accurate topographic surveys of places and objects. Moreover, another essential means of proof, the "judicial experiments"- reproductions of real-world events (e.g. a road accident) the judge uses to evaluate if and how a given fact has taken place - could be usefully carried out in virtual environments. In this paper we propose a novel approach for digital justice based on a multi-user, multimodal virtual collaboration platform that enables technology-enhanced acquisition and analysis of trial evidence.
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[25] Georgia Kougka, Anastasios Gounaris, and Alkis Simitsis. 2018. The many faces of data-centric workflow optimization: a survey. International Journal of Data Science and Analytics 6, 2 (2018), 81-107. https://doi.org/10.1007/s41060-018-0107-0
[26] Bruno Lepri, Nuria Oliver, Emmanuel Letouzé, Alex Pentland, and Patrick Vinck. 2018. Fair, Transparent, and Accountable Algorithmic Decision-making Processes: The Premise, the Proposed Solutions, and the Open Challenges. Philosophy & Technology 31 (12 2018). https://doi.org/10.1007/s13347-017-0279-x
[27] Chee Sun Liew, Malcolm P. Atkinson, Michelle Galea, Tan Fong Ang, Paul Martin, and Jano I. Van Hemert. 2016. Scientific Workflows: Moving Across Paradigms. Comput. Surveys 49, 4 (2016). https://doi.org/10.1145/3012429
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[31] Suresh Marru, Lahiru Gunathilake, Chathura Herath, Patanachai Tangchaisin, Marlon Pierce, Chris Mattmann, Raminder Singh, Thilina Gunarathne, Eran Chinthaka, Ross Gardler, Aleksander Slominski, Ate Douma, Srinath Perera, and Sanjiva Weerawarana. 2011. Apache Airavata: A Framework for Distributed Applications and Computational Workflows. In Proceedings of the 2011 ACM Workshop on Gateway Computing Environments (Seattle, Washington, USA) (GCE '11). ACM, New York, NY, USA, 21-28. https://doi.org/10.1145/2110486.2110490
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[33] David De Roure, Carole Goble, and Robert Stevens. 2009. The design and realisation of the Experimentmy Virtual Research Environment for social sharing of workflows. Future Generation Computer Systems 25, 5 (2009), 561 - 567. https://doi.org/10.1016/j.future.2008.06.010
[34] Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith. 2018. On the convergence of federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 3 (2018).
[35] Nalini Schaduangrat, Samuel Lampa, Saw Simeon, Matthew Paul Gleeson, Ola Spjuth, and Chanin Nantasenamat. 2020. Towards reproducible computational drug discovery. Journal of Cheminformatics 12, 1 (2020), 9. https://doi.org/10. 1186/s13321-020-0408-x
[36] A. Shaon, S. Callaghan, B. Lawrence, B. Matthews, A. Woolf, T. Osborn, and C. Harpham. 2011. A Linked Data Approach to Publishing Complex Scientific Workflows. In 2011 IEEE Seventh International Conference on eScience. 303-310. https://doi.org/10.1109/eScience.2011.49
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[38] John Violos, Vinicius Monteiro de Lira, Patrizio Dazzi, Jörn Altmann, Baseem AlAthwari, Antonia Schwichtenberg, Young-Woo Jung, Theodora Varvarigou, and Konstantinos Tserpes. 2017. User behavior and application modeling in decentralized edge cloud infrastructures. In International Conference on the Economics of Grids, Clouds, Systems, and Services. Springer, Cham, 193-203.
[39] Zhenyu Wen, Renyu Yang, Peter Garraghan, Tao Lin, Jie Xu, and Michael Rovatsos. 2017. Fog orchestration for internet of things services. IEEE Internet Computing 21, 2 (2017), 16-24.
[40] Katherine Wolstencroft, Robert Haines, Donal Fellows, Alan Williams, David Withers, Stuart Owen, Stian Soiland-Reyes, Ian Dunlop, Aleksandra Nenadic, Paul Fisher, Jiten Bhagat, Khalid Belhajjame, Finn Bacall, Alex Hardisty, Abraham Nieva de la Hidalga, Maria P. Balcazar Vargas, Shoaib Sufi, and Carole Goble. 2013. The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Research 41, W1 (2013), W557-W561. https://doi.org/10.1093/nar/gkt328
[41] Chan-Hyun Youn, Min Chen, and Patrizio Dazzi. 2017. Cloud Broker and Cloudlet for Workflow Scheduling. Springer.
[42] Y. Zhao, M. Hategan, B. Cliford, I. Foster, G. von Laszewski, V. Nefedova, I. Raicu, T. Stef-Praun, and M. Wilde. 2007. Swift: Fast, Reliable, Loosely Coupled Parallel Computation. In IEEE Congress on Services (Services 2007). 199-206. https: //doi.org/10.1109/SERVICES.2007.63
[2] Gaetano F Anastasi, Emanuele Carlini, and Patrizio Dazzi. 2013. Smart cloud federation simulations with cloudsim. In Proceedings of the first ACM workshop on Optimization techniques for resources management in clouds. 9-16.
[3] Ranieri Baraglia, Patrizio Dazzi, Barbara Guidi, and Laura Ricci. 2012. GoDel: Delaunay Overlays in P2P Networks via Gossip. In IEEE 12th International Conference on Peer-to-Peer Computing (P2P). IEEE, 1-12.
[4] Ranieri Baraglia, Patrizio Dazzi, Matteo Mordacchini, Laura Ricci, and Luca Alessi. 2011. Group: A gossip based building community protocol. In Smart spaces and next generation wired/wireless networking. Springer, Berlin, Heidelberg, 496-507.
[5] Marcello M. Bersani, Salvatore Distefano, Luca Ferrucci, and Manuel Mazzara. 2015. A Timed Semantics of Workflows. In Software Technologies, Andreas Holzinger, Jorge Cardoso, José Cordeiro, Therese Libourel, Leszek A. Maciaszek, and Marten van Sinderen (Eds.). Springer International Publishing, Cham, 365- 383.
[6] Michael R. Berthold, Nicolas Cebron, Fabian Dill, Thomas R. Gabriel, Tobias Kötter, Thorsten Meinl, Peter Ohl, Kilian Thiel, and Bernd Wiswedel. 2009. KNIME - the Konstanz Information Miner: Version 2.0 and Beyond. SIGKDD Explor. Newsl. 11, 1 (Nov. 2009), 26-31. https://doi.org/10.1145/1656274.1656280
[7] Massimiliano Bertolucci, Emanuele Carlini, Patrizio Dazzi, Alessandro Lulli, and Laura Ricci. 2015. Static and dynamic big data partitioning on apache spark.. In PARCO. 489-498.
[8] Ludäscher Bertram, Altintas Ilkay, Berkley Chad, Higgins Dan, Jaeger Efrat, Jones Matthew, Lee Edward A., Tao Jing, and Zhao Yang. [n.d.]. Scientific workflow management and the Kepler system. Concurrency and Computation: Practice and Experience 18, 10 ([n. d.]), 1039-1065. https://doi.org/10.1002/cpe.994
[9] Tobias Binz, Uwe Breitenbücher, Oliver Kopp, and Frank Leymann. 2014. TOSCA: portable automated deployment and management of cloud applications. In Advanced Web Services. Springer, 527-549.
[10] Antonio Brogi, Luca Rinaldi, and Jacopo Soldani. 2018. TosKer: a synergy between TOSCA and Docker for orchestrating multicomponent applications. Software: Practice and Experience 48, 11 (2018), 2061-2079.
[11] Leonardo Candela, Valerio Grossi, Paolo Manghi, and Roberto Trasarti. 2021. A workflow language for research e-infrastructures. International Journal of Data Science and Analytics (2021). https://doi.org/10.1007/s41060-020-00237-x
[12] Emanuele Carlini, Massimo Coppola, Patrizio Dazzi, Domenico Laforenza, Susanna Martinelli, and Laura Ricci. 2009. Service and resource discovery supports over p2p overlays. In 2009 International Conference on Ultra Modern Telecommunications & Workshops. IEEE, 1-8.
[13] Massimo Coppola, Patrizio Dazzi, Aliaksandr Lazouski, Fabio Martinelli, Paolo Mori, Jens Jensen, Ian Johnson, and Philip Kershaw. 2012. The Contrail approach to cloud federations. In Proceedings of the International Symposium on Grids and Clouds (ISGC'12), Vol. 2. 1.
[14] Gianpaolo Coro, Giancarlo Panichi, Paolo Scarponi, and Pasquale Pagano. 2017. Cloud computing in a distributed e-infrastructure using the web processing service standard. Concurrency and Computation: Practice and Experience 29, 18 (2017), e4219. https://doi.org/10.1002/cpe.4219 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.4219 e4219 cpe.4219.
[15] Marco Danelutto and Patrizio Dazzi. 2008. Workflows on top of a macro data lfow interpreter exploiting aspects. In Making Grids Work. Springer, Boston, MA, 213-224.
[16] Marco Danelutto, Patrizio Dazzi, et al. 2005. A Java/Jini Framework Supporting Stream Parallel Computations.. In PARCO. 681-688.
[17] Marco Danelutto, P Dazzi, D Laforenza, M Pasin, L Presti, and M Vanneschi. 2006. PAL: High level parallel programming with Java annotations. In Proceedings of CoreGRID Integration Workshop (CIW 2006) Krakow, Poland, Academic Computer Centre CYFRONET AGH. 189-200.
[18] Ewa Deelman, Karan Vahi, Gideon Juve, Mats Rynge, Scott Callaghan, Philip J. Maechling, Rajiv Mayani, Weiwei Chen, Rafael Ferreira da Silva, Miron Livny, and Kent Wenger. 2015. Pegasus, a workflow management system for science automation. Future Generation Computer Systems 46 (2015), 17 - 35. https: //doi.org/10.1016/j.future.2014.10.008
[19] R. Filgueira, M. Atkinson, A. Bell, I. Main, S. Boon, C. Kilburn, and P. Meredith. 2014. EScience gateway stimulating collaboration in rock physics and volcanology. Proceedings - IEEE 10th International Conference on eScience, eScience 2014 1 (2014), 187-195. https://doi.org/10.1109/eScience.2014.22
[20] Daniel Garijo, Pinar Alper, Khalid Belhajjame, Oscar Corcho, Yolanda Gil, and Carole Goble. 2014. Common motifs in scientific workflows: An empirical analysis. Future Generation Computer Systems 36 (2014), 338 - 351. https://doi.org/10. 1016/j.future.2013.09.018 Special Section: Intelligent Big Data Processing Special Section: Behavior Data Security Issues in Network Information Propagation Special Section: Energy-eficiency in Large Distributed Computing Architectures Special Section: eScience Infrastructure and Applications.
[21] Daniel Garijo, Yolanda Gil, and Oscar Corcho. 2017. Abstract, link, publish, exploit: An end to end framework for workflow sharing. Future Generation Computer Systems 75 (2017), 271 - 283. https://doi.org/10.1016/j.future.2017.01.008
[22] Fosca Giannotti, Roberto Trasarti, Kalina Bontcheva, and Valerio Grossi. 2018. SoBigData: Social Mining & Big Data Ecosystem. In Companion of the The Web Conference 2018 on The Web Conference 2018. International World Wide Web Conferences Steering Committee, 437-438.
[23] Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, and Daniel Schober. 2020. FAIR Computational Workflows. Data Intelligence 2, 1-2 (2020), 108-121. https://doi.org/10. 1162/dint_a_00033 arXiv:https://doi.org/10.1162/dint_a_00033
[24] Dongmin Kim, Hanif Muhammad, Eunsam Kim, Sumi Helal, and Choonhwa Lee. 2019. TOSCA-based and federation-aware cloud orchestration for Kubernetes container platform. Applied Sciences 9, 1 (2019), 191.
[25] Georgia Kougka, Anastasios Gounaris, and Alkis Simitsis. 2018. The many faces of data-centric workflow optimization: a survey. International Journal of Data Science and Analytics 6, 2 (2018), 81-107. https://doi.org/10.1007/s41060-018-0107-0
[26] Bruno Lepri, Nuria Oliver, Emmanuel Letouzé, Alex Pentland, and Patrick Vinck. 2018. Fair, Transparent, and Accountable Algorithmic Decision-making Processes: The Premise, the Proposed Solutions, and the Open Challenges. Philosophy & Technology 31 (12 2018). https://doi.org/10.1007/s13347-017-0279-x
[27] Chee Sun Liew, Malcolm P. Atkinson, Michelle Galea, Tan Fong Ang, Paul Martin, and Jano I. Van Hemert. 2016. Scientific Workflows: Moving Across Paradigms. Comput. Surveys 49, 4 (2016). https://doi.org/10.1145/3012429
[28] X. Llorà, B. Ács, L. S. Auvil, B. Capitanu, M. E. Welge, and D. E. Goldberg. 2008. Meandre: Semantic-Driven Data-Intensive Flows in the Clouds. In 2008 IEEE Fourth International Conference on eScience. 238-245. https://doi.org/10.1109/ eScience.2008.172
[29] Alessandro Lulli Lucchese, Laura Ricci, Emanuele Carlini, Patrizio Dazzi, and Claudio. 2015. Cracker: Crumbling Large Graphs Into Connected Components. In 20th IEEE Symposium on Computers and Communication (ISCC) (ISCC2015). IEEE.
[30] Alessandro Lulli, Emanuele Carlini, Patrizio Dazzi, Claudio Lucchese, and Laura Ricci. 2016. Fast connected components computation in large graphs by vertex pruning. IEEE Transactions on Parallel and Distributed systems 28, 3 (2016), 760- 773.
[31] Suresh Marru, Lahiru Gunathilake, Chathura Herath, Patanachai Tangchaisin, Marlon Pierce, Chris Mattmann, Raminder Singh, Thilina Gunarathne, Eran Chinthaka, Ross Gardler, Aleksander Slominski, Ate Douma, Srinath Perera, and Sanjiva Weerawarana. 2011. Apache Airavata: A Framework for Distributed Applications and Computational Workflows. In Proceedings of the 2011 ACM Workshop on Gateway Computing Environments (Seattle, Washington, USA) (GCE '11). ACM, New York, NY, USA, 21-28. https://doi.org/10.1145/2110486.2110490
[32] Mirco Nanni, Roberto Trasarti, Anna Monreale, Valerio Grossi, and Dino Pedreschi. 2016. Driving Profiles Computation and Monitoring for Car Insurance CRM. ACM Trans. Intell. Syst. Technol. 8, 1, Article 14 (Aug. 2016), 26 pages. https://doi.org/10.1145/2912148
[33] David De Roure, Carole Goble, and Robert Stevens. 2009. The design and realisation of the Experimentmy Virtual Research Environment for social sharing of workflows. Future Generation Computer Systems 25, 5 (2009), 561 - 567. https://doi.org/10.1016/j.future.2008.06.010
[34] Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith. 2018. On the convergence of federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 3 (2018).
[35] Nalini Schaduangrat, Samuel Lampa, Saw Simeon, Matthew Paul Gleeson, Ola Spjuth, and Chanin Nantasenamat. 2020. Towards reproducible computational drug discovery. Journal of Cheminformatics 12, 1 (2020), 9. https://doi.org/10. 1186/s13321-020-0408-x
[36] A. Shaon, S. Callaghan, B. Lawrence, B. Matthews, A. Woolf, T. Osborn, and C. Harpham. 2011. A Linked Data Approach to Publishing Complex Scientific Workflows. In 2011 IEEE Seventh International Conference on eScience. 303-310. https://doi.org/10.1109/eScience.2011.49
[37] Leonid Teytelman, Alexei Stoliartchouk, Lori Kindler, and Bonnie L. Hurwitz. 2016. Protocols.io: Virtual Communities for Protocol Development and Discussion. PLOS Biology 14, 8 (08 2016), 1-6. https://doi.org/10.1371/journal.pbio. 1002538
[38] John Violos, Vinicius Monteiro de Lira, Patrizio Dazzi, Jörn Altmann, Baseem AlAthwari, Antonia Schwichtenberg, Young-Woo Jung, Theodora Varvarigou, and Konstantinos Tserpes. 2017. User behavior and application modeling in decentralized edge cloud infrastructures. In International Conference on the Economics of Grids, Clouds, Systems, and Services. Springer, Cham, 193-203.
[39] Zhenyu Wen, Renyu Yang, Peter Garraghan, Tao Lin, Jie Xu, and Michael Rovatsos. 2017. Fog orchestration for internet of things services. IEEE Internet Computing 21, 2 (2017), 16-24.
[40] Katherine Wolstencroft, Robert Haines, Donal Fellows, Alan Williams, David Withers, Stuart Owen, Stian Soiland-Reyes, Ian Dunlop, Aleksandra Nenadic, Paul Fisher, Jiten Bhagat, Khalid Belhajjame, Finn Bacall, Alex Hardisty, Abraham Nieva de la Hidalga, Maria P. Balcazar Vargas, Shoaib Sufi, and Carole Goble. 2013. The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Research 41, W1 (2013), W557-W561. https://doi.org/10.1093/nar/gkt328
[41] Chan-Hyun Youn, Min Chen, and Patrizio Dazzi. 2017. Cloud Broker and Cloudlet for Workflow Scheduling. Springer.
[42] Y. Zhao, M. Hategan, B. Cliford, I. Foster, G. von Laszewski, V. Nefedova, I. Raicu, T. Stef-Praun, and M. Wilde. 2007. Swift: Fast, Reliable, Loosely Coupled Parallel Computation. In IEEE Congress on Services (Services 2007). 199-206. https: //doi.org/10.1109/SERVICES.2007.63
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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:479073,
title = {Collaborative visual environments for evidence taking in digital justice: a design concept},
author = {Erra Ugo and Capece Nicola and Lettieri Nicola and Fabiani Ernesto and Banterle Francesco and Cignoni Paolo and Dazzi Patrizio and Aleotti Jacopo and Monica Riccardo},
doi = {10.1145/3452369.3463821},
year = {2021}
}
0000-0002-6374-6657
ACCORDION
Adaptive edge/cloud compute and network continuum over a heterogeneous sparse edge infrastructure to support nextgen applications
SoBigData-PlusPlus
SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics
