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Abstract. In the last fifteen years, several research efforts have been directed towards the representation and the analysis of metabolic pathways by using Petri nets. The goal of this paper is twofold. First, we discuss how the knowledge about metabolic pathways can be represented with Petri nets. We point out the main problems that arise in the construction of a Petri net model of a metabolic pathway and we outline some solutions proposed in the literature. Second, we present a comprehensive review of recent research on this topic, in order to assess the maturity of the field and the availability of a methodology for modelling a metabolic pathway by a corresponding Petri net.

When tackling the construction of a software system, at the software architecture design level there are two main issues related to the system performance. First, the designer may need to choose among several alternative software architectures for the system, with the choice being driven especially by performance considerations. Second, for a specific software architecture of the system, the designer may want to understand whether its performance can be improved and, if so, it would be desirable for the designer to have some diagnostic information that guide the modification of the software architecture itself. In this paper we show how these two issues can be addressed in practice by employing a methodology relying on the combined use of AEmilia --- an architectural description language based on stochastic process algebra --- and queueing networks --- structured performance models equipped with fast solution algorithms --- which allows for a quick prediction, improvement, and comparison of the performance of different software architectures for a given system. The methodology is illustrated through a case study in which a sequential architecture, a pipeline architecture, and a concurrent architecture for a compiler system are compared on the basis of typical average performance indices

We propose an integrated approach to the functional and performance analysis of Software Architectures (SAs) based on Stochastic Process Algebras (SPAs) and Queueing Networks (QNs), in order to combine their main advantages: formal techniques for the verification of functional properties of systems for SPAs, and efficient performance analysis for QNs. We first introduce AEmilia, a SPA based architectural description language for the compositional, graphical and hierarchical modeling of SAs, which is equipped with suitable checks for the detection of architectural mismatches. Then we present a systematic approach to derive QN models from AEmilia specifications. This is based on the identification of three different classes of QN basic elements -arrival processes, buffers, and service processes -and on syntactic restrictions to be imposed to AEmilia specifications, so that each architectural component directly falls into one of the three classes. Although performance analysis could be carried out directly on the Markov chain (MC) underlying an AEmilia specification, having a QN model allows performance indices to be evaluated possibly by exact product form solutions or by well known approximate methods. Furthermore, unlike the underlying MC, the high level of abstraction of the QN model should ease the interpretation of the performance results at the architectural description level.

In this paper we explore a unique, high-value spatio-temporal dataset that results from the fusion of three data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed), the corresponding fish catch reports (i.e., the quantity and type of fish caught), and relevant environmental data. The result of that fusion is a set of semantic trajectories describing the fishing activities in Northern Adriatic Sea over two years. We present early results from an exploratory analysis of these semantic trajectories, as well as from initial predictive modeling using Machine Learning. Our goal is to predict the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation useful for fisheries management. Our predictive results are preliminary in both the temporal data horizon that we are able to explore and in the limited set of learning techniques that are employed on this task. We discuss several approaches that we plan to apply in the near future to learn from such data, evidence, and knowledge that will be useful for fisheries management. It is likely that other centers of intense fishing activities are in possession of similar data and could use the methods similar to the ones proposed here in their local context.