Indoor environments are increasingly exposed to natural hazards, posing significant challenges for ensuring both preparedness and effective emergency response. However, existing monitoring solutions typically address either routine environmental control or post-event analysis, lacking mechanisms for dynamic reconfiguration across operational conditions. This paper presents a distributed IoT system for indoor disaster preparedness and response, grounded in an operational framework that provides a unified rationale for analyzing and managing indoor living environments. The proposed system integrates environmental and structural sensing, edge-level event detection, and cloud coordination services to enable continuous monitoring under normal conditions and coordinated reconfiguration during emergencies. A dual-regime operational model distinguishes preparedness and response phases, enabling state transitions triggered by critical events. The system is evaluated through a six-month real-world deployment in an educational environment, which provides qualitative and quantitative evidence of its behavior under operational conditions, and through controlled laboratory tests to assess its dual-regime behavior. The results show that the proposed system supports operational awareness and the reconfiguration of coordinated behavior in indoor environments.

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.

Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

Solidity is the predominant programming language for blockchain-based smart contracts, and its characteristics pose significant challenges for code analysis and maintenance. Traditional software analysis approaches, while effective for conventional programming languages, often fail to address Solidity-specific features such as gas optimization and security constraints. This paper introduces micro-patterns - recurring, small-scale design structures that capture key behavioral and structural peculiarities specific to a language - for Solidity language and demonstrates their value in understanding smart contract development practices. We identified 18 distinct micro-patterns organized in five categories (Security, Functional, Optimization, Interaction, and Feedback), detailing their characteristics to enable automated detection. To validate this proposal, we analyzed a dataset of 23258 smart contracts from five popular blockchains (Ethereum, Polygon, Arbitrum, Fantom and Optimism). Our analysis reveals widespread adoption of micro-patterns, with 99% of contracts implementing at least one pattern and an average of 2.76 patterns per contract. The Storage Saver pattern showed the highest adoption (84.62% mean coverage), while security patterns demonstrated platform-specific adoption rates. Statistical analysis revealed significant platform-specific differences in adoption, particularly in Borrower, Implementer, and Storage Saver patterns.

Blockchain technology has been widely adopted to enhance the security and the decentralisation of smart applications in large-scale pervasive systems. In such a context, data extraction is crucial as it provides a better understanding of the system’s behaviours. However, several challenges arise in automatically extracting data, due to the variety of data sources, such as transactions, events, contract storage, and the complexity of the blockchain structure. In particular, retrieving smart contract state changes remains unexplored despite its potential usage for discovering unexpected behaviour. For such reasons, in this work, we propose a novel methodology and a supporting application for extracting smart contract state changes and other execution-related data. The obtained data is then decoded and offered in a standard format to be easily reused. The methodology provides additional functionalities such as transaction filtering and capabilities for querying over extracted data. The effectiveness and the performance of the methodology were evaluated on three real-world projects from different EVM-based blockchains.

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.

The adoption of smart contracts in decentralized blockchain-based applications enables reliable and certified audits. These audits allow the extraction of valuable information from blockchains, which can be used to reconstruct the execution of the application and facilitate advanced analyses. One of the most commonly used techniques in this context is process mining, which leverages event logs to trace and accurately represent the process execution of applications. However, extracting execution data from blockchains poses significant challenges, and the current methodologies developed have some limitations. Most approaches are tailored to specific use cases, requiring that analysis techniques are defined during the smart contract’s development. Other techniques are applied a posteriori, relying on blockchain events that often lack a standardized format. This absence of standardization requires complex processing steps to correlate logs with the executed actions and such approaches are not universally applicable to all smart contracts on the blockchain , further limiting their scope. Lastly, none of the existing techniques can extract information from event logs embedded in internal transactions of smart contracts. To address these limitations, we propose EveLog an application-agnostic methodology that can be applied to any EVM-compatible application without predefined constraints. Its primary goal is to extract information from smart contracts, capturing both public and internal transactions, and organizing the results into a structured XES event log. The EveLog methodology consists of five key steps: (i) extraction of data from smart contract transactions, (ii) decoding raw data, (iii) selection of sorting criteria, (iv) construction of traces, and (v) generation of the XES event log. EveLog has been implemented in a client–server application and tested on existing solutions, specifically the CryptoKitties application, a blockchain-based game on the Ethereum blockchain. The study was conducted using 12,996 blocks, including over 8000 real transactions from the Ethereum mainnet.

Voluntary Geographic Information initiatives are transforming the disaster response landscape. Our research provides insights into how the concept of collective intelligence is accomplished in humanitarian mapping initiatives. The main source originates from the data obtained in 746 mapping projects organised by the Humanitarian OpenStreetMap Team between December 2021 and November 2023, where 38,893 contributors completed 312,289 mapping tasks. These data include detailed attributes of the contributors and the states the tasks go through. The methodology adopts a quantitative approach, including descriptive and inferential statistics, and standard process mining techniques. Our results indicate that, in general terms, in humanitarian mapping, a group of contributors from outside the area of interest perform straightforward mapping tasks with limited collaboration among them. The ‘wisdom’ of advanced contributors is the cornerstone that sustains the system. The discussion section elaborates on (1) how these findings suggest that humanitarian mapping projects effectively meet their short-term mapping objectives but fall short if more sustainable mapping objectives are sought and (2) possible strategies for better harnessing the collective intelligence of these efforts.

Abstract The capability to integrate Internet of Things (IoT) technologies into business processes (BPs) has emerged as a transformative paradigm, offering unprecedented opportunities for organisations to enhance their operational efficiency and productivity. Interacting with the physical world and leveraging real-world data to make more informed business decisions is of greatest interest, and the idea of IoT-enhanced BPs promises to automate and improve business activities and permit them to adapt to the physical environment of execution. Nonetheless, combining these two domains is challenging, and it requires new modelling methods that do not increase notation complexity and provide independent execution between the process and the underlying device technology. In this work, we propose FloBP, a model-driven engineering approach separating concerns between the IoT and BPs, providing a structured and systematic approach to modelling and executing IoT-enhanced BPs. Applying the separation of concerns through an interdisciplinary team is needed to ensure that the approach covers all necessary process aspects, including technological and modelling ones. The FloBP approach is based on modelling tools and a microservices architecture to deploy BPMN models, and it facilitates integration with the physical world, providing flexibility to support multiple IoT device technologies and their evolution. A smart canteen scenario describes and evaluates the approach’s feasibility and its possible adoption by various stakeholders. The performed evaluation concludes that the application of FloBP facilitates the modelling and development of IoT-enhanced BPs by sharing and reusing knowledge among IoT and BP experts.

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.

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.

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).