Process mining is a prominent discipline in business process management. It collects a variety of techniques for gathering information from event logs, each fulfilling a different mining purpose. Event logs are always necessary for assessing and validating mining techniques in relation to specific purposes. Unfortunately, event logs are hard to find and usually contain noise that can influence the validity of the results of a mining technique. In this paper, we propose a framework, named purple , for generating, through business model simulation, event logs tailored for different mining purposes, i.e., discovery, what-if analysis, and conformance checking. It supports the simulation of models specified in different languages, by projecting their execution onto a common behavioral model, i.e., a labeled transition system. We present eleven instantiations of the framework implemented in a software tool by-product of this paper. The framework is validated against reference log generators through experiments on the purposes presented in the paper.

The recent growth of blockchain has opened the use of technology for supporting the creation of newkinds of trustable systems. Model-driven engineering methodologies have been conceived to facilitate the automatic generation and deployment of software applications starting from the definition and refinement of abstract specification. BPMN choreography diagrams permit the representation of inter-organisational systems from a high-level perspective, just focusing on message exchange. However, the usage of such models in a blockchain-based setting has been limited to scenarios in which parties are involved in single interactions. This aspect becomes significantly relevant when considering complex applications, particularly those in the realm of the Internet of Things. In these cases, the multiplicity of parties and their actions is crucial and requires novel solutions. In this work, we propose a novel approach for modelling, refining, deploying, and executing a choreography on the blockchain, taking into account those scenarios in which the model includes multiple instances. In particular, the considered models are translated into smart contracts able to correctly manage multiplicity. To demonstrate the approach’s feasibility, we designed and presented a smart thermostat application, which is executed on the Polygon blockchain.

To react to unforeseen circumstances or amend abnormal situations in communication-centric systems, programmers are in charge of "undoing" the interactions which led to an undesired state. To assist this task, session-based languages can be endowed with reversibility mechanisms. In this paper we propose a language enriched with programming facilities to commit session interactions, to roll back the computation to a previous commit point, and to abort the session. Rollbacks in our language always bring the system to previous visited states and a rollback cannot bring the system back to a point prior to the last commit. Programmers are relieved from the burden of ensuring that a rollback never restores a checkpoint imposed by a session participant different from the rollback requester. Such undesired situations are prevented at design-time (statically) by relying on a decidable compliance check at the type level, implemented in MAUDE. We show that the language satisfies error-freedom and progress of a session.

Nowadays, user-generated content is pivotal for many companies: people trust other customers' opinions more than any brand advertisement. Brands are aware of this and try to promote and motivate their customers to create high-quality content. However, this way of operating is still at an early stage: there is a lack of fairness, as companies typically do not provide a validation system, or if they do, it is not based on a transparent solution, and often there is no reward for creating unique and high-quality content. In this paper, we focus on the problem of incentivizing the user's creation of content in the form of customer reviews in the online grocery industry. Specifically, we illustrate the solution to the problem devised in the Re-Taled project by relying on blockchain technology. We developed a decentralized ecosystem of consumers, influencers, and manufacturers, where content creators are rewarded for their contribution according to a framework that provides incentives in the form of both reputation and monetization. Blockchain technology is used to certify the content's authenticity and compensate content creators with a cryptographic token. We illustrate the technical choices of the solution together with its software architecture and implemented platform. In particular, we introduce the framework used to validate the trustworthiness of user-generated content and favor fairness and transparency within the platform.

To react to unforeseen circumstances or amend abnormal situations in communication-centric systems, programmers are in charge of “undoing” the interactions which led to an undesired state. To assist this task, session-based languages can be endowed with reversibility mechanisms. In this paper we propose a language enriched with programming facilities to commit session interactions, to roll back the computation to a previous commit point, and to abort the session. Rollbacks in our language always bring the system to previous visited states and a rollback cannot bring the system back to a point prior to the last commit. Programmers are relieved from the burden of ensuring that a rollback never restores a checkpoint imposed by a session participant different from the rollback requester. Such undesired situations are prevented at design-time (statically) by relying on a decidable compliance check at the type level, implemented in MAUDE. We show that the language satisfies error-freedom and progress of a session.

In modern business scenarios, more and more organisations have to deal with the critical requirements of trustworthiness and flexibility, when collaborating in multi-party business processes. This calls for new kinds of systems able to manage collaborative processes in untrusted and dynamic environments. Concerning the collaborative perspective, the Business Process Management discipline has provided effective and standardised solutions for a long time, now. Regarding the trustworthiness perspective, blockchain is advocated as one of the most prominent technologies to guarantee trust in a multi-party setting. However, while the immutability of blockchain provides transparent and secure proof of past business interactions, it hinders the flexibility of the business process execution, as the business logic regulating the process execution is immutably stored in the blockchain. On the other hand, flexibility is a property that is becoming crucial in such a setting due to the high dynamism of the business scenarios. In fact, it permits to modify a process at run-time to deal with internal or external changes. In this paper, we face this issue by proposing an architecture for the flexible blockchain-based execution of multi-party business processes. In our approach, business processes are modelled by BPMN choreography diagrams translated into code, whose execution state is then stored in the blockchain. Flexibility is achieved by decoupling the business process’s logic from its execution state, thus allowing run-time changes to the process execution without losing the fundamental properties of trust provided by the blockchain. To show the effectiveness of our approach, we provide a prototypical implementation, called FlexChain, and we use it on a case study from the healthcare application domain. The results obtained by the analysis of cost for the reported case study show the feasibility of the approach. In particular, major costs to sustain relate to one-time operations, such as the deployment and the run-time update of the model, while the most frequent actions are quite efficient.

Abstract Software development for robotics applications is still a major challenge that becomes even more complex when considering multi-robot systems (MRSs). Such distributed software has to perform multiple cooperating tasks in a well-coordinated manner to avoid unsatisfactory emerging behavior. This paper provides an approach for programming MRSs at a high abstraction level using the programming language X-Klaim. The computation and communication model of X-Klaim, based on multiple distributed tuple spaces, permits coordinating with the same abstractions and mechanisms both intra- and inter-robot interactions of an MRS. This allows developers to focus on MRS behavior, achieving readable, reusable, and maintainable code. The proposed approach can be used in practice by integrating X-Klaim and the popular robotics framework ROS. We demonstrate the feasibility and effectiveness of our approach by (i) showing how it scales when implementing two warehouse scenarios allowing us to reuse most of the code when passing from the simpler to the more enriched scenario and (ii) presenting the results of a few experiments showing that our code introduces a slightly greater but acceptable latency and consumes less memory than the traditional ROS implementation based on Python code.

As a disruptive technology, the blockchain is continuously finding novel application contexts, bringing new opportunities and radical changes. In this paper, we use blockchain as a communication infrastructure to support multi-party business processes. In particular, through smart contracts specifically generated by the mentioned business process, it is possible to derive a trustable infrastructure enabling the interaction among parties. Moreover, the emergence of different blockchain technologies, satisfying different characteristics, gives the possibility to support the same business process dealing with different non-functional needs. In this paper, we propose a novel engineering methodology supported by a practical framework called Multi-Chain. It permits to derive, using a model-driven strategy, a blockchain-based infrastructure, that can be deployed over a specific blockchain technology (e.g., Ethereum or Hyperledger Fabric). The objective is to permit the single definition and multiple deployments of the business process, to deliver the same functionalities, but satisfying different non-functional needs. In such a way, organisations willing to cooperate can select the multi-party business process and the blockchain technology they would like to use to satisfy their needs. Using Multi-Chain, they will be able to automatically derive from a Business Process Modelling Notation (BPMN) choreography diagram a blockchain infrastructure ready to be used. This overcomes the need to get acquainted with many details of the specific technology.

Access control systems are widely used means for the protection of computing systems. They are defined in terms of access control policies regulating the access to system resources. In this paper, we introduce a formally-defined, fully-implemented framework for specification, analysis and enforcement of attribute-based access control policies. The framework rests on FACPL, a language with a compact, yet expressive, syntax for specification of real-world access control policies and with a rigorously defined denotational semantics. The framework enables the automated verification of properties regarding both the authorisations enforced by single policies and the relationships among multiple policies. Effectiveness and performance of the analysis rely on a semantic-preserving representation of FACPL policies in terms of SMT formulae and on the use of efficient SMT solvers. Our analysis approach explicitly addresses some crucial aspects of policy evaluation, such as missing attributes, erroneous values and obligations, which are instead overlooked in other proposals. The framework is supported by Java-based tools, among which an Eclipse-based IDE offering a tailored development and analysis environment for FACPL policies and a Java library for policy enforcement. We illustrate the framework and its formal ingredients by means of an e-Health case study, while its effectiveness is assessed by means of performance stress tests and experiments on a well-established benchmark.

The BPMN 2.0 standard is nowadays largely used to model distributed informative systems in both academic and industrial contexts. The notation makes possible to represent these systems from different perspectives. A local perspective, using collaboration diagrams, to describe the internal behaviour of each component of the systems, and a global perspective, using choreography diagrams, where the interactions between system components are highlighted without exposing their internal structure. In this paper, we propose a formal approach for checking conformance of collaborations, representing possible system implementations, with respect to choreographies, representing global constraints concerning components' interactions. In particular, we provide a direct formal operational semantics for both BPMN collaboration and choreography diagrams, and we formalise the conformance concept by means of two relations defined on top of the semantics. To support the approach into practice we have developed the C 4 tool. Its main characteristic is to make the exploited formal methods transparent to systems designers, thus fostering a wider adoption of them in the development of distributed informative systems. We illustrate the benefits of our approach by means of a simple, yet realistic, example concerning a traveling scenario.

Business Process Modelling has acquired increasing relevance in software development. Available notations, such as BPMN, permit to describe activities of complex organisations. On the one hand, this shortens the communication gap between domain experts and IT specialists. On the other hand, this permits to clarify the characteristics of software systems introduced to provide automatic support for such activities. Nevertheless, the lack of formal semantics hinders the automatic verification of relevant properties. This paper presents a novel verification framework for BPMN 2.0, called BProVe. It is based on an operational semantics, implemented using MAUDE, devised to make the verification general and effective. A complete tool chain, based on the Eclipse modelling environment, allows for rigorous modelling and analysis of Business Processes. The approach has been validated using more than one thousand models available on a publicly accessible repository. Besides showing the performance of BProVe, this validation demonstrates its practical benefits in identifying correctness issues in real models.

This demo introduces BProVe, a tool supporting automated verification of Business Process models. BProVe analysis is based on a formal operational semantics defined for the BPMN 2.0 modelling language, and is provided as a freely accessible service that uses open standard formats as input data. Furthermore a plug-in for the Eclipse platform has been developed making available a tool chain supporting users in modelling and visualising, in a friendly manner, the results of the verification. Finally we have conducted a validation through more than one thousand models, showing the effectiveness of our verification tool in practice. (Demo video: https://youtu.be/iF5OM7vKtDA).

A promising solution for the management of services in clouds, as fostered by autonomic computing, is to resort to self-management. However, the obfuscation of underlying details of services in cloud computing, also due to privacy requirements, affects the effectiveness of autonomic managers. Data-driven approaches, in particular those relying on service clustering based on machine learning techniques, can assist the autonomic management and support decisions concerning, e.g., the scheduling and deployment of services. Unfortunately, applying such approaches is further complicated by the coexistence of different types of data within the information provided by the monitoring of cloud systems: both continuous (e.g., CPU load) and categorical (e.g., VM instance type) data are available. Current approaches deal with this problem in a heuristic fashion. In this paper, instead, we propose an approach that uses all types of data, and learns in a data-driven fashion the similarities and patterns among the services. More specifically, we design an unsupervised formulation of random forest to calculate service similarities and provide them as input to a clustering algorithm. For the sake of efficiency and to meet the dynamism requirement of autonomic clouds, our methodology consists of two steps: 1) off-line clustering and 2) on-line prediction. Using datasets from real-world clouds, we demonstrate the superiority of our solution with respect to others and validate the accuracy of the on-line prediction. Moreover, to show applicability of our approach, we devise a service scheduler that uses similarity among services, and evaluate its performance in a cloud test-bed using realistic data.

Managing and optimising cloud services is one of the main challenges faced by industry and academia. A possible solution is resorting to self-management, as fostered by autonomic computing. However, the abstraction layer provided by cloud computing obfuscates several details of the provided services, which, in turn, hinders the effectiveness of autonomic managers. Data-driven approaches, particularly those relying on service clustering based on machine learning techniques, can assist the autonomic management and support decisions concerning, for example, the scheduling and deployment of services. One aspect that complicates this approach is that the information provided by the monitoring contains both continuous (e.g. CPU load) and categorical (e.g. VM instance type) data. Current approaches treat this problem in a heuristic fashion. This paper, instead, proposes an approach, which uses all kinds of data and learns in a data-driven fashion the similarities and resource usage patterns among the services. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. Using datasets from real-world clouds, we demonstrate the superiority of our solution with respect to others and validate the accuracy of the on-line prediction. Moreover, to show the applicability of our approach, we devise a service scheduler that uses the notion of similarity among services and evaluate it in a cloud test-bed.

Causal-consistent reversibility is a natural way of undoing concurrent computations. We study causal-consistent reversibility in the context of μKlaim, a formal coordination language based on distributed tuple spaces. We consider both uncontrolled reversibility, suitable to study the basic properties of the reversibility mechanism, and controlled reversibility based on a rollback operator, more suitable for programming applications. The causality structure of the language, and thus the definition of its reversible semantics, differs from all the reversible languages in the literature because of its generative communication paradigm. In particular, the reversible behavior of μKlaim read primitive, reading a tuple without consuming it, cannot be matched using channel-based communication. We illustrate the reversible extensions of μKlaim on a simple, but realistic, application scenario.

Social Web Services (SWSs) constitute a novel paradigm of service-oriented computing, where Web services, just like humans, sign up in social networks that guarantee, e.g., better service discovery for users and faster replacement in case of service failures. In past work, composition of SWSs was mainly supported by specialised social networks of competitor services and cooperating ones. In this work, we continue this line of research, by proposing a novel SWSs composition procedure driven by the SWSs reputation. Making use of a well-known formal language and associated tools, we specify the composition steps and we prove that such reputation-driven approach assures better results in terms of the overall quality of service of the compositions, with respect to randomly selecting SWSs.

Mobile cloud computing (MCC) is an emerging paradigm to transparently provide support for demanding tasks on resource-constrained mobile devices by relying on the integration with remote cloud services. Research in this field is tackling the multiple conceptual and technical challenges (e.g., how and when to offload) that are hindering the full realization of MCC. The NAM framework is a general tool to describe networks of hardware and software autonomic entities, providing or consuming services or resources, that can be applied to MCC scenarios. In this paper, we focus on NAM's features related to the key aspects of MCC, in particular those concerning code mobility capabilities and autonomic offloading strategies. Our first contribution is the definition of a restricted set of mobility actions supporting MCC. The second contribution is a formal semantics for those actions, which allows us to better understand the behavior of MCC systems and paves the way for the application of formal reasoning techniques. As an outcome, we also derive a more precise formalization of the core NAM features, which may contribute to further development of that framework and the related middleware.

The autonomic computing paradigm has been proposed to cope with size, complexity, and dynamism of contemporary software-intensive systems. The challenge for language designers is to devise appropriate abstractions and linguistic primitives to deal with the large dimension of systems and with their need to adapt to the changes of the working environment and to the evolving requirements. We propose a set of programming abstractions that permit us to represent behaviors, knowledge, and aggregations according to specific policies and to support programming context-awareness, self-awareness, and adaptation. Based on these abstractions, we define SCEL (Software Component Ensemble Language), a kernel language whose solid semantic foundations lay also the basis for formal reasoning on autonomic systems behavior. To show expressiveness and effectiveness of SCEL;’s design, we present a Java implementation of the proposed abstractions and show how it can be exploited for programming a robotics scenario that is used as a running example for describing the features and potential of our approach.

We introduce a logical verification methodology for checking behavioral properties of service-oriented computing systems. Service properties are described by means of SocL, a branching-time temporal logic that we have specifically designed for expressing in an effective way distinctive aspects of services, such as, acceptance of a request, provision of a response, correlation among service requests and responses, etc. Our approach allows service properties to be expressed in such a way that they can be independent of service domains and specifications. We show an instantiation of our general methodology that uses the formal language COWS to conveniently specify services and the expressly developed software tool CMC to assist the user in the task of verifying SocL formulas over service specifications. We demonstrate the feasibility and effectiveness of our methodology by means of the specification and analysis of a case study in the automotive domain.