ABSTRACT
The process of transferring technology from research institutes to industry involves benchmarking it in exhaustive experiments to assure it reaches the established quality criteria. This is also true for the livestock domain, in which the technologies developed to sustainably raise animals production are submitted to experiments while preserving their health and wellness. However, since such institutions often conduct several parallel innovation projects, the establishment of an infrastructure to support those experiments can be costly, repetitive, and error-prone. For that purpose, we developed E-SECO, a software ecosystem that encapsulates a life-cycle model for scientific experiments and its supporting platform and actors. The main contribution of this paper is presenting how the E-SECO architecture was successfully applied to create a livestock architecture (named e-Livestock architecture) from which two different (and independent) scientific experiments involving real systems were deployed and executed in the livestock domain. The first experiment involved a Compost Barn production system, i.e., the environment and surrounding technology where bovine milk production takes place; whilst the second experiment involved an automated monitoring environment for aviaries. Preliminary results showed the effectiveness of E-SECO to (i) abstract concepts of scientific experiments for livestock domain, (ii) support reuse and derivation of an architecture to support engineering real systems for different livestock sub-domains, and (iii) support the experiments towards a future transfer of technology to industry.
- Lenita Ambrósio, Heitor Linhares, José Maria N David, Regina Braga, Wagner Arbex, Mariana Magalhães Campos, and Rafael Capilla. 2021. Enhancing the Reuse of Scientific Experiments for Agricultural Software Ecosystems. Journal of Grid Computing 19, 4 (2021), 1--24.Google ScholarDigital Library
- Christiane Bahlo, Peter Dahlhaus, Helen Thompson, and Mark Trotter. 2019. The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review. Computers and electronics in agriculture 156 (2019), 459--466.Google ScholarCross Ref
- Jan Bosch. 2009. From software product lines to software ecosystems.. In SPLC, Vol. 9. 111--119.Google Scholar
- Peter Buneman and Wang-Chiew Tan. 2019. Data provenance: What next? ACM SIGMOD Record 47, 3 (2019), 5--16.Google ScholarDigital Library
- Bin Cao, Beth Plale, Girish Subramanian, Ed Robertson, and Yogesh Simmhan. 2009. Provenance information model of karma version 3. In 2009 Congress on Services-I. IEEE, 348--351.Google ScholarDigital Library
- Tadeu Classe, Regina Braga, José Maria N David, Fernanda Campos, and Wagner Arbex. 2017. A distributed infrastructure to support scientific experiments. Journal of Grid Computing 15, 4 (2017), 475--500.Google ScholarDigital Library
- Sérgio Manuel Serra da Cruz, Marcos Bacis Ceddia, Renan Carvalho Tàvora Miranda, Gabriel Rizzo, Filipe Klinger, Renato Cerceau, Ricardo Mesquita, Ricardo Cerceau, Elton Carneiro Marinho, Eber Assis Schmitz, et al. 2018. Data Provenance in Agriculture. In International Provenance and Annotation Workshop. Springer, 257--261.Google Scholar
- Sérgio Manuel Serra da Cruz and José Antonio Pires do Nascimento. 2019. Towards integration of data-driven agronomic experiments with data provenance. Computers and Electronics in Agriculture 161 (2019), 14--28.Google ScholarDigital Library
- Simone da Silva Amorim, Eduardo Santana de Almeida, and John D McGregor. 2013. Extensibility in ecosystem architectures: an initial study. In Proceedings of the 2013 International Workshop on Ecosystem Architectures. 11--15.Google ScholarDigital Library
- Simone da Silva Amorim, Eduardo Santana de Almeida, and John D McGregor. 2014. Scalability of ecosystem architectures. In ICSA. IEEE, 49--52.Google Scholar
- Simone da Silva Amorim, John D McGregor, Eduardo Santana de Almeida, and Christina von Flach G. Chavez. 2014. Flexibility in ecosystem architectures. In ECSA Workshops. 1--6.Google Scholar
- Embrapa Gado de Leite. 2020. Brasil tem a primeira instalação de compost barn destinada a pesquisa. https://www.embrapa.br/busca-de-noticias/-/noticia/53360675/brasil-tem-a-primeira-instalacao-de-compost-barn-destinada-apesquisa.Google Scholar
- Daniel De Oliveira, Eduardo Ogasawara, Fernanda Baião, and Marta Mattoso. 2010. Scicumulus: A lightweight cloud middleware to explore many task computing paradigm in scientific workflows. In 2010 IEEE 3rd International Conference on Cloud Computing. IEEE, 378--385.Google ScholarDigital Library
- Rodrigo Pereira dos Santos, Cláudia Werner, Olavo Barbosa, and Carina Alves. 2012. Software Ecosystems: Trends and Impacts on Software Engineering. In 26th SBES. IEEE, Natal, Brazil, 206--210. Google ScholarDigital Library
- Juliana Fernandes, Valdemar Vicente Graciano Neto, and Rodrigo Pereira dos Santos. 2021. An Approach Based on Conceptual Modeling to Understand Factors that Influence Interoperability in Systems-of-Information Systems. In XX SBQS. ACM, 34:1--34:10. Google ScholarDigital Library
- Francisco Henrique Ferreira, Elisa Yumi Nakagawa, and Rodrigo Pereira dos Santos. 2021. Reliability in Software-intensive Systems: Challenges, Solutions, and Future Perspectives. In 47th SEAA. IEEE, Palermo, Italy, 54--61. Google ScholarCross Ref
- Yolanda Gil, Ewa Deelman, Mark Ellisman, Thomas Fahringer, Geoffrey Fox, Dennis Gannon, Carole Goble, Miron Livny, Luc Moreau, and Jim Myers. 2007. Examining the challenges of scientific workflows. Computer 40, 12 (2007), 24--32.Google ScholarDigital Library
- Jonas S Gomes, José Maria N David, Regina Braga, Wagner Arbex, Bryan Barbosa, Wneiton Luiz Gomes, and Leonardo M Gravina Fonseca. 2021. e-LivestockProv: An Architecture based on Provenance to Manage Traceability in Precision Livestock Farming. In Anais do I Workshop de Práticas de Ciência Aberta para Engenharia de Software. SBC, 43--48.Google Scholar
- Valdemar Vicente Graciano Neto, Rodrigo Pereira dos Santos, Davi Viana, and Renata Araujo. 2020. Towards a Conceptual Model to Understand Software Ecosystems Emerging from Systems-of-Information Systems. In Software Ecosystems, Sustainability and Human Values in the Social Web, Rodrigo Pereira dos Santos, Cristiano Maciel, and José Viterbo (Eds.). Springer, 1--20.Google Scholar
- Ian Horrocks, Peter F Patel-Schneider, Harold Boley, Said Tabet, Benjamin Grosof, Mike Dean, et al. 2004. SWRL: A semantic web rule language combining OWL and RuleML. W3C Member submission 21, 79 (2004), 1--31.Google Scholar
- Slinger Jansen, Anthony Finkelstein, and Sjaak Brinkkemper. 2009. A sense of community: A research agenda for software ecosystems. In 2009 31st ICSE-Companion. IEEE, 187--190.Google Scholar
- Sander Janssen, Erling Andersen, Ioannis N Athanasiadis, and Martin K van Ittersum. 2009. A database for integrated assessment of European agricultural systems. Environmental Science & Policy 12, 5 (2009), 573--587.Google ScholarCross Ref
- Clement Jonquet, Anne Toulet, Elizabeth Arnaud, Sophie Aubin, Esther Dzale Yeumo, Vincent Emonet, John Graybeal, Marie-Angélique Laporte, Mark AMusen, Valeria Pesce, et al. 2018. AgroPortal: A vocabulary and ontology repository for agronomy. Computers and Electronics in Agriculture 144 (2018), 126--143.Google ScholarCross Ref
- GS Karthick, M Sridhar, and PB Pankajavalli. 2020. Internet of things in animal healthcare (IoTAH): review of recent advancements in architecture, sensing technologies and real-time monitoring. SN Computer Science 1, 5 (2020), 1--16.Google ScholarCross Ref
- Vinícius C Lopes, Roberto Felício de Oliveira, and Valdemar Vicente Graciano Neto. 2021. Towards an IoT-Based Architecture for Monitoring andAutomated Decision-Making in an Aviary Environment. In Anais do XIII Congresso Brasileiro de Agroinformática. SBC, 320--328.Google Scholar
- Konstantinos Manikas. 2016. Revisiting software ecosystems research: A longitudinal literature study. Journal of Systems and Software 117 (2016), 84--103.Google ScholarDigital Library
- Paolo Missier, Khalid Belhajjame, and James Cheney. 2013. The W3C PROV family of specifications for modelling provenance metadata. In 16th EDBT. 773--776.Google Scholar
- Luc Moreau and Paolo Missier. 2013. PROV-DM: The prov data model. W3C recommendation. World Wide Web Consortium (2013).Google Scholar
- Valdemar Vicente Graciano Neto, Fábio Basso, Rodrigo Pereira dos Santos, Noor Hasrina Bakar, Mohamad Kassab, Cláudia Werner, Toacy Cavalcante de Oliveira, and Elisa Yumi Nakagawa. 2019. Model-driven engineering ecosystems. In Proceedings of the 7th SESoS and 13th WDES 2019, Montreal, QC, Canada, May 28, 2019. IEEE / ACM, 58--61. Google ScholarDigital Library
- Valdemar Vicente Graciano Neto, Rodrigo Pereira dos Santos, and Renata Mendes de Araujo. 2017. New Challenges in the Social Web: Towards Systems-of-Information Systems Ecosystems. In VIII WAIHCWS, Cristiano Maciel, José Viterbo, and Rodrigo Pereira dos Santos (Eds.), Vol. 2039. CEUR-WS.org, 1--12. http://ceur-ws.org/Vol-2039/paper01.pdfGoogle Scholar
- Lael Parrott, René Lacroix, and Kevin M Wade. 2003. Design considerations for the implementation of multi-agent systems in the dairy industry. Computers and electronics in agriculture 38, 2 (2003), 79--98.Google Scholar
- Yogesh L Simmhan, Beth Plale, Dennis Gannon, and Suresh Marru. 2006. Performance evaluation of the karma provenance framework for scientific workflows. In International Provenance and Annotation Workshop. Springer, 222--236.Google ScholarDigital Library
- Evren Sirin, Bijan Parsia, Bernardo Cuenca Grau, Aditya Kalyanpur, and Yarden Katz. 2007. Pellet: A practical owl-dl reasoner. Journal of Web Semantics 5, 2 (2007), 51--53.Google ScholarDigital Library
- Pedro Henrique Dias Valle, Lina Garcés, and Elisa Yumi Nakagawa. 2021. Architectural strategies for interoperability of software-intensive systems: practitioners' perspective. In SAC '21, Chih-Cheng Hung, Jiman Hong, Alessio Bechini, and Eunjee Song (Eds.). ACM, 1399--1408. Google ScholarDigital Library
Index Terms
- Deriving experiments from E-SECO software ecosystem in the technology transfer process for the livestock domain
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