This paper presents the work under development to customize a Decision Support System, called CIPCast, conceived in the framework of several international research projects, aiming to support decision makers in the event of natural events. CIPCast can acquire several types of data such as seismic events, weather forecasts, Points of Interest (POI), Critical Infrastructure (CI) components and perform a risk analysis of the vulnerable assets (e.g., buildings, electric substations, water towers) by considering the vulnerability of each asset to a specific natural event and applying a set of damage metrics taken from the literature. In order to study the effects of restoration actions when a perturbation affects one or multiple systems, a novel CIPCast feature relates the assessment of urban resilience in terms of social, economic and operational indicators in a real or simulated scenario. Thus, CIPCast DSS can support decision-makers to plan proper countermeasures to reduce the overall risk of degradation of services.

Increasing industrial resilience is a big challenge for manufacturing enterprises that are continuously facing severe accidents causing injuries, casualties, and economic losses. Assessing industrial resilience requires the analysis of production processes in order to find possible safety flaws. Sociotechnical process management suffers often from misalignments of process descriptions according to formal organization documents or manager views (Work-As-Imagined) and actual work practices as performed by sharp-end operators (Work-As-Done). Furthermore, existing modelling approaches leveraging on techniques such as process mining from digital traces cannot be used to solve such misalignments as these traces are often hardly available. In this context, we propose a computational creativity approach for a semantics-driven transition from Work-As-Imagined to Work-As-Done process models based on the functional resonance analysis method (FRAM). In particular, through formalized semantics, it will be possible to use automatic reasoning for identification of criticalities and prioritization of normal work analyses. To this aim, we introduce some examples of rule patterns, inspired by typical data quality issues, which can be automatically applied to guide such a transition. An explorative case study on chemical cleaning for industrial process is presented to clarify the proposed approach.

The accurate determination of the azimuth of a given direction, e.g., the true (geographic) North, is of fundamental importance in many fields. Just as few examples, it guides buildings construction in civil engineering, supports environmental and cartographic surveys, allows the correct positioning and stability control of concentrating solar power plants, as well as of airport installations, provides the geographic North reference for geomagnetic measurements, contributes to the interpretation of the orientation choices of ancient constructions in archeoastronomy, can be the primary benchmark to calibrate other compasses or gyroscopes. When aiming at reaching azimuth measurements with accuracies well below 1°, magnetic compasses are unreliable: firstly, they indicate the magnetic North rather than the geographic one; secondly, they are heavily influenced by possible surrounding ferromagnetic items.

Risk assessment of urban areas aims at limiting the impact of harmful events by increasing awareness of their possible consequences.Qualitativerisk assessment allows to figure out possible risk situations and to prioritize them, whereasquantitativerisk assessment is devoted to measuring risks from data, in order to improve preparedness in case of crisis situations. We propose an automatic approach to comprehensive risk assessment. This leverages on a semantic and spatiotemporal representation of knowledge of the urban area and relies on a software system including: a knowledge base; two components for quantitative and qualitative risk assessments, respectively; and a WebGIS interface. The knowledge base consists of the TERMINUS domain ontology, to represent urban knowledge, and of a geo-referenced database, including geographical, environmental and urban data as well as temporal data related to the levels of operation of city services. CIPcast DSS is the component devoted to quantitative risk assessment, and WS-CREAM is the component supporting qualitative risk assessment based on computational creativity techniques. Two case studies concerning the city of Rome (Italy) show how this approach can be used in a real scenario for crisis preparedness. Finally, we discuss issues related to plausibility of risks and objectivity of their assessment.

Safety of enterprises, as well as socio-technical systems in general, is due to several factors and can be considered an emergent property, depending on non-linear and symbiotic interactions between humans and technical systems taking places in collaborative business processes. The safety-II perspective promotes assessing and gathering meaningful knowledge about normal work, and its effect on safety and productivity. We aim at providing a support to this activity, proposing a three-phases framework for the definition of indicators on enterprise safety performance. This framework includes collection of knowledge on work-asimagined (WAI) business processes, ontology-based modelling of work-as-done (WAD) business processes and some guidelines to define indicators taking into account the actual needs and implicit knowledge of sharp-end operators by means of the Functional Resonance Analysis Method (FRAM).

Risk assessment aims at improving prevention and preparedness phases of the crisis management lifecycle. Qualitative risk assessment of a system is important for risks identification and analysis by the various stakeholders and often requires multi-disciplinary knowledge. We present an automatic approach to qualitative risk assessment in metropolitan areas using semantic techniques. In particular, users are provided with a computational support to identify and prioritize by relevance risks of city services, through generation of semantic descriptions of risk situations. This approach is enabled by a software system consisting of: TERMINUS, a domain ontology representing city knowledge; WS-CREAM, a web service implementing risk identification and ranking functions; and CIPCast, a GIS-based Decision Support System with functions of risk forecast due to natural hazards. Finally we present the results of a preliminary validation of the generated risks concerning some points of interest in two different areas of the city of Rome.

Business innovation is a process that requires creativity, and benefits from extensive collaboration. Currently, computational support in creativity processes is low, but modern techniques would allow these processes to be sped up. In this context, we provide such a computational support with software for business innovation design that uses computational creativity techniques. Furthermore, the software enables a gamified process to increase user engagement and collaboration, which mimics evolutionary methods, relying on a voting mechanism. The software includes a business innovation ontology representing the domain knowledge that is used to generate and select a set of diverse preliminary representations of business ideas. Indeed, the most promising for novelty and potential impact are identified to ignite a business innovation game where team members collaborate to elaborate new innovation ideas based on those inputs until convergence to a shortlist of business model proposals. The main features of the approach are illustrated by means of a running example concerning innovative services for smart cities.

We present a method to create semantic representations of cascading risks of interoperable socio-technical systems. This is based on a structured domain ontology representing socio-technical systems, their interdependencies, environmental and anthropic hazards, and the related threats. The ontology is accompanied by a software application, i.e., the CREAtivity Machine that generates cascades of risks by means of semantic and computational creativity techniques. The presented running prototype refers to risk assessment of critical infrastructures; however, the same method can be applied to risks concerning other system types like businesses, ecosystems, and financial networks.

Modelling risks related to critical infrastructures requires integrated and multi disciplinary competencies of experts, stakeholders, and users who are often located in different places and have limited time to be involved in a strenuous knowledge elicitation project. In this context we propose a gamified and participatory modelling approach to boost engagement of people involved in risk assessment related to critical infrastructures. The approach is supported by a risk management system based on the ICE-CREAM framework, including the CREAM (CREAtivity Machine) software for computational creativity and the ICE (Innovation through Collaborative Environment) mobile app developed for iOS. Insights on risk assessment of water systems are also discussed.

Managing crisis and emergency requires a deep knowledge of the related scenario. Simulation and analysis tools are considered as a promising mean to reach such understanding. Precondition to these types of tools is the availability of a graphical modeling language allowing domain experts to build formally grounded models. To reach this goal, in this paper, we propose the CEML language and the related meta-model to describe structural aspects of crisis and emergency scenarios. The meta-model consists of a set of modeling constructs, a set of domain relationships, and a set of modeling rules. Then we introduce a set of methodological guidelines to reach an executable code, consisting of a system architecture and the mapping rules to transform the CEML modeling constructs into others typical of discrete event simulation. Finally, we propose a preliminary set of collaboration design patterns to model interaction and communication exchange arising among emergency services providers and citizens to solve the crisis. An emergency scenario example demonstrates the applicability of the presented approach.

Crisis management is a critical task requiring a deep knowledge of the related scenario where usually interoperability and collaboration among different services and actors take place. To support crisis management a promising approach is based on simulation and analysis. Precondition to them is the possibility to model both structural and behavioural aspects of the addressed domain. In a previous paper we presented the CEML domain specific language to model structural aspects of a crisis scenario. Here we focus on behavioural aspects and we present the CEML behavioural modeling framework to model them. This framework consists of the CEML language and an incremental method to specify behaviour based on the stimulus-response model, the event-condition-action rules, and the Reaction RuleML language.

Systems of technological networks (so-called Critical Infrastructures) might undergo strong perturbations leading to the reduction of their functionalities (crisis scenarios). Under perturbed conditions, infrastructures deliver their specific services in a range from a partial reduction up to a complete loss. Systems of complex technological networks (electrical, telecommunications, roads and railways, oil, gas and water pipelines etc.) are known to display large interdependences (a fault on one system spreads over the others); for this reason, it is relevant to devise simulators and decision support systems able to capture the complexity of such a "system of systems". The paper presents parts of the outcomes of a larger scientific project aiming at realizing a Decision Support System to predict the occurrence of undesired events and their consequences. In particular, we present a conceptual modeling approach consisting of a declarative language and a software implementation for the description and simulation of elements of Crisis Scenarios. The software application is conceived for risk managers, i.e. operators with a deep knowledge of critical infrastructure scenarios, but not necessarily with high-level IT or mathematical skill.

Managing crisis and emergency requires a deep knowledge of the related scenario. Simulation and analysis tools are considered as a promising mean to reach such understanding. Precondition to these types of tools is the availability of a graphical modeling language allowing domain experts to build formally grounded models. To reach this goal, in this paper, we propose the CEML language and the related meta-model to describe structural aspects of crisis and emergency scenarios. The meta-model consists of a set of modeling constructs, a set of domain relationships, and a set of modeling rules. Finally, we propose a preliminary set of collaboration design patterns to model interaction and communication exchange arising among emergency services providers and citizens to solve the crisis.

Public life, economy and society as a whole depend to a very large extend on the proper functioning of critical infrastructures (CIs) like energy supply or telecommunication. The extensive use of information and communication technologies (ICT) has pervaded the critical infrastructures, rendering them more intelligent but even increasingly interconnected, complex, interdependent, and therefore more vulnerable. In this paper a new technology (MIT - Middleware Improvement Technology) is proposed: it is based on a collection of software components aiming at enhancing the dependability, the survivability and the resilience of LCCIs (Large Complex Critical Infrastructures) by mitigating dependency and interdependency effects. It should prevent and limit cascading effects and/or support automated (if possible) recovery and service continuity in critical situations. The research activities and results described in the paper have been developed inside EU/FP6 Integrated Project IRRIIS - Integrated Risk Reduction of Information-based Infrastructure Systems.

When the complexity of a system increases, the number of possible faults and anomalous working conditions becomes very high; on the contrary, the number of 'normal' behaviours is generally low and often well determined by rules and constraints defined by the characteristics of the furnished services. In this paper, after a general overview of the SAFEGUARD1 system, a more detailed description of the agents, dedicated to early detection of anomalies and failures inside a Supervisory and Control and Data Acquisition (SCADA) system of an electricity transmission network is given.The paper also describes how it is possible to correlate the detected novelty events and to decide the right recovery policies avoiding inappropriate reactions caused by false alarms.A test benchmark of the novelty detection agents will be executed inside a simulated SCADA electricity transmission system and the layout of the utilised testing environment is described in the paper.

The paper explores the possibility of using interacting agents for modelling and discrete event simulation as a tool to approach interdependencies analysis and evaluation for critical infrastructures. A discrete event simulation system was developed, using agent-oriented programming, considering the following limited sets of critical infrastructures: a great hospital infrastructure, a railway transportation infrastructure and other public transportation infrastructures.Faults inside the electricity distribution system are simulated, producing electrical power outages whose duration could be variable with respect to time and space, and generating consequences inside the transportation infrastructures.The hospital infrastructure users, such as different types of physicians, nurses, subsidiary personnel, students and patients are also modelled using agent oriented architectures. The objective of the simulations is to study and analyse the interdependencies of the considered infrastructures.Many typologies of test scenarios are also executed and the severity of the generated consequences are analysed in the paper.

This paper refers to research activities related to SAFEGUARD project (IST Project Number: IST-2001-32685). The aims of the project is to examine LCCI’s in terms of nature of different facets in each infrastructure: organizational, computational (cyber) and physical layers. Critical inter-dependencies among layers can thus be analyzed. Possible impact of bad events, early classified in attack scenarios with and without SAFEGUARD, will be coped with countermeasures to maintain at acceptable level system’s operability. SAFEGUARD, an agent-based middleware, is conceived to operate embedded inside of the cyber-layers, the more sensitive part to malicious attacks and anomalies, and is designed to enhance dependability and survivability of a LCCI. Self-healing mechanism of SAFEGUARD agents will start with the trouble diagnosis and classification using Hybrid Intrusion Detection techniques (software instrumentation, novelty detection, etc.). Once the problem has been diagnosed, a number of techniques will be used to solve and repair the fault (i.e.: adaptive middleware technology, backup, hot standby and so on). More self-healing mechanisms will have to be combined and coordinated to with an attempt to deal with the source of the problem.

The paper presents italian results of the CEC Environment Project MUSTER (Multi-Users System for Training and Evaluating Environmental Emergency Response). A novel conceptualization framework for the computer aided training of cooperating and coordinated emergency managers is discussed. Two basic functions of the computer support system have been defined: a simulation of an emergency environment and the activity of emergency organization., a support to the instructor functions. The trainee model is based on the functional architecture of an abstract intelligent agent. An application of the concept of intelligent agent enables to represent the cooperation between emergency managers in multi-agent system, and to analyse causes of their errorneuss decisions. The architecture of the prototype system is described. A concrete example of its applications is referred to the benchmark represented the emergency state and its evolution at the Genoa Oil Port, caused by an explosion and firing of an oil tanker.