Luigi Portinale

Amedeo Avogadro University of Eastern Piedmont | UNIPMN · Computer Science Institute

This work proposes an approach based on dynamic Bayesian networks to support the cybersecurity analysis of network-based controllers in distributed energy plants. We built a system model that exploits real world context information from both information and operational technology environments in the energy infrastructure, and we use it to demonstra...

In this paper, we will explore the potential of knowledge discovery from bio-medical databases in health safeguard, by illustrating two specific case studies, where different knowledge extraction techniques have been exploited. Specifically, we will first report on how machine learning and data mining algorithms can address the problem of food adul...

In this paper, we will explore the potential of knowledge discovery from bio-medical databases in health safeguard, by illustrating two specific case studies, where different knowledge extraction techniques have been exploited. Specifically, we will first report on how machine learning and data mining algorithms can address the problem of food adul...

In Case-Based Reasoning, when the similarity assumption does not hold, the retrieval of a set of cases structurally similar to the query does not guarantee to get a reusable or revisable solution. Knowledge about the adaptability of solutions has to be exploited, in order to define a method for adaptation-guided retrieval. We propose a novel approa...

The paper reports the contribution of Piero Torasso to the field of Case-Based Reasoning (CBR), with particular attention to the role of CBR in multi-modal diagnostic problem solving. Starting from the idea that CBR could be adopted to focus model-based reasoning during diagnostic problem solving, Piero's work concentrated on all the different step...

We tackle the problem of authenticating high value Italian wines through machine learning classification. The problem is a seriuos one, since protection of high quality wines from forgeries is worth several million of Euros each year. In a previous work we have identified some base models (in particular classifiers based on Bayesian network (BNC),...

We exploit Decision Networks (DN) for the analysis of attack/defense scenarios in critical infrastructures. DN extend Bayesian Networks (BN) with decision and value nodes. DN inherit from BN the possibility to naturally address uncertainty at every level, making possible the modeling of situations that are not limited to Boolean combinations of eve...

This paper discusses a machine learning approach, aimed at the definition of methods for authenticity assessment of some of the highest valued Nebbiolo-based wines from Piedmont (Italy). This issue is one of the most relevant in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros....

This paper discusses an intelligent data analysis approach, based on machine learning techniques, and aimed at the definition of methods for chemical data analysis assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont (Italy). This is an important and very relevant issu...

We discuss the main features of Generalized Continuous Time Bayesian Networks (GCTBN) as a dependability formalism: we resort to two specific case studies adapted from the literature, and we discuss modeling choices, analysis results and advantages with respect to other formalisms. From the modeling point of view, GTCBN allow the introduction of ge...

This paper discusses the data mining approach followed in a project called TRAQUASwine, aimed at the definition of methods for data analytical assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont region in Italy. This is a big issue in the wine market, where commercial...

The problem of retrieving time series similar to a specified query pattern has been recently addressed within the case based reasoning (CBR) literature. Providing a flexible and efficient way of dealing with such an issue is of paramount importance in many domains (e.g., medical), where the evolution of specific parameters is collected in the form...

This paper describes how to exploit the modeling features and inference capabilities of dynamic Bayesian networks (DBN), in designing and implementing an innovative approach to fault detection, identification, and recovery (FDIR) for autonomous spacecrafts (e.g., a Mars rover). In particular, issues like partial observability, uncertain system evol...

We talk about dynamic reliability when the reliability parameters of the system, such as the failure rates, vary according to the current state of the system. In this article, several versions of a benchmark on dynamic reliability taken from the literature are examined. Each version deals with particular aspects such as state-dependent failure rate...

We exploit Decision Networks (DN) for the analysis of attack/defense scenarios. DN extend both the modeling and the analysis capabilities of formalisms based on Attack Trees, which are the main reference model in such a context. In particular, DN can naturally address uncertainty at every level, including the interaction level of attacks and counte...

Computer Interpretable Guidelines (CIG) are an emerging research area, to support medical decision making through evidence-based recommendations. New challenges in the data management field have to be faced, to integrate CIG management with a proper treatment of patient data, and of other forms of medical knowledge. The GINSENG (GuIdeliNe SEcoNd Ge...

We present a software tool for the analysis of Generalized Continuous Time Bayesian Networks (GCTBN) which extend CTBN introducing in addition to continuous time delayed variables, non delayed or “immediate” variables whose evolution is conditionally and immediately determined by the values of other variables in the model. The tool is based on the...

We propose a framework for the selection of failure countermeasures and repair actions, based on Decision Networks (DN). We show, through specific examples, that standard probabilistic inference on DN can be used to compute system reliability, component importance measures, as well as to select the best (in terms of Maximum Expected Utility) set of...

We propose to exploit Decision Networks (DN) for the analysis of attack/defense scenarios. We show that DN extend both the modeling and the analysis capabilities of formalisms based on Attack Trees, which are the main reference model in such a context. Uncertainty can be addressed at every system level and a decision-theoretic analysis of the risk...

A software tool for the analysis of Generalized Continuous Time Bayesian Networks (GCTBN) is presented. GCTGBN extend CTBN introducing in addition to continuous time-delayed variables, non-delayed or “immediate” variables. The tool is based on the conversion of a GCTBN model into a Generalized Stochastic Petri Net (GSPN), which is an actual mean to...

Although the notion of diagnostic problem has been extensively investigated in the context of static systems, in most practical applications the behavior of the modeled system is significantly variable during time. The goal of the paper is to propose a novel approach to the modeling of uncertainty about temporal evolutions of time-varying systems a...

Computer Interpretable Guidelines (CIG) are an emerging area of research, to support medical decision making through evidence-based recommendations. However, new challenges in the data management field have to be faced, to integrate CIG management with a proper treatment of patient data, and of other forms of medical knowledge (e.g., causal and beh...

Recent studies have focused on spacecraft autonomy. The traditional approach for FDIR (Fault Detection, Identification, Recovery) consists of the Run-Time observation of the operational status to detect faults; the initiation of recovery actions uses static Pre-Compiled tables. This approach is purely reactive, puts the spacecraft into a safe confi...

Dependability modeling and evaluation is aimed at investigating that a system performs its function correctly in time. A usual way to achieve a high reliability, is to design redundant systems that contain several replicas of the same subsystem or component. State space methods for dependability analysis may suffer of the state space explosion prob...

The problem of retrieving time series similar to a specified query pattern has been recently addressed within the Case Based Reasoning (CBR) literature. Providing a flexible and efficient way of dealing with such an issue would be of paramount importance in medical domains, where many patient parameters are often collected in the form of time serie...

This paper introduces a software prototype called ARPHA for on-board diagnosis, prognosis and recovery. The goal is to allow the design of an innovative on-board FDIR (Fault Detection, Identification and Recovery) process for autonomous systems, able to deal with uncertain system/environment interactions, uncertain dynamic system evolution, partial...

We address the problem of defining the behavior of an autonoumous FDIR (Fault Detection, Identification and Recovery) agent (e.g. a space rover), in presence of uncertainty and partial observability, we show how a Dynamic Decision Network (DDN) can be built through a fault analysis phase by producing an Extended Dynamic Fault Tree (EDFT). In this f...

Supporting decision making in domains in which the observed phenomenon dynamics have to be dealt with, can greatly benefit of retrieval of past cases, provided that proper representation and retrieval techniques are implemented. In particular, when the parameters of interest take the form of time series, dimensionality reduction and flexible retrie...

We present an extension to Continuous Time Bayesian Networks (CTBN) called Generalized CTBN (GCTBN). The formalism allows one to model continuous time delayed variables (with exponentially distributed transition rates), as well as non delayed or “immediate ” variables, which act as standard chance nodes in a Bayesian Network. The usefulness of this...

Interpreting time series of measurements and exploring a repository of cases with time series data looking for similarities, are non-trivial, but very important tasks. Classical methodological solutions proposed to deal with (some of) these goals, typically based on mathematical techniques, are characterized by strong limitations, such as unclear o...

In this paper, we present an approach to reliability modeling and analysis based on the automatic conversion of a particular reliability engineering model, the Dynamic Fault Tree (DFT), into Dynamic Bayesian Networks (DBN). The approach is implemented in a software tool called RADYBAN (Reliability Analysis with DYnamic BAyesian Networks). The aim i...

In recent years, the growing interest toward complex critical infrastructures and their interdependencies have solicited new efforts in the area of modeling and analysis of large interdependent systems. Cascading effects are a typical phenomenon of dependencies of components inside a system or among systems. The present paper deals with the modelin...

In the present paper, we propose an approach to intelligent retrieval and configuration of component-based products, starting from a set of possibly fuzzy user requirements provided at different levels of detail. A conceptual product model is introduced and its use during the configuration process is discussed. The proposed approach exploits a fuzz...

CBR systems designers and developers' research can benefit from the availability of existing platforms, able to provide software design and implementation assistance. The JColibri platform, realized and maintained by the University of Madrid, is one of the most well known among such tools. In this work, we describe a couple of extensions we have pr...

In the hemodialysis domain, we are implementing a case-based, closed-loop architecture aimed at configuring temporal abstractions (TA), which will be applied to time series data. The advantage of a case-based approach is the one of “quickly” obtaining a suitable TA parameter configuration, simply by looking at the most similar already configured ca...

Time series retrieval is a critical issue in all domains in which the observed phenomenon dynamics have to be dealt with. In this paper, we propose a novel, domain independent time series retrieval framework, based on Temporal Abstractions (TA). Our framework allows for multi-level abstractions, according to two dimensions, namely a taxonomy of (tr...

The aim of this paper is to formally introduce a fuzzy logic based notion of acceptance and similarity among case features, for case matching and retrieval. In particular, we present an approach where local acceptance relative to a feature can be expressed through fuzzy distributions on its domain, abstracting the actual values to linguistic terms....

In this paper, we present Radyban (Reliability Analysis with DYnamic BAyesian Networks), a software tool which allows to analyze a dynamic fault tree relying on its conversion into a dynamic Bayesian network. The tool implements a modular algorithm for automatically translating a dynamic fault tree into the corresponding dynamic Bayesian network an...

In general, cases capture knowledge and concrete experiences of specific situations. By exploiting case-based knowledge for characterizing a subgroup pattern, additional information about the subgroup objects can be provided. This paper proposes a case-based ...

IntroductionDynamic fault treesDynamic Bayesian networksA case study: The Hypothetical Sprinkler SystemConclusions

Over the last decade, Bayesian networks (BNs) have become a popular tool for modelling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. In this paper we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applicatio...

In this paper, we present Radyban (Relia- bility Analysis with DYnamic BAyesian Net- works), a software tool which allows to ana- lyze systems modeled by means of Dynamic Fault Trees (DFT), by relying on automatic conversion into Dynamic Bayesian Networks (DBN). The tools aims at providing a famil- iar interface to reliability engineers, by al- low...

End Stage Renal Disease is a severe chronic condition that corresponds to the final stage of kidney failure. Hemodialysis (HD) is the most widely used treatment method for ESRD. The HD treatment is costly and demanding from an organizational viewpoint, requiring day hospital beds, specialized nurses and periodical visits and exams of out-patients....

Over the last decade, Bayesian networks (BNs) have become a popular tool for modeling many kinds of statistical problems. In this chapter we will discuss the properties of the modeling framework that make BNs particularly well suited for reliability applications. This discussion is closely linked to the analysis of a real-world example.

Over the last decade, Bayesian networks (BNs) have become a popular tool for modeling many kinds of statistical problems. In this chapter we will discuss the properties of the modeling framework that make BNs particularly well suited for reliability applications. This discussion is closely linked to the analysis of a real-world example.

In this work we propose a case-based architecture tackling the problem of configuring and processing temporal abstractions (trends and qualitative states) produced from raw time series data. The parameter configuration is a critical problem in many temporal abstraction processes; in several application domains (especially in medical ones), contextu...

Time-varying information embedded in cases has often been neglected and its role oversimplified in case-based reasoning systems. In several real-world problems, and in particular in medical applications, a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels:...

In the present paper, we describe an application of case-based retrieval to the domain of end stage renal failure patients, treated with hemodialysis. Defining a dialysis session as a case, retrieval of past similar cases has to operate both on static and on dynamic features, since most of the monitoring variables of a dialysis session are time ser...

This paper presents a software tool allowing the automatic analysis of a dynamic fault tree (DFT) exploiting its conversion to a dynamic Bayesian network (DBN). First, the architecture of the tool is described, together with the rules implemented in the tool, to convert dynamic gates in DBNs. Then, the tool is tested on a case of system: its DFT mo...

The unreliability evaluation of a system including dependencies involving the state of components or the failure events, can be performed by modelling the system as a dynamic fault tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a dynamic Bayesian network (DBN...

The present work is aimed at exploring the capabilities of the Bayesian Networks (BN) formalism in the modeling and analysis of dependable systems. We compare BN with one of the most popular techniques for the dependability analysis of large, safety-critical systems, namely Fault Tree Analysis (FTA). The work shows that any Fault Tree (FT) can be d...

The temporal dimension of the knowledge embedded in cases has often been neglected or oversimplified in Case Based Reasoning systems. However, in several real world problems a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, if som...

Integrating different reasoning modes in the construction of an intelligent system is one of the most interesting and challenging aspects of modern AI. Exploiting the complementarity and the synergy of different approaches is one of the main motivations that led several researchers to investigate the possibilities of building multi-modal reasoning...

In this paper, we present a case-based retrieval system called Rhene ( Retrieval of HEmodialysis in NEphrological disorders) working in the domain of patients affected by nephropatologies and treated with hemodialysis. Defining a dialysis session as a case, retrieval of past similar cases has to operate both on static and on dynamic (time-dependent...

This paper shows how heterogeneous stochastic modelling techniques of increasing modelling power can be applied to assess the safety of a digital control system. First, a Fault-Tree (FT) has been built to model the system, assuming two-state components and independent failures. Then, the FT is automatically converted into a Bayesian Network, allowi...

In order to cope efficiently with the dependability analysis of redundant systems with replicated units, a new, more compact fault-tree formalism, called Parametric Fault Tree (PFT), is defined. In a PFT formalism, replicated units are folded and indexed so that only one representative of the similar replicas is included in the model. From the PFT,...

The paper describes a probabilistic approach based on methods of increasing modelling power and different analytical tractability, to analyse safety of turbine digital control system. First, a Fault-Tree (FT) has been built to model the system, assuming independent failures and binary states of its components. To include multi-states and sequential...

Providing a flexible and efficient way of consulting a catalog in e-commerce applications is of primary importance in order to guarantee the customer with a set of products actually related to his/her interests. Most electronic catalogs exploit standard database techniques both for storage and retrieval of product information. However, a naive appl...

The use of database technologies for implementing case-based reasoning techniques is attracting a lot of attention for several reasons. First, the possibility of using standard DBMS for storing and representing cases significantly reduces the effort needed to develop a CBR system; in fact, data of interest are usually already stored into relational...

terns inside available data, by using specific statistical techniques [2]. Even if they are around from almost 50 years, pattern recognition approaches have recently gained a new popularity, due to emerging applications which are not only challanging, but also computationally expensive and very demanding like data mining (identifying a pattern or a...

In this paper we present the results of the MIE/GMDS-2000 Workshop 'Case-Based Reasoning for Medical Knowledge-based Systems'. While in many domains Cased-Based Reasoning (CBR) has become a successful technique for knowledge-based systems, in the medical field attempts to apply the complete CBR cycle are rather exceptional. Some systems have recent...

Backward reachability on Petri net models has been proposed since the beginning of the development of net theory without giving it a suitable motivation. For this reason, reachability analysis has been successively developed essentially by taking into account forward reachability. In this paper backward reachability analysis is motivated by showing...

The present paper outlines the PVM implementation of a particular approach to model-based diagnosis which uses a Petri net model of the system to be diagnosed. Parallel backward reachability analysis on the state space of the net is used to explain the misbehavior of the modeled system. The analysis algorithm is based on the automatic identificatio...

Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty, but despite their formal grounds are strictly based on the notion of conditional dependence, not much attention has been paid so far to their use in dependability analysis. The aim of this paper is to propose BN as a suitable tool for dependability analysis,...

The definition of suitable case-base maintenance policies is widely recognized as a major key to success for case-based systems; underestimating this issue may lead to systems that either do not fulfill their role of knowledge management and preservation or that do not perform adequately under performance dimensions, namely, computation time and co...

Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks but they have received little attention in the area of dependability. The present paper is aimed at exploring the capabilities of the BN formalism in the analysis of dependable systems. To...

The case-study presented in this paper is aimed at assessing the dependability of a Programmable Logic Controller (PLC) devoted to safety functions. This case study has been brought to our attention by a national environmental agency and has been partially abstracted and anonymized to protect proprietary information. The PLC consists of a triplicat...

The use of database technologies for implementing CBR techniques is attracting a lot of attention for several reasons. First, the possibility of using standard DBMS for storing and representing cases significantly reduces the effort needed to develop a CBR system; in fact, data of interest are usually already stored into relational databases and us...

Computer based systems, which are devoted to control critical functions, may incur in safety and dependability problems. In the safety area a new standard is currently emerging, IEC 61508, which is intended to provide a unified framework which may deserve as guideline for the analysis of safety related systems. The present paper deals with the safe...

We present a knowledge management and decision support methodology for insulin dependent diabetes mellitus (IDDM) patients care. Such methodology exploits the integration of case based reasoning (CBR) and rule based reasoning (RBR), with the aim of helping physicians during therapy planning, by overcoming the intrinsic limitations shown by the inde...