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**Title**: Developing recommendation systems to support open-source software developers: challenges and lessons learned
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**Abstract:** Open-source software (OSS) forges contain rich data sources useful for supporting development activities. Several techniques and tools have been promoted to provide open source developers with innovative features, aiming to obtain improvements in development effort, cost savings, and developer productivity. In the context of the EU H2020 CROSSMINER project, different recommendation systems have been conceived to assist software programmers in different phases of the development process by providing them with various artifacts, such as third-party libraries, or documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects, including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This lecture provides an introduction to Recommendation Systems in Software Engineering (RSSE) and describes the challenges that have been encountered in the context of the CROSSMINER project. Specific attention is devoted to present the intricacies related to the development and evaluation techniques that have been employed to conceive and evaluate the CROSSMINER recommendation systems. The lessons that have been learned while working on the project are also discussed.
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**Teacher: Davide Di Ruscio** is an Associate Professor at the Department of Information Engineering Computer Science and Mathematics of the Univ. of L'Aquila. His main research interests are related to several aspects of Software Engineering and Model-Driven Engineering, including domain-specific modelling languages, model evolution, low-code development, and recommendation systems. He has published more than 150 papers in international journals and conference proceedings on such topics. He has been in the PC and involved in several international events and reviewer of top journals, including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, Empirical Software Engineering Journal, Software and Systems Modeling, and J. of Systems and Software. Davide is on the editorial boards of the International Journal on Software and Systems Modeling (SoSyM), of IEEE Software, of the Journal of Object Technology, and of the IET Software journal. Since 2006 Davide has been working on national and international research projects, contributing to the application of MDE techniques and tools in various application domains, including service-based software systems, autonomous systems, mining of open-source systems, and modeling hybrid polystore systems. Davide has been the technical and scientific coordinator of the EU H2020 CROSSMINER project and principal investigator for the University of L'Aquila of the EU H2020 TYPHON project. (http://people.disim.univaq.it/diruscio/)
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Prodotto a partire dal [[Elenco_titoli_e_pubblicazioni_37513.pdf]]
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Davide Di Ruscio is Associate Professor at the Dep. of Information Engineering Computer Science and Mathematics of the Univ. of L’Aquila. His main research interests are related to several aspects of Software Engineering and Model-Driven Engineering, including domain-specific modelling languages, model evolution, low-code development, and recommender systems. He has published more than 190 papers in international journals and conference proceedings on such topics. He has been in the PC and involved in the organisation of several international events, and reviewer of top journals including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, Empirical Software Engineering Journal, Software and Systems Modeling, and J. of Systems and Software (http://people.disim.univaq.it/diruscio/)
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MAIN COORDINATION ACTIVITIES IN RESEARCH PROJECTS
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2017-2020: Technical Leader for the University of L'Aquila of the EU H2020 MegaM@Rt2 project
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since 2019: co-PI of the EU H2020-MSCA-ITN-2018 project "Large-scale Repository and Services for Low-code Engineering"
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2017-2019: PI, Scientific and Technical Leader, Workpackage Leader for the EU H2020 CROSSMINER project
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since 2018: PI for the University of L'Aquila and Workpackage Leader of the EU H2020 TYPHON project
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since 2018: Steering committee member of the project POR FESR ABRUZZO “ConnectPA”
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2014-2017: Technical Leader of the MiSE project "Smart digital ecosystem for the Customer Experience Enhancement"
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2012-2015: co-PI for the University of L'Aquila and Workpackage Leader of the EU FP7 STREP OSSMETER project
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2008-2011: Workpackage Leader of the EU FP7 MANCOOSI project
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MAIN RESEARCH PROJECT PARTICIPATIONS
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2015-2017 "Flexor: FLEXIBLE MODEL-DRIVEN ENGINEERING FOR MOBILE, OPEN, DYNAMIC DATA SYSTEMS" Financing Institution: Spanish Ministry of Economy and Competitivity
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2012-2013: "F.A.R.M. Free Architecture and Rational Methodology POR FESR ABRUZZO"
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2010-2013: European IP FP7 CHOReOS project
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2006-2009: European IP FP6 PLASTIC project
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CURRENT PHD STUDENTS
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-I. Felicien, working on low-code techniques and tools supporting the development of IoT systems
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-C. Di Sipio, working on the low-code development of recommendation systems
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-A. Indamutsa, working on reuse and integration of low-code artifacts
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-A. Sahay, working on mechanisms to compose model transformations in low-code development environments
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-R. Rubei, working on the definition of recommendation systems supporting the development of complex software systems
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CURRENT POST-DOCS
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-Dr. J. Di Rocco (co-advised with Prof. A. Pierantonio) working on traceability mechanisms to model management in MDE
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-Dr. P. Nguyen working on the development of recommendation systems to support the development of complex software systems
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PAST STUDENT SUPERVISIONS
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-Dr. G. Fiore, post-doc on the project “Engineering the Software of Mission Critical Unmanned Vehicles”
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-M. GRUENPETER, research associate on the project “Ontologie e matadati per la classificazione di progetti Open Source” under the co-supervision of Prof. Roberto Di Cosmo (Inria, Paris Diderot University Francia)
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-G. Luca, research associate on the MiSE project “Smart digital ecosystem for the Customer Experience Enhancement”
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-F. Di Paolo, research associate on the MiSE project “Smart digital ecosystem for the Customer Experience Enhancement”
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ROLES IN INTERNATIONAL JOURNALS
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since 2020: Editorial Board Member IEEE Software
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since 2019: Editorial Board Member J. of Object Technology (JOT)
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since 2019: Editorial Board Member Springer Int. Journal on Software and Systems Modeling (SoSyM)
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since 2017: Editorial Board Member IET Software J.
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2015-2016: Guest Editor of the special issue “Flexible Model Driven Engineering” of the Elsevier Computer Languages, Systems & Structures j.
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2014: Guest Editor of the special issue “Success Stories in Model Driven Engineering” of the Elsevier Science of Computer Programming j.
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2013: Guest Editor of the special issue “Experimental Software and Toolkits” of the Elsevier Science of Computer Programming j.
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2012-2013: Guest Editor of the special issue “Extreme Modeling” of the J. of Object Technology
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2011-2012: Guest Editor of the special issue “Model Comparison In Practice” of the J. of Object Technology
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PHD EVALUATION COMMITTEES
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2020: I. Berrouyne "A Model-Driven Methodology to Unify Software Engineering in the Internet of Things" Supervisor Prof. M. Tisi, IMT Atlantique, Nantes (France)
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2020: P. NEUBAUER "A Framework for Modernizing Domain-Specific Languages" Supervisor Prof. M. Wimmer, Technische Universität Wien (Austria)
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2020: M. Marthinsen "Automatic Model Repair Using Machine Learning" Supervisor A. Rutle, Department of Informatics, University of Bergen
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2018: C. Debreceni "Advanced Techniques and Tools for Secure Collaborative Modeling" Supervisor Prof. D. Varro Budapest University of Technology and Economics
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2017: H. Mohammad "A Query Structured Model Transformation Approach" Supervisors Dr. Tom Maibaum, Dr. Z. Diskin McMaster University (Canada)
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2017: J. J. López-Fernández "An agile process for the example-driven development of modelling languages and environments" Supervisors Prof. E. Guerra, Prof. J. de Lara Universidad Autónoma de Madrid (Spain)
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2016: D. B. Caballero "On the Quality Properties of Model Transformations: Performance and Correctness" Supervisors Prof. A. Vallecillo, Dr. M. Wimmer University of Malaga (Spain)
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2015: A. Bucaioni “Raising Abstraction in Timing Analysis for Vehicular Embedded Systems through ModelDriven Engineering” Supervisors M. Sjödin, A. Cicchetti, F. Ciccozzi
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2011: J. L. Canovas Izquierdo "Domain-Specic Languages for bridging modelware with grammarware, relational data and API Technical Spaces" Supervisor Prof. J. G. Molina Universidad De Murcia (Spain)
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PROJECT EVALUATIONS
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2018-2019: Reviewer for the National Centre of Science and Technology, Ministry of Education and Science, Republic of Kazakhstan
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2016: Reviewer for the EU COST Association -Open Call OC-2016-1 in the roles of EXTERNAL EXPERT and RAPPORTEUR
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2015: Reviewer for the Austrian Science Fund
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STEERING COMMITTEES
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2013-2018: Int. Conf. on Model Transformation (ICMT)
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since 2016: Workshop on Modelling in Software Engineering at ICSE (MiSE)
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since 2015: Software Language Engineering (SLE) Conf.
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since 2014: Seminar Series on Advanced Techniques & Tools for Software Evolution (SATTOSE)
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MAIN ACADEMIC SERVICES
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since 2018: Management support of the PhD program in INGEGNERIA E SCIENZE DELL'INFORMAZIONE Università degli Studi dell'Aquila
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since 2017: Member of the executive committee of the PhD program in INGEGNERIA E SCIENZE DELL'INFORMAZIONE Università degli Studi dell'AQUILA
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since 2017: Member of the DEWS Excellence center on "Design methodologies of Embedded controllers, Wireless interconnect and System-on-Chip"
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since 2017: Member of "Laboratorio CINI per SmartCities & Communities"
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MAIN INVITED TALKS
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2018 “Use of MDE to Analyse Open Source Software” Model Management And Analytics, Portugal
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2017 “The Role of Models in Engineering the Software of Robotic System” MORSE Workshop at STAF, Germany
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2017 “Developer-Centric Knowledge Mining from Large Open-Source Software Repositories” OW2Con, France
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2017 “The CROSSMINER project” Eclipse Con, France
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2016 “A Classification of Collaborative Model-Driven Software Engineering Approaches: Opportunities and Challenges” COMMitMDE at MODELS, France
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2016 “MDEForge an extensible software-as-a-service modeling platform” SATToSE, Norway
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2012 “Co-evolution in Model Driven Engineering” SoTeSoLA Summer School, University of Koblenz-Landau, Germany
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2011 "Adopting MDE to support the evolution of component-based FOSS systems” INGI Fall Doctoral School Day, Univ. catholique de Louvain
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2011 “Managing the evolution of F/OSS with Model-Driven Techniques” GTTSE summer school, Portugal
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MAIN CONFERENCE ORGANISATIONS AND PROGRAM COMMITTEES
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2023 PC Member ICSE2023
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2021 PC Member ICSE2021 Artifact Evaluation Track
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2021 Program Board Member ACM/IEEE 24th Int. Conf. on Model Driven Engineering Languages and Systems
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2021 PC Member of ESEC/FSE 2021
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-2020/2019/2017: PC Member of the ACM/IEEE Int. Conf. on Model Driven Engineering Languages and Systems
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2019/2020 PC Member of the European Conf. on Modelling Foundations and Applications
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2020/2019/2019 PC Member of Euromicro Conf. on Software Engineering and Advanced Applications
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2018 Program Board Member 21th Int. Conf. on Model Driven Engineering Languages and Systems
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2018 Program Co-Chair 11th Int. Conf. on the Quality of Information and Communications Technology -thematic track on Quality Aspects in Model-Driven Engineering
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2015 Program Co-Chair 8th Software Language Engineering Conf.
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2015 Workshop Chair STAF 2015, 4th edition of Software Technologies: Applications and Foundations Conf.
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2014 Program Co-Chair Int. Conf. on Model Transformation
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2013 Formal Demo Selection Committee, 35th Int. Conf. on Software Engineering
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2013/2014 Int. Conf. on Software Language Engineering
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Overall, Davide has served as PC member for more than 90 international events including workshops, doctoral symposiums, and tool tracks co-located with international conferences including ICSE, MODELS, and ECMFA. He has also organised the ACM Student Research Competition sponsored by Microsoft Research in the context of MODELS 2020, 2017, and 2013
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AWARDS
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2020: Most Influential Paper Award (MIP) received at the 13th ACM SIGPLAN Int. Conf. on Software Language Engineering (SLE) for the paper: R. Eramo, A. Pierantonio, D. Di Ruscio and A. Cicchetti: JTL: a bidirectional and change propagating transformation language. SLE 2010
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2019: Best paper award: P. T. Nguyen, J. Di Rocco, D. Di Ruscio, A. Pierantonio, L. Iovino "Automated Classification of Metamodel Repositories: A Machine Learning Approach" (MODELS 2019)
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2017: Best reviewer award at ACM/IEEE 20th Int. Conf. on Model Driven Engineering Languages and Systems
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2013: Winner of the context “20 talenti per l’Italia” Working Capital Telecom Italia
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[[CV_text_removed]]
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# TO BE IGNORED
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ATTRIBUZIONE INCARICO DI INSEGNAMENTO: Guest Lecturer at the “Model Driven Engineering” Master Course 2013/2014 – Prof. Juan de Lara Universidad Autónoma de Madrid (Spain). Lecture title: “Coupled Evolution in Model Driven Engineering” dal 06-08-2013 al 12-12-2013
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MEMBRO Commissione "Comunicazione e Promozione" del CAD di Informatica, del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica, Università degli Studi dell'Aquila dal 01-01-2016 a oggi
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MEMBRO della Commissione “Rapporti con le scuole del territorio” del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica, Università degli Studi dell'Aquila dal 23-02-2017 a oggi
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MEMBRO della Commissione "Scuole e Orientamento" del CAD di Informatica, del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica, Università degli Studi dell'Aquila dal 08-03-2017 a oggi
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MEMBRO COMMISSIONIE ORIENTAMENTO di Ateneo come rappresentante per il placement del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica dell'Università degli Studi dell'Aquila dal 14-10-2018 a oggi
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**Responsabilita' di studi e ricerche scientifiche affidati da qualificate istituzioni pubbliche o private**
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TECHNICAL LEADER: Progetto Regionale POS/FESR Project FARM, Budget EUR 850,000 dal 01-01-2012 al 31-12-2013 RESPONSABILE del Laboratorio Open Source istituito nell'ambito del progetto RIDITT "Ricostruire" (finanziato dal Ministero dello Sviluppo Economico) Trasferimento tecnologico e creazione di nuove imprese nell'ambito delle tecnologie ICT avanzate applicate allo sviluppo economico e territoriale post sisma.
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CO-RESPONSABILE del progetto FLYAQ co-finanziato da Telecom Italia nell'ambito dell'iniziativa “20 talenti per l’Italia” Working Capital. L'iniziativa è di Telecom Italia per favorire la nascita di giovani aziende digitali e "green".
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CONTO TERZI per attività di consulenza a supporto dello sviluppo della piattaforma software E-Health Technology. Lo studio è stato finanziato dalla regione Abruzzo nell’ambito del POR FESR 2007-2013 per supportare l’implementazione di progetti di ricerca industriale e sviluppo sperimentale. Il dominio del progetto è stato quello dell’e-Health con particolare focus a servizi innovativi fruibili mediante servizi Web e tecnologie mobile. dal 01-01-2013 al 01-01-2015
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CO-RESPONSABILE Progetto EXPO ABRUZZO, Budget EUR 75,000 dal 01-03-2014 al 31-12-2015 CO-RESPONSABILE del progetto "Smart digital ecosystem for the Customer Experience Enhancement", finanziato dal MiSE numero M/0015/03/X23. Il budget totale del progetto è stato €2.2M di cui €450’450 per l'Università degli Studi dell'Aquila. dal 13-10-2014 al 12-10-2017
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RESPONSABILE DELLA MEMBERSHIP AGREEMENT tra Eclipse Foundation Inc. e Università degli Studi dell’Aquila (UDA). La membership dà la possibilità ad UDA di promuovere proprie tecnologie e ricerche direttamente alla community Eclipse e partecipare allo sviluppo di progetti Eclipse. Tra i membri dell’Eclipse Foundation ci sono importanti player accademici ed industriali tra cui Carleton University, Carnegie Mellon University, Queen's University, Google, IBM, Oracle, etc. dal 12-01-2018 a oggi
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CO-RESPONSABILE DEL GRUPPO DI PILOTAGGIO del Progetto POR FESR Abruzzo 2014-2020 “ConnectPA”. La ricerca affidata dalla regione Abruzzo vede la partecipazione di Maggioli spa, Gruppo Metron srl, Tirasa srl, oltre all'Universita' degli Studi dell'Aquila. Lo studio riguarda lo sviluppo di nuove tecnologie per supportare la pubblica amministrazione locale a migrare verso un modello tecnologico che abiliti l'interconnessioni di servizi, sfruttando paradigmi di interoperabilità avanzati. Il costo totale del progetto per il quale è stata richiesta l'attività di pilotaggio in questione (oltre al contributo tecnico scientifico) è di circa €5.8M dal 18-07-2018 a oggi
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CO-RESPONSABILE dello studio affidato dall'USRC (Ufficio Speciale Ricostruzione Comuni del Cratere) relativamente lo sviluppo di sistemi software a supporto delle attività di ricostruzione nei comuni del cratere. dal 20-12-2019 a oggi
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## Risultati ottenuti nel trasferimento tecnologico in termini di partecipazione alla creazione di nuove imprese (spin off), sviluppo, impiego e commercializzazione di brevetti
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ATTIVITA' DI TRASFERIMENTO TECNOLOGICO con il Parco Scientifico e Tecnologico delle Marche (TecnoMarche S.r.l). Durante la collaborazione sono stati identificati, selezionati e customizzati sistemi di modellazione di processo da poter impiegare nel settore manifatturiero con il quale TecnoMarche aveva progetti di trasferimento tecnologici attivi. dal 01-01-2004 al 31-12-2005
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- ATTIVITA’ DI TRASFERIMENTO TECNOLOGICO con Neta Informatica (Mosciano Sant'Angelo) per uno studio pilota finalizzato alla generazione automatica a partire da modelli software relativi a sistemi enterprise su Web. In particolare l'obiettivo primario è stato quello di verificare la riduzione dei tempi e dei costi per diversi pattern applicativi (come ad esempio CRUD) mediante l'impiego delle Software Factory di Microsoft. dal 01-10-2007 al 30-11-2007
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- ATTIVITA’ DI TRASFERIMENTO TECNOLOGICO con Mandriva (Parigi, Francia) per la realizzazione di un simulatore per upgrade script di pacchetti Linux. dal 01-02-2008 al 31-05-2011
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- ATTIVITA’ DI TRASFERIMENTO TECNOLOGICO con Caixa Magica (Lisbona, Portogallo) per la realizzazione di un meta-installer per pacchetti debian APT e successiva verifica di applicabilità per applicazioni mobile di tipo Android. dal 01-02-2008 al 31-05-2011
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- ATTIVITA’ DI TRASFERIMENTO TECNOLOGICO con la società Zeroclock (Roma) finalizzata alla realizzazione di un generatore di piani di rientro relativa all'architettura hardware e software di una grande azienda telefonica nazionale. In particolare, si è trattato di studiare come mediante modelli e tecniche di similarità strutturale fosse possibile effettuare un confronto tra il modello della specifica ed il modello dell'architettura reale in divergenza da quella iniziale a causa di successivi interventi di manutenzione ed evoluzione. dal 01-04-2011 al 06-07-2011
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- PARTECIPAZIONE al progetto RIDITT "Ricostruire" (finanziato dal Ministero dello Sviluppo Economico) - Trasferimento tecnologico e creazione di nuove imprese nell'ambito delle tecnologie ICT avanzate applicate allo sviluppo economico e territoriale post sisma. dal 01-04-2012 al 31-03-2015
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-
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## Specifiche esperienze professionali caratterizzate da attivita' di ricerca attinenti al settore concorsuale per cui e' presentata la domanda per l'abilitazione ##
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---
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REVISORE per numerose riviste internazionali tra le più importanti: ACM Computer Surveys, Elsevier Science of Computer Programming, IEEE Transactions on Software Engineering, IEEE Software, Springer Software and System Modeling, Elsevier Journal of Systems and Software, Elsevier Information and Software Technology, Springer Empirical Software Engineering dal 01-01-2004 a oggi
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MEMBRO Commissione "Comunicazione e Promozione" del CAD di Informatica, del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica, Università degli Studi dell'Aquila dal 01-01-2016 a oggi
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MEMBRO della Commissione “Rapporti con le scuole del territorio” del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica, Università degli Studi dell'Aquila dal 23-02-2017 a oggi
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MEMBRO della Commissione "Scuole e Orientamento" del CAD di Informatica, del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica, Università degli Studi dell'Aquila dal 08-03-2017 a oggi
|
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|
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MEMBRO COMMISSIONIE ORIENTAMENTO di Ateneo come rappresentante per il placement del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica dell'Università degli Studi dell'Aquila dal 14-10-2018 a oggi
|
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MEMBRO della COMMISSIONE "Gestione delle Emergenze" del Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica dell'Università degli Studi dell'Aquila dal 22-04-2020 a oggi
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EMELIOT will contribute to research and industrial practice by conceiving and assessing innovative solutions for developing, testing, deploying and operating dependable ML-based IoT systems. In the following, the EMELIOT project's impact is discussed under different dimensions, including its potential economical and industrial impacts, and the potential key roles it can play to support the protection of cultural heritage and of the natural environment.
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The Internet of Things is rapidly gaining ground in different application domains. According to the US National Intelligence Council, <i>``by 2025 Internet nodes may reside in everyday things - food packages, furniture, paper documents, and more''</i> [Nat08]. According to a recent report by IoT analytics [IoT20], in 2020 IoT connections (i.e., those established by connected cars, smart home devices, industrial equipment, etc.) surpassed non-IoT connections for the first time. According to the same IoT analytics report, by 2025 the expected number of IoT connections will be more than 30 billion, i.e., four IoT devices per person on average. With the advent of new technology standards like 5G, IoT devices are expected to grow much faster.
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ECONOMICAL IMPACTS
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The availability of a massive amount of data from multiple sources is pushing business across the globe to collect and analyse such data. Under such circumstances, according to a recent report [Glob20], Artificial Intelligence and Machine Learning are gaining a key role in IoT enabling the automated identification of patterns, detection of anomalies, and in general they will permit different stakeholders to gain insights from the huge amount of available data. In this respect, the global AI and IoT market size is expected to grow from $6.81 billion in 2020 to $18.2 billion in 2025 [Glob20]. Moreover, according to the recent <i>Global Industry Analysts, Inc</i> report [Traj20] the global market on Machine Learning is expected to reach $27.7 Billion by 2027 growing at a rate of 38.4% over the analysis period 2020-2027. However, according to [Glob20] the lack of a skilled workforce, together with concerns related to data security, are some of the major factors, that can restrain the expected market growth.
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EMELIOT will define techniques and tools to support the whole life-cycle of ML-based IoT systems. The envisioned platform will permit teams consisting of different figures with heterogeneous expertise (e.g., data scientists, ML experts, and software engineers) to collaborate on the development and management of complex ML-based systems. The whole software life-cycle will be guided and supported by leveraging MLOps practices. Development environments and recommendation systems will be provided to support the early phases of system development; advanced testing and monitoring techniques will be devised to check the dependability of the deployed systems. Concerning security aspects, adversarial machine learning techniques will be conceived to avoid malfunction in developed machine learning models.
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|
||||
Thus, the EMELIOT adopters will benefit novel technologies to enter a promising market or even make their presence more robust. The IoT market is growing very fast, and it demands advanced approaches supporting the development of dependable ML-based IoT systems. In such a context, the availability of the right tools and technologies is crucial for ensuring economic growth in a very competitive context.
|
||||
|
||||
TECHNOLOGICAL INNOVATIONS AND IMPACTS ON INDUSTRIAL APPLICATIONS
|
||||
The adoption of the Internet-of-Things is significantly changing the way industries work: manufacturing processes involve heterogeneous interconnected smart devices, which collaboratively work on core production operations to optimise them and disclose further revenue possibilities. Digital transformation can be fully achieved only if advanced technologies are exploited to take full advantage of large amounts of data, which are collected by different and heterogeneous sources including employed industrial robots, sensors used to monitor various stages of production, and devices that monitor at real-time the overall factory effectiveness.
|
||||
|
||||
Traditionally, the management and analysis of collected data are performed by cloud services. However, according to a recent GlobeNewsire report [Edge20], the scenario is changing with the increasing spread of novel technologies, like sensors and other data-producing devices, which permit to perform analysis tasks in the network edge. According to the same report, the edge computing market is expected to grow 29.4% by 2025. To this end, dedicated techniques and tools are needed to improve the management and analysis of larger datasets in a distributed manner. EMELIOT is expected to provide relevant contributions also in this respect by devising novel architectures underpinning federated and distributed learning techniques. They will permit to optimise ML training activities by mixing the usage of cloud and on-premise services and thus, by properly distributing training tasks on different platforms. The exploitation of edge computing is considered as a key factor to foster the market growth predicted by the <i>marketdataforecast</i> report [Glob20].
|
||||
|
||||
Overall, EMELIOT will foster dependable software solutions, which can enhance industries under different perspectives. In particular, ML-based IoT systems developed, verified, and maintained with EMELIOT technologies can improve industrial applications by optimizing maintenance, reducing costs, avoiding congestion and hazards, and improving sustainability.
|
||||
|
||||
CULTURAL HERITAGE AND ENVIRONMENT
|
||||
Over the last years, several IT solutions have been proposed regarding the preservation of the environment and of Cultural Heritage (CH) sites. The availability of devices characterized by low power consumption, large connectivity, and small size, fostered the possibility of continuously monitoring monuments, art exhibitions, environmental parameters, etc., for instance, to fight vandalism, to sense structural changes in historic buildings, and to protect vulnerable environments. Machine learning techniques play a crucial role in such contexts, e.g., to improve visitors' experience, to predict structural issues of monitored buildings, which might require dedicated maintenance operations, or to predict dangerous situations, e.g., due to the air pollutant concentration in some of the monitored areas. Unfortunately, analyzing the vast amount of data produced by the monitored sites can be difficult and require advanced expertise. EMELIOT can be a precious support to develop software systems dedicated to protecting the environment and CH sites. In particular, the EMELIOT technologies will simplify the collaboration of different and heterogeneous figures, including software developers, data and climate scientists, and CH experts.
|
||||
|
||||
SCIENTIFIC AND TECHNOLOGICAL IMPACT
|
||||
The scientific and technological impact of the project will be based on top-level publications and demonstrations, and it will be articulated in many directions:
|
||||
<ul>
|
||||
<li>A novel low-code environment to model ML-based IoT systems in a systematic way;
|
||||
<li>Newfangled recommendation systems specifically conceived to support different stakeholders, all involved in the development of ML-based IoT systems. Interestingly, the envisioned tools will be able to manage in a homogeneous manner different kinds of artifacts ranging from e.g., data collected from IoT sensors, to snippets of source code that are relevant for the current development task;
|
||||
<li>Innovative approaches and technologies to support the validation and verification of specified ML-based IoT systems;
|
||||
<li>Innovative techniques and tools supporting the continuous integration, delivery and monitoring of ML-intensive IoT systems.
|
||||
<li>A novel MLOps infrastructure supporting all the aspects related to the development and operations of ML-intensive IoT systems. The infrastructure will foster a systematic collaboration and interaction of different experts involved in the development of this kind of systems including data scientists, ML experts, and software developers.
|
||||
</ul>
|
||||
|
||||
|
||||
DISSEMINATION ACTIONS
|
||||
The EMELIOT partners have identified four communities to foster the use of the envisioned techniques and tools developed in the project:
|
||||
|
||||
<ul>
|
||||
<li><B>Software developers</B> of IoT systems will create demand for the project technologies as they become aware of the substantial improvements EMELIOT provides for developing ML-intensive IoT systems. The project will carry out a wide range of dissemination actions to create awareness of the project results amongst Software Developers of IoT systems. As project results are made public, EMELIOT partners expect the dissemination actions will lead to demand for the project technologies amongst Software Developers. This demand will be addressed in some cases by the EMELIOT open-source results being adopted directly by some, but for others, this demand will be important in motivating commercial providers to adopt and exploit the project results;</li>
|
||||
|
||||
<li><B>IoT platform providers</B> are an important part of the EMELIOT dissemination strategy. Once the prototypes are developed and validated within the project, the project partners will target dissemination actions specifically towards IoT platform providers to make them aware of the benefits the project technologies will provide to their customers;</li>
|
||||
|
||||
<li><B>System Integrators</B> are a separate community targeted by EMELIOT as they often deliver IoT solutions for enterprise and government. They often have the capabilities to adopt open source technologies and utilise it within the commercial applications they deliver. The project partners will specifically target System Integrators in dissemination actions and encourage their evaluation and eventual adoption of the EMELIOT technologies for custom application development;</li>
|
||||
|
||||
<li><B>Academia and Researchers</B> communities are important because they can contribute to the evolution of the EMELIOT technologies via a sustainable open source process. Dissemination actions will target technical journals, conferences and other academic and research events and communications channels.</li>
|
||||
</ul>
|
||||
|
||||
The partners have the capabilities and existing contacts to address each of the four target markets for the project results.
|
||||
|
||||
|
||||
References
|
||||
|
||||
[Nat08] National Intelligence Council, Disruptive Civil Technologies – Six Technologies with Potential Impacts on US Interests Out to 2025 – Conference Report CR 2008-07, April 2008, [http://www.dni.gov/nic/NIC\_home.html](http://www.dni.gov/nic/NIC_home.html)
|
||||
[IoT20] [State of the IoT 2020: 12 billion IoT connections (iot-analytics.com)](https://iot-analytics.com/state-of-the-iot-2020-12-billion-iot-connections-surpassing-non-iot-for-the-first-time/)
|
||||
[Glob20] [Global Artificial Intelligence in IoT Market | Size, Trends, Forecast | 2020 - 2025 (marketdataforecast.com)](https://www.marketdataforecast.com/market-reports/artificial-intelligence-in-iot-market)
|
||||
[Traj20] [Machine Learning - Global Market Trajectory & Analytics (researchandmarkets.com)](https://www.researchandmarkets.com/reports/4806169/machine-learning-global-market-trajectory-and?utm_source=dynamic&utm_medium=BW&utm_code=h2m9f6&utm_campaign=1354544+-+Global+Machine+Learning+Market+Analysis%2c+Trends%2c+and+Forecasts+2019-2025&utm_exec=anwr281bwd)
|
||||
[Edge20] [Edge Computing Market - Growth, Trends, Forecasts (2020 - 2025) (globenewswire.com)](https://www.globenewswire.com/news-release/2020/12/16/2146204/0/en/Edge-Computing-Market-Growth-Trends-Forecasts-2020-2025.html)**
|
||||
Binary file not shown.
@@ -0,0 +1,277 @@
|
||||
---
|
||||
tags:
|
||||
- '#meetings'
|
||||
- '#projects/prin2021'
|
||||
alias:
|
||||
- "PRIN2021 TODOs"
|
||||
---
|
||||
|
||||
# Notes related to the Prin 2021 proposal
|
||||
|
||||
- Proposal: [Proposal-PRIN2020 - Google Docs](https://docs.google.com/document/d/1Ygn968RjYRHQ6JQRzdvahrpATZO7pprDl7nBwsOCfNg/edit#)
|
||||
- Folder: [Proposta PRIN - Google Drive](https://drive.google.com/drive/u/0/folders/1DyCyvIyNQH_W6IeDC5sob3H80BijiUH9)
|
||||
- Titolo del progetto: EMELIOT - Engineered MachinE Learning-intensive IoT systems
|
||||
|
||||
# Working notes
|
||||
- Stiamo puntando ad un TRL 3/4
|
||||
- Per quanto riguarda scenari/casi di studio dobbiamo menzionare qualche azienda e che andremo a usare simulatori
|
||||
-
|
||||
## TODOs
|
||||
- Sezione 1 (stato dell'arte)
|
||||
- Va messo un cappello iniziale per giustificare le linee di interesse del progetto.
|
||||
|
||||
- Sezione 2:
|
||||
- [x] **Sistemare figura 1 con piramide**
|
||||
- [x] CASE STUDY: oltre ad una revisione, richiede una integrazione per quello che riguarda gli esempi di sistemi open source, contatti industriali e simulatori
|
||||
|
||||
- Sezione 3:
|
||||
- [x] rivedere contributi nei task
|
||||
- [x] aggiungere una breve descrizione in testa al WP di vostra competenza
|
||||
- [x] aggiornare lista expertise e aggiungere una frasetta in cui si spiega il ruolo principale dell’unita’
|
||||
- [x] Aggiornare la figura delle dipendenze tra WP
|
||||
- [x] Riusciamo a mettere qualche azienda da elencare?
|
||||
- Proviamo ad avere un OK per citare qualche azienda?
|
||||
- Abbiamo la disponibilità di dati?
|
||||
- In WP4 fondere 2,4 e 3,5
|
||||
|
||||
- Sezione 4
|
||||
- [x] Ripassare
|
||||
|
||||
- [x] O5: da rivedere
|
||||
|
||||
- GENERALE
|
||||
- [x] Controllare gli acronimi
|
||||
|
||||
|
||||
# NOTES
|
||||
|
||||
## WP DESCRIPTION NOTES
|
||||
This WP fulfills O1 i.e., the definition of a low-code development platform to support the design of ML-intensive IoT systems. The different stakeholders of the platform will be provided with domain-specific languages for specifying the components building up the system under development, the data to be managed, and how to exploit them by means of ML techniques. Dedicated recommendation systems will be also defined to support users during such activities. The work is organized around three tasks:
|
||||
|
||||
## ROLE NOTES
|
||||
|
||||
- Univaq: model-driven engineering, domain specific languages, low-code development platforms, ML-based recommendation systems, IoT modeling. Main role: Conceive languages and supporting tools for designing IoT systems characterized by ML-based functionalities. In this respect, Univaq will also conceive recommendation systems to help developers to properly configure ML pipelines. Collaborations: Cooperate on the definition of IoT architectures, on the execution of CI/CD pipelines, on test case specifications, and on the development of adversarial ML techniques.
|
||||
|
||||
## REFINEMENT OF SECTION 4
|
||||
[[PRIN2021_SECTION4]]
|
||||
|
||||
|
||||
|
||||
|
||||
# Call del 19/01/2021
|
||||
- [ ] Aggiornare citazioni
|
||||
- Sullo stato dell'arte provare a fare un discorso unico
|
||||
- [ ] Controllare gli acronimi
|
||||
- Durante la call è uscito fuori https://blogs.oracle.com/il-blog-di-oracle/proxima-smart-city3a-la-smart-city-del-futuro-fatta-di-cloud2c-dati-aumentati-e-intelligenza-artificiale-di-oracle
|
||||
- [ ] Nella sezione 3 quando parliamo di consorzio menzioniamo attrezzature che abbiamo etc.
|
||||
- Riusciamo a mettere qualche azienda da elencare?
|
||||
- Proviamo ad avere un OK per citare qualche azienda?
|
||||
- Abbiamo la disponibilità di dati?
|
||||
|
||||
- [ ] Leonardo fa un passaggio sulla 2, noi dobbiamo rivedere la questione case studies
|
||||
- [x] **Sistemare figura 1 con piramide**
|
||||
- [ ] Rivedere anche Fig WP deps
|
||||
- [ ] In WP4 fondere 2,4 e 3,5
|
||||
- [ ] Aggiungere contributo nella sezioncina Role and Contribution of the research units
|
||||
- [ ] Ripassare sezione 4
|
||||
|
||||
# Call del 12/01/2021
|
||||
|
||||
- [ ] Aggiungere chiarimento Fig. 1 per dire che quella e' una architettura di riferimento e che le varie applicazioni istanziano i vari layer con predominanza diversa
|
||||
- [ ] Stiamo puntando ad un TRL 3/4
|
||||
- [ ] IN OB5 introdurre tra gli obiettivi che andremo a fornire un testbed
|
||||
- [ ] Per quanto riguarda scenari/casi di studio dobbiamo menzionare qualche azienda e che andremo a usare simulatori
|
||||
- [ ] Raffinare titoli task di WP3
|
||||
- [ ] Menzionare il proprio contributo negli altri WP cosi da generare la tabella
|
||||
- [ ] Ci sara' poi un paragrafo in cui si dirà in generale il contributo di ogni partner nei vari partner
|
||||
- [ ] Nella sezione di impatto fare riferimento agli scenari soprattutti se questi sono importanti da un punt di vista sociale etc. Vedere comunque scheda sulle linee guida per i revisori.
|
||||
- [ ] Per quanto riguarda la sezione 1 (stato dell'arte) va messo un cappello iniziale per giustificare le linee di interesse del progetto.
|
||||
- [ ] Aggiornare CV e selezionare pubblicazioni.
|
||||
|
||||
# Call del 5/01/2021
|
||||
|
||||
- [x] Aggiungere Edge in Fig. 1
|
||||
- [x] Guardare [MLOps: Continuous delivery and automation pipelines in machine learning (google.com)](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)
|
||||
- [ ] Fare una passata per sottolineare le specificità ML intensive nel contesto IoT rispetto ai sistemi tradizionali.
|
||||
- [x] Pensare ad un titolo/acronimo
|
||||
- [x] Fare budget [BUDGET_PI_PRIN_2020_c.xlsx - Google Sheets](https://docs.google.com/spreadsheets/d/1jQWdkDvyphpDNmsNn3aL0PlYu0EPTasR/edit#gid=1568281331)
|
||||
- 1.2M Euro costo totale del progetto.
|
||||
- Non c'e' ne minimo e ne massimo al co-finanziamento.
|
||||
- Tabelle stipendiali [[tabelle-definitive-docenti-e-ricercatori-dal-01-01-2019.pdf]]
|
||||
- [x] Lavorare su sezione 4
|
||||
|
||||
# Call del 29/12/2020
|
||||
|
||||
Far trasparire di piu' la presenza di strumenti a supporto dello sviluppo di sistemi ML-based.
|
||||
|
||||
- Il nostro principale stackeholder e' l'ingegnere del software che non ha competenze di machine learning.
|
||||
- Difficoltà intrinsiche di sistemi IoT
|
||||
- Enfatizzare la combinazione di diverse competenze che sono necessarie per sviluppare sistemi IoT che sono intrinsicamente complessi.
|
||||
- Necessità di mettere insieme le varie parti insieme
|
||||
- Architetture, strumenti model-driven per chiudere il gap tra gli stackholder e gli strumenti necessari
|
||||
- Noi ci rivolgiamo ad un team interdisciplinare in cui trovi dentro data scientist, esperto di machine learning, sviluppatore software, etc.
|
||||
- Invece di identificare la figura specifica, e' meglio fermarsi a livello di ** team interdisciplinare **
|
||||
- Il contesto che sia distribuito sia un data di fatto. E quindi la parte di federated learning va sviluppata a partire da questo. Sempre comunque da un punto di vista ingegneristico (non vogliamo dare contributi nell'aria machine learning). Noi partiamo da modelli machine learning esistenti e vogliamo studiare come poterli integrare in piu' complessi etc. Vediamo l'algoritmo come black box, noi forniamo architetture per comporre, distribuire, etc. Ci sono forse tecniche diverse di learning e quindi potremmo pensare di fornire architetture diverse per supportrarli nei diversi scenari.
|
||||
|
||||
|
||||
## Last notes from Massimiliano
|
||||
|
||||
### Mail 1
|
||||
|
||||
```
|
||||
Ciao
|
||||
|
||||
|
||||
|
||||
per la sezione 3, vi chiederei appena possibile di:
|
||||
|
||||
\- completare l’individuazione dei task nei WP di vostra competenza. Sono in stato draft e in alcuni WP forse occorre rinominare i task usando nomi + esplicativi
|
||||
|
||||
\- aggiungere accanto a ogni task degli altri WP se intendete contribuire. Per ora lo si mette cosi’ poi faccio una tabella
|
||||
|
||||
\- nella sezione "Role and contribution of the research unit” c’e’ un elenco puntato (che poi eventualmente puo’ diventare tabella a seconda dello spazio dove dovremmo provare a elencare le expertise uniche. Mi rendo conto che non e’ banale e che ognuno potrebbe mettere tutto, ma se provassimo a mettere un paio di keyword per ciascuna unita’ (ho messo degli esempi, ma cambiateli/cancellateli) potrebbe funzionare meglio.
|
||||
|
||||
Se pero’ pensate sia una cattiva idea, lasciamo perdere
|
||||
|
||||
|
||||
|
||||
Intanto sto preparando il gantt e una versione aggiornata della figura delle dipendenze tra i wp
|
||||
|
||||
|
||||
|
||||
ciao
|
||||
|
||||
Max
|
||||
```
|
||||
## Last notes from Leonardo
|
||||
|
||||
### Mail 1 (Circa sezione 2)
|
||||
|
||||
Ciao a tutti,
|
||||
|
||||
ho lavorato ancora alla sezione 2 gestendo i commenti presenti e completandola. Al momento manca la descrizione di un obiettivo e siamo leggermente sopra i 25K caratteri. Dovremo poi ripassarla per portarla a dimensione. Al momento i todo principali sono:
|
||||
\- @Luciano: Dovresti scrivere O3 seguendo lo schema indicato (il testo “vecchio” degli obiettivi lo trovi alla fine della sezione 2)
|
||||
\- @tutti: come potrete vedere dalla nota che ho aggiunto propongo di togliere la figura su MLops e il mapping degli obiettivi (alla fine quasi tutti gli obiettivi sono ortogonali) tenendo solo il paragrafo del testo che cita la metodologia per dimostrare che siamo aware della sua esistenza e che intendiamo seguirla
|
||||
\- @tutti: ho prodotto un draft per i casi study, oltre ad una revisione, richiede una integrazione per quello che riguarda gli esempi di sistemi open source, contatti industriali e simulatori
|
||||
\- @chi lavorerà su sicurezza: l’obiettivo O2 è ora focalizzato su testing per reliability (quindi sia aspetti funzionali sia sicurezza). Mi sembra coeso, ma verificate per favore che con le varie revisioni non siano rimaste fuori delle cose importanti (btw che potremmo comunque fare senza scriverle, quindi valutiamo anche l’impatto sulla presentazione del progetto).
|
||||
\- @tutti ho scritto un draft di O5 da rivedere
|
||||
|
||||
Risolti i punti sopra la sezione 2 è pronta. Posso occuparmi io di portarla poi alla lunghezza richiesta.
|
||||
|
||||
Ciao,
|
||||
Leo
|
||||
|
||||
### Mail 2 (Circa budget)
|
||||
|
||||
Bozza Budget [[modelloA_2020W3A5FY.pdf]]
|
||||
|
||||
### Mail 3 (Circa formattazione)
|
||||
|
||||
Ciao,
|
||||
|
||||
ho aggiunto i tag html per la formattazione. Proporrei di aggiungerli quando usiamo testo in bold (tra i tag <B> e </B>), in italico (tra i tag <I> e </I>) e bullet list (<ul></ul> perla lista e <li></li> per ciascun item). Li ho già aggiunti in sezione 2. L’obiettivo è poter caricare il testo facendo semplicemente copia e incolla così da tenere sincronizzato il foglio e la versione sul sito, oltre a poter contare i caratteri effettivi.
|
||||
|
||||
Riguardo ai riferimenti bibliografici, tempo ad un certo potremmo dover optare per una versione molto più compatta: indicare il nome del solo primo autore seguito da et at., indicare la conferenza/rivista per acronimo ed eliminare qualsiasi informazioni non necessaria. A questo punto una correzione da fare all’ultimo una volta ottenuta una stesura abbastanza stabile di tutte le sezioni.
|
||||
|
||||
Ciao,
|
||||
Leo
|
||||
|
||||
## Struttura della sezione 2
|
||||
|
||||
- Motivations: _growth of IoT_, gartner emerging challenge for ICT, other paper mentioned by Davide.
|
||||
|
||||
- _Pervasiveness_ of ML in IoT systems
|
||||
|
||||
- _Dependability challenge_ and project ambition
|
||||
|
||||
- Manca un paragrafo che descriva che stiamo parlando di sistemi ML-intensive e che quindi stiamo parlando di un sistema interdisciplinare
|
||||
|
||||
- [x] Descrizione O1
|
||||
|
||||
- [x] Sezione 4 sull'impatto
|
||||
|
||||
## O1 - Development democracy
|
||||
|
||||
- Methodology: Concerning the _development democracy_ objective, EMELIOT will identify the peculiar elements characterizing ML-Intensive IoT systems. We will design the abstract syntax of a language for modeling such systems and will provide users with constructs to specify for instance data sources, data analysis tasks, machine learning techniques to be employed, etc. The language will be defined through an iterative process consisting of two main steps: a) elicitation of new concepts from the considered application domains, and b) validation of the elicited and properly formalized concepts by modeling concrete AI-Intensive IoT systems.
|
||||
- Results: EMELIOT will propose a novel low-code development platform, which will provide adopters with user-friendly graphical interfaces, and drag-and-drop utilities, allowing citizen developers to build ML-Intensive IoT systems. The envisioned platform will be endowed with recommendation systems that will provide teams with recommendations that are relevant for the particular task at hand. For instance, ML experts will get guidance to select and properly configure ML models that might used for the particular IoT system being developed. Similarly, software engineer will get recommendations, e.g., source code snippets, API function calls, etc. which can be employed to develop the IoT system under analysis.
|
||||
- Assessment strategy: EMELIOT will assess the benefits of the envisioned low-code development platform within the context of representative use cases. Thus, we aim at involving interested industrial contacts to quantify their experiences in adopting the proposed platform e.g., in terms of development effort, potential cost savings and developer productivity.
|
||||
- Relevance of the results for the advancement of knowledge: To the best of our knowledge, there is no concrete methodology and supporting tools for designing and developing ML-intensive IoT systems that particularly focus on fostering the systematic collaboration of interdisciplinary teams consisting of different stakeholders including software engineers, data scientist, and ML experts. Particularly relevant will be the low-code development environment endowed with recommendation systems capable of providing users with building blocks and suggestions, which are relevant for the current development tasks.
|
||||
|
||||
## Sezione 4 (Impatto): Possible application potentialities and scientific and/or technological and/or social and/or economic impact, with indications of the possible use of research infrastructures
|
||||
|
||||
EMELIOT will contribute to research and industrial practice by conceiving and assessing innovative solutions for developing, testing, deploying and operating dependable ML-based IoT systems.
|
||||
|
||||
Internet of Things is rapidly gain ground in different application domains. According to the US National Intelligence Council "by 2025 Internet nodes may reside in everyday things - food packages, furniture, paper documents, and more" [1]. According to a recent report by IoT analytics [2] in 2020, for the first time, IoT connections (i.e., those established by connected cars, smart home devices, industrial equipment, etc.) surpassed non-IoT connections for the first time. According to the same IoT analytics report, by 2025 the expected number of IoT connections will be more than 30 billion, i.e., 4 IoT devices per person on average. With the advent of new like technology standards like 5G, IoT devices are expected to continue to grow much faster.
|
||||
|
||||
The availability of a massive amount of data from multiple sources is pushing business across the globe to collect and analyze such data. Under such circumstances, according to a recent report [3], Artificial Intelligence and Machine Learning are gaining a key role in IoT enabling the automated identification of patterns, detection of anomalies, and in general they will permit different stakeholders to gain insights from the huge amount of available data. In this respect, the global AI in IoT market size is expected to grow from $ 6.81 billion in 2020 to $18.2 billion in 2025 [3]. Moreover, according to the recent _Global Industry Analysts, Inc_ report [4] the global marked on Machine Learning is expected to reach $27.7 Billion by 2027 growing at a rate of 38.4% over the analysis period 2020-2027. However, according to [3] the lack of skilled workforce, together with concerns related to data security, are some of the major factors, which can restrain the expected market growth.
|
||||
|
||||
EMELIOT will define techniques and tools to support the whole life-cycle of ML-based IoT systems. The envisioned platform will permit teams consisting of different figures with heterogeneous expertise (e.g., data scientists, ML experts, and software engineers) to collaborate on the development of ML-based systems. The whole software lifecycle will be guided and supported by leveraging MLOps practices. Development environments and recommendation systems will be provided to support the early phases of the system development; advanced testing and monitoring techniques will be devised to check the dependability of the deployed systems. Concerning security aspects, adversarial machine learning techniques will be devised to avoid malfunction in developed machine learning models.
|
||||
|
||||
Traditionally, data management and analysis are performed by cloud services. However, according to a recent GlobeNewsire report [5], the scenario is changing with the increasing spread of novel technologies, like sensors and other data-producing devices, which permit to perform analysis tasks in the network edge. According to the same report, the edge computing market is expected to grow of 29.4% by 2025. To this end, dedicated techniques and tools are needed to improve the management and analysis of larger datasets in a distributed manner. EMELIOT is expected to provide relevant contributions also in this respect by devising novel architectures underpinning federated and distributed learning techniques. They will permit to optimize ML training activities by mixing the usage of cloud and on-premise services and thus, by properly distributing training tasks on different platforms. The exploitation of edge computing is considered as a key factor to foster the market growth predicted by the _marketdataforecast_ report [3].
|
||||
|
||||
The scientific and technological impact of the project will be based on top-level publications and demonstrations, and it will be articulated in many directions:
|
||||
|
||||
(i) A novel low-code environment to model ML-based IoT systems in a systematic way;
|
||||
(ii) Newfangled recommendation systems specifically conceived to support different stakeholders, all involved in the development of ML-based IoT systems. Interestingly, the envisioned tools will be able to manage in a homogeneous manner different kinds of artifacts ranging from e.g., data collected from IoT sensors, to snippets of source code that are relevant for the current development task;
|
||||
(iii) Innovative approaches and technologies to support the validation and verification of specified ML-based IoT systems;
|
||||
(iv) Innovative techniques and tools supporting the continuous integration, delivery and monitoring of ML-intensive IoT systems.
|
||||
(v) A novel MLOps infrastructure supporting all the aspects related to the development and operations of ML-intensive IoT systems. The infrastructure will foster a systematic collaboration and interaction of different experts involved in the development of this kind of systems including data scientists, ML experts, and software developers.
|
||||
|
||||
The EMELIOT partners have identified four communities to foster the use of the envisioned techniques and tools developed in the project:
|
||||
|
||||
- **Software developers** of IoT systems will create demand for the project technologies as they become aware of the substantial improvements the EMELIOT provides for developing ML-intensive IoT systems. The project will carry out a wide range of dissemination actions to create awareness of the project results amongst Software Developers of IoT systems. As project results are made public, EMELIOT partners expect the dissemination actions will lead to demand for the project technologies amongst Software Developers. This demand will be addressed in some cases by the EMELIOT open source results being adopted directly by some, but for others this demand will be important in motivating commercial providers to adopt and exploit the project results;
|
||||
|
||||
- **IoT platform providers** are an important part of the EMELIOT exploitation strategy. Once the prototypes are developed and validated within the project, the project partners will target dissemination actions specifically towards IoT platform providers to make them aware of the benefits the project technologies will provide to their customers;
|
||||
|
||||
- **System Integrators** are a separate community targeted by the EMELIOT as they often deliver IoT solutions for enterprise and government. They often have the capabilities to adopt open source technologies and utilise it within the commercial applications they deliver. The project partners will specifically target System Integrators in dissemination actions and encourage their evaluation and eventual adoption of the EMELIOT technologies for custom application development;
|
||||
|
||||
- **Academia and Researchers** communities are important because they can contribute to the evolution of the EMELIOT technologies via a sustainable open source process. Dissemination actions will target technical journals, conferences and other academic and research events and communications channels.
|
||||
|
||||
The partners have the capabilities and existing contacts to address each of the four target markets for the project results.
|
||||
|
||||
**References**
|
||||
|
||||
[1] National Intelligence Council, Disruptive Civil Technologies – Six Technologies with Potential Impacts on US Interests Out to 2025 – Conference Report CR 2008-07, April 2008, <http://www.dni.gov/nic/NIC_home.html>
|
||||
[2] [State of the IoT 2020: 12 billion IoT connections (iot-analytics.com)](https://iot-analytics.com/state-of-the-iot-2020-12-billion-iot-connections-surpassing-non-iot-for-the-first-time/)
|
||||
[3] [Global Artificial Intelligence in IoT Market | Size, Trends, Forecast | 2020 - 2025 (marketdataforecast.com)](https://www.marketdataforecast.com/market-reports/artificial-intelligence-in-iot-market)
|
||||
[4] [Machine Learning - Global Market Trajectory & Analytics (researchandmarkets.com)](https://www.researchandmarkets.com/reports/4806169/machine-learning-global-market-trajectory-and?utm_source=dynamic&utm_medium=BW&utm_code=h2m9f6&utm_campaign=1354544+-+Global+Machine+Learning+Market+Analysis%2c+Trends%2c+and+Forecasts+2019-2025&utm_exec=anwr281bwd)
|
||||
[5] [Edge Computing Market - Growth, Trends, Forecasts (2020 - 2025) (globenewswire.com)](https://www.globenewswire.com/news-release/2020/12/16/2146204/0/en/Edge-Computing-Market-Growth-Trends-Forecasts-2020-2025.html)
|
||||
|
||||
## PREVIOUS REFERENCES
|
||||
|
||||
**
|
||||
|
||||
### [The power of combining AI and IoT | by Shanika Perera | Towards Data Science](https://towardsdatascience.com/the-power-of-combining-ai-and-iot-4db98ac9f252)
|
||||
|
||||
[State of the IoT 2018: Number of IoT devices now at 7B – Market accelerating (iot-analytics.com)](https://iot-analytics.com/state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b/)
|
||||
|
||||
Spending on machine learning is estimated to reach $57.6 billion by 2021, a compound annual growth rate (CAGR) of 50.1%
|
||||
|
||||
Minonne, Andrea; Schubmel, David; George, Jebin; Piña, Jeronimo; Danqing Cai, Jessie; Leung, Jonathan; Dimitrov, Lubomir; Ranjan, Manish; Daquila, Marianne; Kumar, Megha; Iwamoto, Naoko; Anand, Nikhil; Carnelley, Philip; Membrila, Roberto; Chaturvedi, Swati; Manabe, Takashi; Vavra, Thomas; Zhang, Xiao-Fei; Zhong, Zhenshan. ["Worldwide Semiannual Artificial Intelligence Systems Spending Guide"](https://www.idc.com/getdoc.jsp?containerId=IDC_P33198). IDC. Retrieved 25 September 2017.
|
||||
|
||||
### [Gartner 2021 top tech trends](https://drive.google.com/file/d/1Y4xk2Itq6jrEkj6ZU3hi1cRTYa-lIruJ/view?usp=sharing)
|
||||
|
||||
## **
|
||||
|
||||
**%Importanza di IoT **
|
||||
|
||||
> **[State of the IoT 2018: Number of IoT devices now at 7B – Market accelerating (iot-analytics.com)](https://iot-analytics.com/state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b/)**
|
||||
|
||||
> **[Gartner 2021 top tech trends](https://drive.google.com/file/d/1Y4xk2Itq6jrEkj6ZU3hi1cRTYa-lIruJ/view?usp=sharing)**
|
||||
> **%Importanza di ML e IoT **
|
||||
> **[The power of combining AI and IoT | by Shanika Perera | Towards Data Science](https://towardsdatascience.com/the-power-of-combining-ai-and-iot-4db98ac9f252)**
|
||||
|
||||
> Spending on machine learning is estimated to reach $57.6 billion by 2021, a compound annual growth rate (CAGR) of 50.1%
|
||||
|
||||
Minonne, Andrea; Schubmel, David; George, Jebin; Piña, Jeronimo; Danqing Cai, Jessie; Leung, Jonathan; Dimitrov, Lubomir; Ranjan, Manish; Daquila, Marianne; Kumar, Megha; Iwamoto, Naoko; Anand, Nikhil; Carnelley, Philip; Membrila, Roberto; Chaturvedi, Swati; Manabe, Takashi; Vavra, Thomas; Zhang, Xiao-Fei; Zhong, Zhenshan. ["Worldwide Semiannual Artificial Intelligence Systems Spending Guide"](https://www.idc.com/getdoc.jsp?containerId=IDC_P33198). IDC. Retrieved 25 September 2017.**
|
||||
|
||||
**%Importanza di Lowcode nella digitalizzazione**
|
||||
|
||||
> ...
|
||||
|
||||
The scientific and technological impact of the project will be based on top-level publications and demonstrations, and it will be articulated in many directions:
|
||||
%elencare qui le direzioni relative ai singoli obiettivi
|
||||
|
||||
**%Altro**
|
||||
|
||||
> [Amazon, we don’t need another AI tool or APl, we need an open AI platform for cloud and edge | VentureBeat](https://venturebeat.com/2020/12/31/mohammed-farooq-qa-how-hypergiant-is-pushing-for-an-open-ai-platform-for-modelops/amp/)
|
||||
@@ -0,0 +1,104 @@
|
||||
EMELIOT will contribute to research and industrial practice by conceiving and assessing innovative solutions for developing, testing, deploying, and operating dependable ML-based IoT systems. In the following, the EMELIOT project's impact is discussed under different dimensions, including its potential economic and industrial impacts, and the potential key roles it can play to support the protection of cultural heritage and of the natural environment.
|
||||
|
||||
The Internet of Things is rapidly gaining ground in different application domains. According to the US National Intelligence Council, <i>\\\`\\\`by 2025 Internet nodes may reside in everyday things - food packages, furniture, paper documents, and more''</i> (NIC, 2008). According to a recent report by IoT analytics (IoT-Analytics, 2020), in 2020 IoT connections (i.e., those established by connected cars, smart home devices, industrial equipment, etc.) surpassed non-IoT connections for the first time. According to the same IoT analytics report, by 2025 the expected number of IoT connections will be more than 30 billion, i.e., four IoT devices per person on average. With the advent of new technology standards like 5G, IoT devices are expected to grow much faster.
|
||||
|
||||
|
||||
|
||||
|
||||
ECONOMIC IMPACTS
|
||||
|
||||
|
||||
|
||||
The availability of a massive amount of data from multiple sources is pushing business across the globe to collect and analyze such data. Under such circumstances, according to a recent report (GAI, 2020), Artificial Intelligence and Machine Learning are gaining a key role in IoT enabling the automated identification of patterns, detection of anomalies, and in general they will permit different stakeholders to gain insights from the huge amount of available data. In this respect, the global AI and IoT market size is expected to grow from $6.81 billion in 2020 to $18.2 billion in 2025 (GAI, 2020). Moreover, according to the recent <i>Global Industry Analysts, Inc</i> report (Research Markets, 2020) the global market on Machine Learning is expected to reach $27.7 Billion by 2027 growing at a rate of 38.4% over the analysis period 2020-2027. However, according to GAI (2020) the <i>lack of a skilled workforce, together with concerns related to data security, are some of the major factors that can restrain the expected market growth</i>.
|
||||
|
||||
|
||||
|
||||
EMELIOT will define techniques and tools to support the whole life-cycle of ML-based IoT systems. The envisioned platform will permit teams consisting of different figures with heterogeneous expertise (e.g., data scientists, ML experts, and software engineers) to collaborate on the development and management of complex ML-based systems. The whole software life-cycle will be guided and supported by leveraging MLOps practices. Development environments and recommendation systems will be provided to support the early phases of system development; advanced testing and monitoring techniques will be devised to check the dependability of the deployed systems. Concerning security aspects, adversarial machine learning techniques will be conceived to avoid malfunction in developed machine learning models.
|
||||
|
||||
|
||||
|
||||
Thus, the EMELIOT adopters will benefit novel technologies to enter a promising market or even make their presence more robust. The IoT market is growing very fast, and it necessitates advanced approaches supporting the development of dependable ML-based IoT systems. In such a context, the availability of the right tools and technologies is crucial for ensuring economic growth in a very competitive context.
|
||||
|
||||
|
||||
|
||||
|
||||
TECHNOLOGICAL INNOVATIONS AND IMPACTS ON INDUSTRIAL APPLICATIONS
|
||||
|
||||
|
||||
|
||||
The adoption of the Internet-of-Things is significantly changing the way industries work. For instance, manufacturing processes involve heterogeneous interconnected smart devices, which collaboratively work on core production operations to optimise them and disclose further revenue possibilities. Digital transformation can be fully achieved only if advanced technologies are exploited to take full advantage of large amounts of data, which are collected by different and heterogeneous sources including employed industrial robots, sensors used to monitor various smart buildings, and devices that monitor at real-time the overall system effectiveness.
|
||||
|
||||
|
||||
|
||||
Traditionally, the management and analysis of collected data are performed by cloud services. However, according to a recent GlobeNewsire report (ECM, 2020), the scenario is changing with the increasing spread of novel technologies, like sensors and other data-producing devices, which permit to perform also analysis tasks in the network edge. According to the same report, the edge computing market is expected to grow 29.4% by 2025. To this end, dedicated techniques and tools are needed to improve the management and analysis of larger datasets in a distributed manner. EMELIOT is expected to provide relevant contributions in this respect by devising novel architectures underpinning federated and distributed learning techniques. They will permit to optimise ML training activities by mixing cloud and on-premise services by adequately distributing training tasks on different platforms. The exploitation of edge computing is considered as a critical factor to foster the market growth predicted by the <i>marketdataforecast</i> report (GAI, 2020).
|
||||
|
||||
|
||||
|
||||
Overall, EMELIOT will foster dependable software solutions, which can enhance industries under different perspectives. In particular, ML-based IoT systems developed, verified, and maintained with EMELIOT technologies will improve industrial applications by optimizing maintenance, reducing costs, avoiding congestion and hazards, and improving sustainability.
|
||||
|
||||
|
||||
|
||||
CULTURAL HERITAGE AND ENVIRONMENT
|
||||
|
||||
|
||||
|
||||
Over the last years, several IT solutions have been proposed for preserving the environment and Cultural Heritage (CH) sites. The availability of devices characterized by low-power consumption, extensive connectivity, and small size, fostered the possibility of continuously monitoring monuments, art exhibitions, environmental parameters, etc., for instance, to fight vandalism, to sense structural changes in historic buildings, and to protect vulnerable natural environments. Machine learning techniques can play a crucial role in such contexts, e.g., to predict structural issues of monitored facilities, which might require dedicated maintenance operations. Moreover, they can anticipate dangerous situations, e.g., due to high air pollutant concentrations in some of the monitored areas. Unfortunately, analyzing the vast amount of data produced by the monitored sites can be difficult and require advanced expertise. EMELIOT will be a precious support to develop this kind of software systems. In particular, the EMELIOT technologies will simplify the collaboration of different and heterogeneous professionals, including software developers, data and climate scientists, and CH experts that might need working together to protect CH sites of interest or specific natural environments.
|
||||
|
||||
|
||||
|
||||
|
||||
SCIENTIFIC AND TECHNOLOGICAL IMPACTS
|
||||
|
||||
|
||||
|
||||
The scientific and technological impact of the project will be based on top-level publications and demonstrations, and it will be articulated in many directions:
|
||||
|
||||
|
||||
|
||||
<ul>
|
||||
|
||||
<li>A novel low-code environment to model ML-Intensive IoT systems in a systematic way;
|
||||
|
||||
<li>Newfangled recommendation systems specifically conceived to support different stakeholders, all involved in the development of ML-Intensive IoT systems. Interestingly, the envisioned tools will be able to manage in a homogeneous manner different kinds of artifacts ranging from e.g., data collected from IoT sensors, to snippets of source code that are relevant for the current development task;
|
||||
|
||||
<li>Innovative approaches and technologies to support the validation and verification of specified ML-Intensive IoT systems;
|
||||
|
||||
<li>Innovative techniques and tools supporting the continuous integration, delivery and monitoring of ML-Intensive IoT systems.
|
||||
|
||||
<li>A novel MLOps infrastructure supporting all the aspects related to the development and operations of ML-Intensive IoT systems. The infrastructure will foster a systematic collaboration and interaction of different professionals involved in the development of this kind of systems including domain experts, data scientists, ML experts, and software developers.
|
||||
|
||||
</ul>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
DISSEMINATION ACTIONS
|
||||
|
||||
|
||||
|
||||
The EMELIOT partners have identified three communities to foster the use of the envisioned techniques and tools developed in the project:
|
||||
|
||||
|
||||
|
||||
<ul>
|
||||
|
||||
|
||||
|
||||
<li><B>Software developers</B> of IoT systems will create demand for the project technologies as they become aware of the substantial improvements EMELIOT provides for developing ML-intensive IoT systems. <i>Specific dissemination actions:</i> The project will carry out a wide range of dissemination actions to create awareness of the project results amongst Software Developers of IoT systems. Industrial and tutorial tracks of scientific and technical events will be targeted to organize demonstration and hands-on sessions where EMELIOT technologies will be shown in practice.</li>
|
||||
|
||||
|
||||
|
||||
<li><B>System Integrators</B> are a separate community targeted by EMELIOT as they often deliver IoT solutions for enterprise and government. They often have the capabilities to adopt open source technologies and utilise them within the commercial applications they deliver. <i>Specific dissemination actions:</i> The project partners will target System Integrators in dissemination actions and encourage their evaluation and eventual adoption of the EMELIOT technologies for custom application development. All the EMELIOT partners have in place several collaborations with National and International players developing complex software systems for their customers. Technical and demonstration sessions will be organized with them to show the technical results achieved in the project and the potential benefits related to the adoption of the EMELIOT technologies.
|
||||
|
||||
<li><B>Academia and Researchers</B> communities are important because they can contribute to the evolution of the EMELIOT technologies via a sustainable open source process. Dissemination actions will target technical journals, conferences and other academic and research events and communications channels. <i>Specific dissemination actions:</i>There are several different communities that will be highly impacted by the project results, including general communities of practice in software product development, machine learning, software product life-cycle management as well as IoT system modelling and analysis. The relevant research communities maintain a large number of international journals and conferences. Only very few examples of relevant scientific journals that will be targeted by the project for papers can be listed such as: IEEE Transactions on Software Engineering (IEEE), Software and Systems Modeling (Springer), Empirical Software Engineering (Springer), International Conference on Software Engineering (ICSE), International Conference on Model-Driven Engineering Languages & Systems (MODELS), International Conference on Software Maintenance and Evolution (ICSME), ACM Conference Series on Recommender Systems (RecSys), International Conference on Machine Learning (ICML), International Conference on the Internet of Things (IoT)
|
||||
|
||||
</li>
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
|
||||
|
||||
|
||||
The partners have the capabilities and existing contacts to address each of the three target markets for the project results.
|
||||
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Reference in New Issue
Block a user