BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture
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Today we live in a world of digital objects and digital technology; industry and humanities as well as technologies are truly in the midst of a digital environment driven by ICT and cyber informatics. A digital ecosystem can be defined as a digital environment populated by interacting and competing digital species. Digital species have autonomous, proactive and adaptive behaviors, regulated by peer-to-peer interactions without central control point. An interconnecting architecture with few highly connected nodes (hubs) and many low connected nodes has a scale- free architecture. A new bio-inspired analysis methodology (BIAM) environment, an investigation strategy for information flow, fault and error tolerance detection in digital ecosystems based on a scale-free architecture is presented in this paper. In order to extract the information about modules and digital species role, the analysis methodology, inspired by metabolic network working, implements a set of three interacting techniques, i.e., topological analysis, flux balance analysis and extreme pathway analysis. Highly connected nodes, intermodule connectors and ultra-peripheral nodes can be identified by evaluating their impact on digital ecosystems behavior and addressing their strengthen, fault tolerance and protection countermeasures. Two real case studies of ecosystems have been analyzed in order to test the functionalities of the proposed (BIAM) environment and the goodness of this approach.
KeywordsDigital ecosystems Metabolic networks Scale-free architecture DE architectural analysis
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Briscoe G (2010) Complex adaptive digital ecosystems. In: proceedings of the international conference on management of emergent digital ecosystems, ACM New York, NY, USA. doi: 10.1145/1936254.1936262
- Briscoe G, Sadedin S, Paperin G (2007) Biology of applied digital ecosystems. In IEEE 1st international conference on digital ecosystems and technologies. http://arxiv.org/abs/0712.4153v2
- Conti V, Lanza B, Vitabile S, Sorbello F (2009) Neural networks and metabolic networks: fault tolerance and robustness features. Front Artif Intell Appl 204: Neural Nets WIRN09, IOS Press Editor, pp 39–48, ISSN 0922-6389, ISBN 978-1-60750-072-8. doi: 10.3233/978-1-60750-072-8-39
- Conti V, Lanza B, Vitabile S, Sorbello F (2010) BioAnalysis: a framework for structural and functional robustness analysis of metabolic networks. In: 4th international IEEE conference on complex, intelligent and software intensive systems (CISIS 2010), pp 138–145. doi: 10.1109/CISIS.2010.136
- Conti V, Vitabile S, Militello C, Lanza B, Sorbello F (2011) An embedded processor for metabolic networks optimization. In: 5th international conference on complex, intelligent and software intensive systems (CISIS 2011), pp 77–84, ISBN: 978-0-7695-4373-4. Korean Bible University (KBU), Seoul, Korea, June 30th–July 2ndGoogle Scholar
- Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis, current Opinion in Biotechnology. Elsevier, Hoboken, pp 491–496Google Scholar
- Lopardo GA, Rateb FN (2008) Chaos and budworm dynamics of agent interactions: a biologically-inspired approach to digital ecosystems. In: MICAI 2008, advances in artificial intelligence. Lecture notes in computer science, vol 5317, pp 889–899Google Scholar
- Lowe E (2004) Defining eco-industrial parks: The global context and China. Report prepared for the Policy Research Center for Environment and Economy, State Environmental Protection Administration, ChinaGoogle Scholar
- Provost A and Bastin G (2006) Metabolic flux analysis: an approach for solving non-stationary undetermined systems. In: Proceedings 5th MATHMOD, Vienna, Febbraio, I Troch, F. Breitenecker, pp 5/1-5/10Google Scholar
- Razavi AR, Moschoyiannis SK, Krause PJ (2008) A scale-free business network for digital ecosystems. In: proceedings of the 2nd IEEE international conference on digital ecosystems and technologies, pp 241–246. ISBN: 978-1-4244-1489-5Google Scholar
- Vitabile S, Conti V, Lanza B, Cusumano D, Sorbello F (2011) metabolic networks robustness: theory, simulations and results. J Interconnect Netw (JOIN) 12(3):221–240, World Scientific Publishing Company, ISSN: 0219-2659 (print), pp 1793–6713 (online). doi: 10.1142/S0219265911002964
- Vitabile S, Conti V, Lanza B, Cusumano D, Sorbello F (2011) Topological information, flux balance analysis, and extreme pathways extraction for metabolic networks behaviour investigation. Front Artif Intell Appl IOS Press Editor, vol 234: Neural Nets WIRN11, pp 66–73, ISSN 0922-6389, ISBN 978-1-60750-971-4. doi: 10.3233/978-1-60750-972-1-66